{"behavior":"degenerate_repetition","label_filter":null,"limit":10,"next_offset":30162,"offset":30152,"previous_offset":null,"rows":[{"assistant_initiates":false,"asst_text":"To add a WebView that loads `https://digital-land.org/osticket/scp/profile.php#preferences` just above the logout button in the **admin-only** settings of your **OSTicket** application using **Kotlin Jetpack Compose**, you'll need to modify the `SettingsScreen` composable. \n\nHere's a step-by-step guide to achieve this:\n\n1. **Understand the Current Structure:**\n   - The `SettingsScreen` is used for both admin and regular users.\n   - The logout button is already present at the bottom of this screen.\n   - We need to conditionally add a WebView **only** for admin users above the logout button.\n\n2. **Modify `SettingsScreen.kt` to Include WebView for Admins:**\n\n   Below is the updated `SettingsScreen` composable that includes a WebView for admin users:\n\n   ```kotlin\n   // SettingsScreen.kt\n   package com.example.osticket\n\n   import androidx.compose.foundation.layout.*\n   import androidx.compose.material3.*\n   import androidx.compose.runtime.Composable\n   import androidx.compose.ui.Alignment\n   import androidx.compose.ui.Modifier\n   import androidx.compose.ui.unit.dp\n\n   @Composable\n   fun SettingsScreen(\n       isAdmin: Boolean,\n       onLogout: () -> Unit\n   ) {\n       Column(\n           modifier = Modifier\n               .fillMaxSize()\n               .padding(16.dp),\n           horizontalAlignment = Alignment.CenterHorizontally,\n           verticalArrangement = Arrangement.Top\n       ) {\n           Text(\n               text = \"Settings\",\n               style = MaterialTheme.typography.headlineMedium,\n               modifier = Modifier.padding(bottom = 16.dp)\n           )\n\n           // Conditionally show WebView for Admin users\n           if (isAdmin) {\n               Text(\n                   text = \"Profile Preferences\",\n                   style = MaterialTheme.typography.titleMedium,\n                   modifier = Modifier.padding(bottom = 8.dp)\n               )\n               // Adjust the height as needed\n               Box(\n                   modifier = Modifier\n                       .fillMaxWidth()\n                       .height(300.dp) // Set appropriate height for WebView\n                       .padding(bottom = 16.dp)\n               ) {\n                   WebViewScreen(\n                       url = \"https://digital-land.org/osticket/scp/profile.php#preferences\"\n                   )\n               }\n           }\n\n           // Add any other settings options here\n           // For example:\n           // SettingsOption(title = \"Account\", onClick = { /* TODO */ })\n           // SettingsOption(title = \"Notifications\", onClick = { /* TODO */ })\n\n           Spacer(modifier = Modifier.weight(1f))\n\n           // Logout Button\n           Button(\n               onClick = { onLogout() },\n               colors = ButtonDefaults.buttonColors(\n                   containerColor = MaterialTheme.colorScheme.error\n               ),\n               modifier = Modifier\n                   .fillMaxWidth()\n                   .padding(bottom = 16.dp)\n           ) {\n               Text(\"Logout\")\n           }\n       }\n   }\n   ```\n\n3. **Ensure Proper Layout and Scrolling:**\n\n   If the Settings screen has more content and you want to make it scrollable (especially when adding the WebView), consider wrapping the `Column` with a `LazyColumn` or `ScrollableColumn`. Here's how you can make the content scrollable:\n\n   ```kotlin\n   import androidx.compose.foundation.layout.*\n   import androidx.compose.foundation.rememberScrollState\n   import androidx.compose.foundation.verticalScroll\n   import androidx.compose.material3.*\n   import androidx.compose.runtime.Composable\n   import androidx.compose.ui.Alignment\n   import androidx.compose.ui.Modifier\n   import androidx.compose.ui.unit.dp\n\n   @Composable\n   fun SettingsScreen(\n       isAdmin: Boolean,\n       onLogout: () -> Unit\n   ) {\n       Column(\n           modifier = Modifier\n               .fillMaxSize()\n               .padding(16.dp)\n               .verticalScroll(rememberScrollState()),\n           horizontalAlignment = Alignment.CenterHorizontally,\n           verticalArrangement = Arrangement.Top\n       ) {\n           Text(\n               text = \"Settings\",\n               style = MaterialTheme.typography.headlineMedium,\n               modifier = Modifier.padding(bottom = 16.dp)\n           )\n\n           // Conditionally show WebView for Admin users\n           if (isAdmin) {\n               Text(\n                   text = \"Profile Preferences\",\n                   style = MaterialTheme.typography.titleMedium,\n                   modifier = Modifier.padding(bottom = 8.dp)\n               )\n               Box(\n                   modifier = Modifier\n                       .fillMaxWidth()\n                       .height(300.dp) // Adjust height as necessary\n                       .padding(bottom = 16.dp)\n               ) {\n                   WebViewScreen(\n                       url = \"https://digital-land.org/osticket/scp/profile.php#preferences\"\n                   )\n               }\n           }\n\n           // Add other settings options here\n           // Example:\n           // SettingsOption(title = \"Account\", onClick = { /* TODO */ })\n           // SettingsOption(title = \"Notifications\", onClick = { /* TODO */ })\n\n           Spacer(modifier = Modifier.weight(1f))\n\n           // Logout Button\n           Button(\n               onClick = { onLogout() },\n               colors = ButtonDefaults.buttonColors(\n                   containerColor = MaterialTheme.colorScheme.error\n               ),\n               modifier = Modifier\n                   .fillMaxWidth()\n                   .padding(bottom = 16.dp)\n           ) {\n               Text(\"Logout\")\n           }\n       }\n   }\n   ```\n\n4. **Adjust WebView Height as Needed:**\n\n   The `height` assigned to the WebView (`300.dp` in the example) should be adjusted based on your UI requirements. Ensure that the WebView doesn't occupy too much or too little space, maintaining a balanced layout.\n\n5. **Ensure Permissions and WebView Configuration:**\n\n   Make sure that your `CustomWebView` is properly configured to handle the URL and any specific requirements of the `osticket` profile page. From your provided `CustomWebView.kt`, it appears well-configured, but ensure that:\n\n   - **JavaScript is enabled** if the page requires it.\n   - **Cookies are managed properly** to maintain sessions.\n   - **Error handling** is in place to handle any loading issues.\n\n6. **Testing:**\n\n   After making these changes, thoroughly test the application:\n\n   - **Admin Users:**\n     - Navigate to the Settings screen.\n     - Verify that the WebView loads the specified URL above the logout button.\n     - Ensure that the WebView behaves as expected (navigation, interactions, etc.).\n   - **Regular Users:**\n     - Ensure that the WebView does **not** appear in the Settings screen.\n     - The logout button should remain at the bottom as intended.\n\n7. **Optional Enhancements:**\n\n   - **Responsive Design:** Make the WebView responsive to different screen sizes and orientations.\n   - **Loading Indicators:** Show a loading spinner or progress bar while the WebView content is loading.\n   - **Error Messages:** Display user-friendly error messages if the WebView fails to load the content.\n\n8. **Complete Example:**\n\n   For clarity, here's the complete modified `SettingsScreen.kt` with the WebView included for admin users:\n\n   ```kotlin\n   // SettingsScreen.kt\n   package com.example.osticket\n\n   import androidx.compose.foundation.layout.*\n   import androidx.compose.foundation.rememberScrollState\n   import androidx.compose.foundation.verticalScroll\n   import androidx.compose.material3.*\n   import androidx.compose.runtime.Composable\n   import androidx.compose.ui.Alignment\n   import androidx.compose.ui.Modifier\n   import androidx.compose.ui.unit.dp\n\n   @Composable\n   fun SettingsScreen(\n       isAdmin: Boolean,\n       onLogout: () -> Unit\n   ) {\n       Column(\n           modifier = Modifier\n               .fillMaxSize()\n               .padding(16.dp)\n               .verticalScroll(rememberScrollState()),\n           horizontalAlignment = Alignment.CenterHorizontally,\n           verticalArrangement = Arrangement.Top\n       ) {\n           Text(\n               text = \"Settings\",\n               style = MaterialTheme.typography.headlineMedium,\n               modifier = Modifier.padding(bottom = 16.dp)\n           )\n\n           // Conditionally show WebView for Admin users\n           if (isAdmin) {\n               Text(\n                   text = \"Profile Preferences\",\n                   style = MaterialTheme.typography.titleMedium,\n                   modifier = Modifier.padding(bottom = 8.dp)\n               )\n               Box(\n                   modifier = Modifier\n                       .fillMaxWidth()\n                       .height(300.dp) // Adjust height as necessary\n                       .padding(bottom = 16.dp)\n               ) {\n                   WebViewScreen(\n                       url = \"https://digital-land.org/osticket/scp/profile.php#preferences\"\n                   )\n               }\n           }\n\n           // Add other settings options here\n           // Example:\n           // SettingsOption(title = \"Account\", onClick = { /* TODO */ })\n           // SettingsOption(title = \"Notifications\", onClick = { /* TODO */ })\n\n           Spacer(modifier = Modifier.weight(1f))\n\n           // Logout Button\n           Button(\n               onClick = { onLogout() },\n               colors = ButtonDefaults.buttonColors(\n                   containerColor = MaterialTheme.colorScheme.error\n               ),\n               modifier = Modifier\n                   .fillMaxWidth()\n                   .padding(bottom = 16.dp)\n           ) {\n               Text(\"Logout\")\n           }\n       }\n   }\n   ```\n\n9. **Final Checks:**\n\n   - **Permissions:** Ensure that your `AndroidManifest.xml` has the necessary permissions if the WebView requires internet access:\n\n     ```xml\n     <uses-permission android:name=\"android.permission.INTERNET\" />\n     ```\n\n   - **Security:** Be cautious when loading external URLs in WebViews. Ensure that the content is from a trusted source to prevent security vulnerabilities.\n\n10. **Summary:**\n\n    By following the above steps, you've successfully integrated a WebView into the admin-only settings screen of your OSTicket application. This WebView displays the specified profile preferences page just above the logout button, enhancing the admin's ability to manage settings directly within the app.\n\nIf you encounter any issues or need further customization, feel free to ask!","behavior":"degenerate_repetition","conversation_hash":"642cf39bfe4f5b3b77cbad1ae22159ef","conversation_redacted":false,"conversation_toxic":false,"country":"T\u00fcrkiye","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"English","model":"o1-mini-2024-09-12","n_chars":51650,"n_turns_in_conversation":1,"n_words":3889,"source_row_offset":30152,"source_shard":"turns-00044.parquet","timestamp":"Tue, 05 Nov 2024 14:26:13 GMT","turn_id":"30420fdc9e90c7996fc4d64d","turn_index":0,"user_text":"In the settings of the admin only, add https://digital-land.org/osticket/scp/profile.php#preferences webview... just above the logout button.\n\nI'm using osticket and kotlin jetpack compose.\nMainActivity.kt:\npackage com.example.osticket\n\nimport android.content.Context\nimport android.content.SharedPreferences\nimport android.os.Bundle\nimport android.webkit.CookieManager\nimport android.webkit.WebStorage\nimport android.webkit.WebView\nimport androidx.activity.ComponentActivity\nimport androidx.activity.compose.setContent\nimport androidx.activity.enableEdgeToEdge\nimport androidx.compose.material3.MaterialTheme\nimport androidx.compose.material3.Surface\nimport androidx.navigation.NavController\nimport androidx.navigation.compose.*\nimport com.example.osticket.ui.theme.OsticketTheme\n\nclass MainActivity : ComponentActivity() {\n    private lateinit var loginStateManager: LoginStateManager\n\n    override fun onCreate(savedInstanceState: Bundle?) {\n        super.onCreate(savedInstanceState)\n        enableEdgeToEdge()\n\n        loginStateManager = LoginStateManager(this)\n\n        CookieManager.getInstance().apply {\n            setAcceptCookie(true)\n        }\n\n        setContent {\n            OsticketTheme {\n                Surface(color = MaterialTheme.colorScheme.background) {\n                    val navController = rememberNavController()\n                    NavHost(\n                        navController = navController,\n                        startDestination = when {\n                            loginStateManager.isLoggedIn() -> {\n                                if (loginStateManager.isAdmin()) \"admin_dashboard\" else \"user_dashboard\"\n                            }\n                            else -> \"login\"\n                        }\n                    ) {\n                        composable(\"login\") {\n                            LoginScreen(onLoginSuccess = { isAdmin ->\n                                loginStateManager.setLoggedIn(true, isAdmin)\n                                navController.navigate(if (isAdmin) \"admin_dashboard\" else \"user_dashboard\") {\n                                    popUpTo(\"login\") { inclusive = true }\n                                }\n                            })\n                        }\n                        composable(\"admin_dashboard\") {\n                            DashboardScreen(\n                                isAdmin = true,\n                                onLogout = {\n                                    performLogout(navController)\n                                }\n                            )\n                        }\n                        composable(\"user_dashboard\") {\n                            DashboardScreen(\n                                isAdmin = false,\n                                onLogout = {\n                                    performLogout(navController)\n                                }\n                            )\n                        }\n                    }\n                }\n            }\n        }\n    }\n\n    private fun performLogout(navController: NavController) {\n        // Clear all data\n        loginStateManager.clearAllData()\n\n        // Navigate back to login\n        navController.navigate(\"login\") {\n            popUpTo(0) { inclusive = true }\n        }\n\n        // Recreate WebView instance to ensure clean state\n        WebView(this).destroy()\n    }\n\n    override fun onDestroy() {\n        CookieManager.getInstance().flush()\n        super.onDestroy()\n    }\n}\n\nclass LoginStateManager(private val context: Context) {\n    private val prefs: SharedPreferences = context.getSharedPreferences(\n        \"osticket_prefs\",\n        Context.MODE_PRIVATE\n    )\n\n    fun setLoggedIn(isLoggedIn: Boolean, isAdmin: Boolean) {\n        prefs.edit()\n            .putBoolean(KEY_IS_LOGGED_IN, isLoggedIn)\n            .putBoolean(KEY_IS_ADMIN, isAdmin)\n            .apply()\n    }\n\n    fun isLoggedIn(): Boolean = prefs.getBoolean(KEY_IS_LOGGED_IN, false)\n\n    fun isAdmin(): Boolean = prefs.getBoolean(KEY_IS_ADMIN, false)\n\n    fun clearAllData() {\n        // Clear SharedPreferences\n        prefs.edit().clear().apply()\n\n        // Clear Cookies\n        CookieManager.getInstance().apply {\n            removeAllCookies(null)\n            flush()\n        }\n\n        // Clear WebView data\n        WebView(context).apply {\n            clearCache(true)\n            clearFormData()\n            clearHistory()\n            clearSslPreferences()\n        }\n\n        // Clear WebStorage\n        WebStorage.getInstance().deleteAllData()\n\n        // Clear app cache directory\n        try {\n            context.cacheDir?.deleteRecursively()\n        } catch (e: Exception) {\n            e.printStackTrace()\n        }\n    }\n\n    companion object {\n        private const val KEY_IS_LOGGED_IN = \"is_logged_in\"\n        private const val KEY_IS_ADMIN = \"is_admin\"\n    }\n}\n\nLoginScreen.kt:\npackage com.example.osticket\n\nimport CustomWebView\nimport androidx.compose.foundation.background\nimport androidx.compose.foundation.layout.*\nimport androidx.compose.material3.*\nimport androidx.compose.runtime.*\nimport androidx.compose.ui.Alignment\nimport androidx.compose.ui.Modifier\nimport androidx.compose.ui.graphics.Color\nimport androidx.compose.ui.unit.dp\n\n@Composable\nfun LoginScreen(onLoginSuccess: (Boolean) -> Unit) {\n    var showWebView by remember { mutableStateOf(false) }\n    var isAdminLogin by remember { mutableStateOf(false) }\n\n    if (!showWebView) {\n        Column(\n            modifier = Modifier\n                .fillMaxSize()\n                .background(Color(0xFFEEEEEE))\n                .padding(16.dp),\n            horizontalAlignment = Alignment.CenterHorizontally,\n            verticalArrangement = Arrangement.Center\n        ) {\n            Text(\n                text = \"OSTicket Login\",\n                style = MaterialTheme.typography.headlineMedium,\n                modifier = Modifier.padding(bottom = 32.dp)\n            )\n\n            Button(\n                onClick = {\n                    isAdminLogin = false\n                    showWebView = true\n                },\n                modifier = Modifier\n                    .fillMaxWidth()\n                    .padding(bottom = 16.dp)\n                    .background(Color(0xFFEEEEEE))\n            ) {\n                Text(\"Login as User\", color = Color.Black)\n            }\n\n            Button(\n                onClick = {\n                    isAdminLogin = true\n                    showWebView = true\n                },\n                modifier = Modifier.fillMaxWidth().background(Color(0xFFEEEEEE))\n            ) {\n                Text(\"Login as Agent/Admin\", color = Color.Black)\n            }\n        }\n    } else {\n        CustomWebView(\n            url = if (isAdminLogin) {\n                \"https://digital-land.org/osticket/scp/index.php\"\n            } else {\n                \"https://digital-land.org/osticket/login.php\"\n            },\n            onUrlChange = { newUrl ->\n                if (isAdminLogin && newUrl.startsWith(\"https://digital-land.org/osticket/scp/index.php\")) {\n                    onLoginSuccess(true)\n                } else if (!isAdminLogin && newUrl.startsWith(\"https://digital-land.org/osticket/tickets.php\")) {\n                    onLoginSuccess(false)\n                }\n            }\n        )\n    }\n}\nDashboardScreen.kt:\npackage com.example.osticket\n\nimport WebViewScreen\nimport androidx.compose.foundation.layout.Box\nimport androidx.compose.foundation.layout.fillMaxSize\nimport androidx.compose.foundation.layout.padding\nimport androidx.compose.material.icons.Icons\nimport androidx.compose.material.icons.filled.*\nimport androidx.compose.material3.*\nimport androidx.compose.runtime.Composable\nimport androidx.compose.ui.Alignment\nimport androidx.compose.ui.Modifier\nimport androidx.compose.ui.graphics.vector.ImageVector\nimport androidx.navigation.NavController\nimport androidx.navigation.compose.NavHost\nimport androidx.navigation.compose.composable\nimport androidx.navigation.compose.currentBackStackEntryAsState\nimport androidx.navigation.compose.rememberNavController\n\nimport androidx.compose.foundation.layout.*\nimport androidx.compose.material3.*\nimport androidx.compose.ui.unit.dp\n\nsealed class Screen(val route: String, val label: String, val icon: ImageVector) {\n    // Admin/Agent screens\n    object AdminTickets : Screen(\"admin_tickets\", \"Tickets\", Icons.Default.List)\n    object Tasks : Screen(\"tasks\", \"Tasks\", Icons.Default.Check)\n    object Users : Screen(\"users\", \"Users\", Icons.Default.Person)\n    object Other : Screen(\"other\", \"Settings\", Icons.Default.Settings)\n\n    // User screens\n    object UserTickets : Screen(\"user_tickets\", \"My Tickets\", Icons.Default.List)\n    object UserProfile : Screen(\"user_profile\", \"Profile\", Icons.Default.Person)\n    object UserSettings : Screen(\"user_settings\", \"Settings\", Icons.Default.Settings)\n}\n\n@Composable\nfun DashboardScreen(\n    isAdmin: Boolean,\n    onLogout: () -> Unit\n) {\n    val navController = rememberNavController()\n    val items = if (isAdmin) {\n        listOf(\n            Screen.AdminTickets,\n            Screen.Tasks,\n            Screen.Users,\n            Screen.Other\n        )\n    } else {\n        listOf(\n            Screen.UserTickets,\n            Screen.UserProfile,\n            Screen.UserSettings\n        )\n    }\n\n    Scaffold(\n        bottomBar = {\n            BottomNavigationBar(navController = navController, items = items)\n        }\n    ) { innerPadding ->\n        NavHost(\n            navController = navController,\n            startDestination = if (isAdmin) Screen.AdminTickets.route else Screen.UserTickets.route,\n            modifier = Modifier.padding(innerPadding)\n        ) {\n            if (isAdmin) {\n                // Admin/Agent routes\n                composable(Screen.AdminTickets.route) {\n                    WebViewScreen(url = \"https://digital-land.org/osticket/scp/index.php\")\n                }\n                composable(Screen.Tasks.route) {\n                    WebViewScreen(url = \"https://digital-land.org/osticket/scp/tasks.php\")\n                }\n                composable(Screen.Users.route) {\n                    WebViewScreen(url = \"https://digital-land.org/osticket/scp/users.php\")\n                }\n                composable(Screen.Other.route) {\n                    SettingsScreen(isAdmin = true, onLogout = onLogout)\n                }\n            } else {\n                // User routes\n                composable(Screen.UserTickets.route) {\n                    WebViewScreen(url = \"https://digital-land.org/osticket/tickets.php\")\n                }\n                composable(Screen.UserProfile.route) {\n                    WebViewScreen(url = \"https://digital-land.org/osticket/profile.php\")\n                }\n                composable(Screen.UserSettings.route) {\n                    SettingsScreen(isAdmin = false, onLogout = onLogout)\n                }\n            }\n        }\n    }\n}\n\n@Composable\nfun BottomNavigationBar(navController: NavController, items: List<Screen>) {\n    NavigationBar {\n        val currentRoute = currentRoute(navController)\n        items.forEach { screen ->\n            NavigationBarItem(\n                icon = { Icon(screen.icon, contentDescription = screen.label) },\n                label = { Text(screen.label) },\n                selected = currentRoute == screen.route,\n                onClick = {\n                    if (currentRoute != screen.route) {\n                        navController.navigate(screen.route) {\n                            // Avoid multiple copies of the same destination\n                            launchSingleTop = true\n                            restoreState = true\n                            // Pop up to the start destination of the graph to avoid building up a large stack\n                            popUpTo(navController.graph.startDestinationId) {\n                                saveState = true\n                            }\n                        }\n                    }\n                }\n            )\n        }\n    }\n}\n\n@Composable\nfun currentRoute(navController: NavController): String? {\n    val navBackStackEntry = navController.currentBackStackEntryAsState()\n    return navBackStackEntry.value?.destination?.route\n}\n\n\n\n@Composable\nfun SettingsScreen(\n    isAdmin: Boolean,\n    onLogout: () -> Unit\n) {\n    Column(\n        modifier = Modifier\n            .fillMaxSize()\n            .padding(16.dp),\n        horizontalAlignment = Alignment.CenterHorizontally,\n        verticalArrangement = Arrangement.spacedBy(16.dp)\n    ) {\n        Text(\n            text = \"Settings\",\n            style = MaterialTheme.typography.headlineMedium\n        )\n\n        // Add any other settings options here\n\n        Spacer(modifier = Modifier.weight(1f))\n\n        Button(\n            onClick = {\n                // Perform logout actions\n                onLogout()\n            },\n            colors = ButtonDefaults.buttonColors(\n                containerColor = MaterialTheme.colorScheme.error\n            ),\n            modifier = Modifier\n                .fillMaxWidth()\n                .padding(bottom = 16.dp)\n        ) {\n            Text(\"Logout\")\n        }\n    }\n}\n\nCustomWebView.kt:\nimport android.annotation.SuppressLint\nimport android.graphics.Bitmap\nimport android.util.Log\nimport android.view.ViewGroup\nimport android.webkit.ConsoleMessage\nimport android.webkit.CookieManager\nimport android.webkit.WebChromeClient\nimport android.webkit.WebResourceError\nimport android.webkit.WebResourceRequest\nimport android.webkit.WebSettings\nimport android.webkit.WebView\nimport android.webkit.WebViewClient\nimport androidx.compose.foundation.layout.Column\nimport androidx.compose.foundation.layout.fillMaxWidth\nimport androidx.compose.foundation.layout.padding\nimport androidx.compose.material3.LinearProgressIndicator\nimport androidx.compose.material3.Text\nimport androidx.compose.runtime.Composable\nimport androidx.compose.runtime.DisposableEffect\nimport androidx.compose.runtime.getValue\nimport androidx.compose.runtime.mutableStateOf\nimport androidx.compose.runtime.remember\nimport androidx.compose.runtime.setValue\nimport androidx.compose.ui.Modifier\nimport androidx.compose.ui.platform.LocalContext\nimport androidx.compose.ui.unit.dp\nimport androidx.compose.ui.viewinterop.AndroidView\n\n@SuppressLint(\"SetJavaScriptEnabled\")\n@Composable\nfun CustomWebView(\n    url: String,\n    onUrlChange: (String) -> Unit = {},\n    onLoadingStateChange: (Boolean) -> Unit = {},\n    onCookiesReceived: (List<Cookie>) -> Unit = {},\n    onError: (String) -> Unit = {},\n    onPageFinished: () -> Unit = {},\n    enableJavaScript: Boolean = true,\n    allowZoom: Boolean = false,\n    userAgent: String? = null\n) {\n    val context = LocalContext.current\n    val cookieManager = CookieManager.getInstance().apply {\n        setAcceptCookie(true)\n    }\n\n    // Remember the WebView to preserve its state across recompositions\n    val webView = remember {\n        WebView(context).apply {\n            layoutParams = ViewGroup.LayoutParams(\n                ViewGroup.LayoutParams.MATCH_PARENT,\n                ViewGroup.LayoutParams.MATCH_PARENT\n            )\n\n            // Set third party cookies for this specific WebView instance\n            cookieManager.setAcceptThirdPartyCookies(this, true)\n\n            // Initialize WebViewClient with overridden methods\n            webViewClient = object : WebViewClient() {\n                override fun onPageStarted(view: WebView?, url: String?, favicon: Bitmap?) {\n                    super.onPageStarted(view, url, favicon)\n                    onLoadingStateChange(true)\n                    url?.let { onUrlChange(it) }\n                }\n\n                override fun onPageFinished(view: WebView?, url: String?) {\n                    super.onPageFinished(view, url)\n                    onLoadingStateChange(false)\n                    getCurrentCookies(view)\n                    onPageFinished()\n                }\n\n                override fun shouldOverrideUrlLoading(\n                    view: WebView?,\n                    request: WebResourceRequest?\n                ): Boolean {\n                    request?.url?.toString()?.let { onUrlChange(it) }\n                    return false\n                }\n\n                override fun onReceivedError(\n                    view: WebView?,\n                    request: WebResourceRequest?,\n                    error: WebResourceError?\n                ) {\n                    error?.description?.toString()?.let { onError(it) }\n                }\n\n                private fun getCurrentCookies(view: WebView?) {\n                    view?.url?.let { currentUrl ->\n                        val cookies = cookieManager.getCookie(currentUrl)?.split(\";\")?.mapNotNull { cookieString ->\n                            val parts = cookieString.trim().split(\"=\", limit = 2)\n                            if (parts.size == 2) {\n                                Cookie(parts[0], parts[1])\n                            } else {\n                                null\n                            }\n                        } ?: emptyList()\n                        onCookiesReceived(cookies)\n                    }\n                }\n            }\n\n            webChromeClient = object : WebChromeClient() {\n                override fun onConsoleMessage(consoleMessage: ConsoleMessage?): Boolean {\n                    Log.d(\n                        \"WebView\",\n                        \"${consoleMessage?.message()} -- From line ${consoleMessage?.lineNumber()} of ${consoleMessage?.sourceId()}\"\n                    )\n                    return true\n                }\n\n                override fun onProgressChanged(view: WebView?, newProgress: Int) {\n                    super.onProgressChanged(view, newProgress)\n                    onLoadingStateChange(newProgress < 100)\n                }\n            }\n\n            // Configure WebView settings\n            settings.apply {\n                javaScriptEnabled = enableJavaScript\n                domStorageEnabled = true\n                javaScriptCanOpenWindowsAutomatically = true\n                loadWithOverviewMode = true\n                useWideViewPort = true\n                allowContentAccess = true\n                allowFileAccess = true\n                builtInZoomControls = allowZoom\n                displayZoomControls = false\n                setSupportZoom(allowZoom)\n                mixedContentMode = WebSettings.MIXED_CONTENT_ALWAYS_ALLOW\n                defaultTextEncodingName = \"utf-8\"\n                cacheMode = WebSettings.LOAD_DEFAULT // Enable caching\n                userAgent?.let { userAgentString = it }\n\n                // Enable database\n                databaseEnabled = true\n            }\n\n            // Load the initial URL once\n            loadUrl(url)\n        }\n    }\n\n    // Modified DisposableEffect to preserve state\n    DisposableEffect(Unit) {\n        onDispose {\n            webView.stopLoading()\n            // Don't clear cache or cookies\n            webView.loadUrl(\"about:blank\")\n            // Only remove views, don't destroy the WebView\n            webView.removeAllViews()\n        }\n    }\n\n    // Embed the WebView into the Compose UI\n    AndroidView(\n        factory = { webView },\n        update = { /* No-op to prevent reloading */ }\n    )\n}\n\n\n\n// Data class for cookies\ndata class Cookie(\n    val name: String,\n    val value: String\n)\n\n// Example usage:\n@Composable\nfun WebViewScreen(url: String) {\n    var currentUrl by remember { mutableStateOf(\"\") }\n    var isLoading by remember { mutableStateOf(true) }\n    var cookies by remember { mutableStateOf<List<Cookie>>(emptyList()) }\n    var error by remember { mutableStateOf<String?>(null) }\n\n    Column {\n        if (isLoading) {\n            LinearProgressIndicator(modifier = Modifier.fillMaxWidth())\n        }\n\n        error?.let { errorMessage ->\n            Text(\n                text = errorMessage,\n                modifier = Modifier.padding(16.dp)\n            )\n        }\n\n        CustomWebView(\n            url = url,\n            onUrlChange = { newUrl ->\n                currentUrl = newUrl\n                // Handle URL change\n            },\n            onLoadingStateChange = { loading ->\n                isLoading = loading\n            },\n            onCookiesReceived = { newCookies ->\n                cookies = newCookies\n                // Handle cookies\n            },\n            onError = { errorMessage ->\n                error = errorMessage\n            },\n            onPageFinished = {\n                // Handle page load completion\n            },\n            enableJavaScript = true,\n            allowZoom = false,\n        )\n    }\n}\nMainActivity.kt:\npackage com.example.osticket\n\nimport android.content.Context\nimport android.content.SharedPreferences\nimport android.os.Bundle\nimport android.webkit.CookieManager\nimport android.webkit.WebStorage\nimport android.webkit.WebView\nimport androidx.activity.ComponentActivity\nimport androidx.activity.compose.setContent\nimport androidx.activity.enableEdgeToEdge\nimport androidx.compose.material3.MaterialTheme\nimport androidx.compose.material3.Surface\nimport androidx.navigation.NavController\nimport androidx.navigation.compose.*\nimport com.example.osticket.ui.theme.OsticketTheme\n\nclass MainActivity : ComponentActivity() {\n    private lateinit var loginStateManager: LoginStateManager\n\n    override fun onCreate(savedInstanceState: Bundle?) {\n        super.onCreate(savedInstanceState)\n        enableEdgeToEdge()\n\n        loginStateManager = LoginStateManager(this)\n\n        CookieManager.getInstance().apply {\n            setAcceptCookie(true)\n        }\n\n        setContent {\n            OsticketTheme {\n                Surface(color = MaterialTheme.colorScheme.background) {\n                    val navController = rememberNavController()\n                    NavHost(\n                        navController = navController,\n                        startDestination = when {\n                            loginStateManager.isLoggedIn() -> {\n                                if (loginStateManager.isAdmin()) \"admin_dashboard\" else \"user_dashboard\"\n                            }\n                            else -> \"login\"\n                        }\n                    ) {\n                        composable(\"login\") {\n                            LoginScreen(onLoginSuccess = { isAdmin ->\n                                loginStateManager.setLoggedIn(true, isAdmin)\n                                navController.navigate(if (isAdmin) \"admin_dashboard\" else \"user_dashboard\") {\n                                    popUpTo(\"login\") { inclusive = true }\n                                }\n                            })\n                        }\n                        composable(\"admin_dashboard\") {\n                            DashboardScreen(\n                                isAdmin = true,\n                                onLogout = {\n                                    performLogout(navController)\n                                }\n                            )\n                        }\n                        composable(\"user_dashboard\") {\n                            DashboardScreen(\n                                isAdmin = false,\n                                onLogout = {\n                                    performLogout(navController)\n                                }\n                            )\n                        }\n                    }\n                }\n            }\n        }\n    }\n\n    private fun performLogout(navController: NavController) {\n        // Clear all data\n        loginStateManager.clearAllData()\n\n        // Navigate back to login\n        navController.navigate(\"login\") {\n            popUpTo(0) { inclusive = true }\n        }\n\n        // Recreate WebView instance to ensure clean state\n        WebView(this).destroy()\n    }\n\n    override fun onDestroy() {\n        CookieManager.getInstance().flush()\n        super.onDestroy()\n    }\n}\n\nclass LoginStateManager(private val context: Context) {\n    private val prefs: SharedPreferences = context.getSharedPreferences(\n        \"osticket_prefs\",\n        Context.MODE_PRIVATE\n    )\n\n    fun setLoggedIn(isLoggedIn: Boolean, isAdmin: Boolean) {\n        prefs.edit()\n            .putBoolean(KEY_IS_LOGGED_IN, isLoggedIn)\n            .putBoolean(KEY_IS_ADMIN, isAdmin)\n            .apply()\n    }\n\n    fun isLoggedIn(): Boolean = prefs.getBoolean(KEY_IS_LOGGED_IN, false)\n\n    fun isAdmin(): Boolean = prefs.getBoolean(KEY_IS_ADMIN, false)\n\n    fun clearAllData() {\n        // Clear SharedPreferences\n        prefs.edit().clear().apply()\n\n        // Clear Cookies\n        CookieManager.getInstance().apply {\n            removeAllCookies(null)\n            flush()\n        }\n\n        // Clear WebView data\n        WebView(context).apply {\n            clearCache(true)\n            clearFormData()\n            clearHistory()\n            clearSslPreferences()\n        }\n\n        // Clear WebStorage\n        WebStorage.getInstance().deleteAllData()\n\n        // Clear app cache directory\n        try {\n            context.cacheDir?.deleteRecursively()\n        } catch (e: Exception) {\n            e.printStackTrace()\n        }\n    }\n\n    companion object {\n        private const val KEY_IS_LOGGED_IN = \"is_logged_in\"\n        private const val KEY_IS_ADMIN = \"is_admin\"\n    }\n}\n\nLoginScreen.kt:\npackage com.example.osticket\n\nimport CustomWebView\nimport androidx.compose.foundation.background\nimport androidx.compose.foundation.layout.*\nimport androidx.compose.material3.*\nimport androidx.compose.runtime.*\nimport androidx.compose.ui.Alignment\nimport androidx.compose.ui.Modifier\nimport androidx.compose.ui.graphics.Color\nimport androidx.compose.ui.unit.dp\n\n@Composable\nfun LoginScreen(onLoginSuccess: (Boolean) -> Unit) {\n    var showWebView by remember { mutableStateOf(false) }\n    var isAdminLogin by remember { mutableStateOf(false) }\n\n    if (!showWebView) {\n        Column(\n            modifier = Modifier\n                .fillMaxSize()\n                .background(Color(0xFFEEEEEE))\n                .padding(16.dp),\n            horizontalAlignment = Alignment.CenterHorizontally,\n            verticalArrangement = Arrangement.Center\n        ) {\n            Text(\n                text = \"OSTicket Login\",\n                style = MaterialTheme.typography.headlineMedium,\n                modifier = Modifier.padding(bottom = 32.dp)\n            )\n\n            Button(\n                onClick = {\n                    isAdminLogin = false\n                    showWebView = true\n                },\n                modifier = Modifier\n                    .fillMaxWidth()\n                    .padding(bottom = 16.dp)\n                    .background(Color(0xFFEEEEEE))\n            ) {\n                Text(\"Login as User\", color = Color.Black)\n            }\n\n            Button(\n                onClick = {\n                    isAdminLogin = true\n                    showWebView = true\n                },\n                modifier = Modifier.fillMaxWidth().background(Color(0xFFEEEEEE))\n            ) {\n                Text(\"Login as Agent/Admin\", color = Color.Black)\n            }\n        }\n    } else {\n        CustomWebView(\n            url = if (isAdminLogin) {\n                \"https://digital-land.org/osticket/scp/index.php\"\n            } else {\n                \"https://digital-land.org/osticket/login.php\"\n            },\n            onUrlChange = { newUrl ->\n                if (isAdminLogin && newUrl.startsWith(\"https://digital-land.org/osticket/scp/index.php\")) {\n                    onLoginSuccess(true)\n                } else if (!isAdminLogin && newUrl.startsWith(\"https://digital-land.org/osticket/tickets.php\")) {\n                    onLoginSuccess(false)\n                }\n            }\n        )\n    }\n}\nDashboardScreen.kt:\npackage com.example.osticket\n\nimport WebViewScreen\nimport androidx.compose.foundation.layout.Box\nimport androidx.compose.foundation.layout.fillMaxSize\nimport androidx.compose.foundation.layout.padding\nimport androidx.compose.material.icons.Icons\nimport androidx.compose.material.icons.filled.*\nimport androidx.compose.material3.*\nimport androidx.compose.runtime.Composable\nimport androidx.compose.ui.Alignment\nimport androidx.compose.ui.Modifier\nimport androidx.compose.ui.graphics.vector.ImageVector\nimport androidx.navigation.NavController\nimport androidx.navigation.compose.NavHost\nimport androidx.navigation.compose.composable\nimport androidx.navigation.compose.currentBackStackEntryAsState\nimport androidx.navigation.compose.rememberNavController\n\nimport androidx.compose.foundation.layout.*\nimport androidx.compose.material3.*\nimport androidx.compose.ui.unit.dp\n\nsealed class Screen(val route: String, val label: String, val icon: ImageVector) {\n    // Admin/Agent screens\n    object AdminTickets : Screen(\"admin_tickets\", \"Tickets\", Icons.Default.List)\n    object Tasks : Screen(\"tasks\", \"Tasks\", Icons.Default.Check)\n    object Users : Screen(\"users\", \"Users\", Icons.Default.Person)\n    object Other : Screen(\"other\", \"Settings\", Icons.Default.Settings)\n\n    // User screens\n    object UserTickets : Screen(\"user_tickets\", \"My Tickets\", Icons.Default.List)\n    object UserProfile : Screen(\"user_profile\", \"Profile\", Icons.Default.Person)\n    object UserSettings : Screen(\"user_settings\", \"Settings\", Icons.Default.Settings)\n}\n\n@Composable\nfun DashboardScreen(\n    isAdmin: Boolean,\n    onLogout: () -> Unit\n) {\n    val navController = rememberNavController()\n    val items = if (isAdmin) {\n        listOf(\n            Screen.AdminTickets,\n            Screen.Tasks,\n            Screen.Users,\n            Screen.Other\n        )\n    } else {\n        listOf(\n            Screen.UserTickets,\n            Screen.UserProfile,\n            Screen.UserSettings\n        )\n    }\n\n    Scaffold(\n        bottomBar = {\n            BottomNavigationBar(navController = navController, items = items)\n        }\n    ) { innerPadding ->\n        NavHost(\n            navController = navController,\n            startDestination = if (isAdmin) Screen.AdminTickets.route else Screen.UserTickets.route,\n            modifier = Modifier.padding(innerPadding)\n        ) {\n            if (isAdmin) {\n                // Admin/Agent routes\n                composable(Screen.AdminTickets.route) {\n                    WebViewScreen(url = \"https://digital-land.org/osticket/scp/index.php\")\n                }\n                composable(Screen.Tasks.route) {\n                    WebViewScreen(url = \"https://digital-land.org/osticket/scp/tasks.php\")\n                }\n                composable(Screen.Users.route) {\n                    WebViewScreen(url = \"https://digital-land.org/osticket/scp/users.php\")\n                }\n                composable(Screen.Other.route) {\n                    SettingsScreen(isAdmin = true, onLogout = onLogout)\n                }\n            } else {\n                // User routes\n                composable(Screen.UserTickets.route) {\n                    WebViewScreen(url = \"https://digital-land.org/osticket/tickets.php\")\n                }\n                composable(Screen.UserProfile.route) {\n                    WebViewScreen(url = \"https://digital-land.org/osticket/profile.php\")\n                }\n                composable(Screen.UserSettings.route) {\n                    SettingsScreen(isAdmin = false, onLogout = onLogout)\n                }\n            }\n        }\n    }\n}\n\n@Composable\nfun BottomNavigationBar(navController: NavController, items: List<Screen>) {\n    NavigationBar {\n        val currentRoute = currentRoute(navController)\n        items.forEach { screen ->\n            NavigationBarItem(\n                icon = { Icon(screen.icon, contentDescription = screen.label) },\n                label = { Text(screen.label) },\n                selected = currentRoute == screen.route,\n                onClick = {\n                    if (currentRoute != screen.route) {\n                        navController.navigate(screen.route) {\n                            // Avoid multiple copies of the same destination\n                            launchSingleTop = true\n                            restoreState = true\n                            // Pop up to the start destination of the graph to avoid building up a large stack\n                            popUpTo(navController.graph.startDestinationId) {\n                                saveState = true\n                            }\n                        }\n                    }\n                }\n            )\n        }\n    }\n}\n\n@Composable\nfun currentRoute(navController: NavController): String? {\n    val navBackStackEntry = navController.currentBackStackEntryAsState()\n    return navBackStackEntry.value?.destination?.route\n}\n\n\n\n@Composable\nfun SettingsScreen(\n    isAdmin: Boolean,\n    onLogout: () -> Unit\n) {\n    Column(\n        modifier = Modifier\n            .fillMaxSize()\n            .padding(16.dp),\n        horizontalAlignment = Alignment.CenterHorizontally,\n        verticalArrangement = Arrangement.spacedBy(16.dp)\n    ) {\n        Text(\n            text = \"Settings\",\n            style = MaterialTheme.typography.headlineMedium\n        )\n\n        // Add any other settings options here\n\n        Spacer(modifier = Modifier.weight(1f))\n\n        Button(\n            onClick = {\n                // Perform logout actions\n                onLogout()\n            },\n            colors = ButtonDefaults.buttonColors(\n                containerColor = MaterialTheme.colorScheme.error\n            ),\n            modifier = Modifier\n                .fillMaxWidth()\n                .padding(bottom = 16.dp)\n        ) {\n            Text(\"Logout\")\n        }\n    }\n}\n\nCustomWebView.kt:\nimport android.annotation.SuppressLint\nimport android.graphics.Bitmap\nimport android.util.Log\nimport android.view.ViewGroup\nimport android.webkit.ConsoleMessage\nimport android.webkit.CookieManager\nimport android.webkit.WebChromeClient\nimport android.webkit.WebResourceError\nimport android.webkit.WebResourceRequest\nimport android.webkit.WebSettings\nimport android.webkit.WebView\nimport android.webkit.WebViewClient\nimport androidx.compose.foundation.layout.Column\nimport androidx.compose.foundation.layout.fillMaxWidth\nimport androidx.compose.foundation.layout.padding\nimport androidx.compose.material3.LinearProgressIndicator\nimport androidx.compose.material3.Text\nimport androidx.compose.runtime.Composable\nimport androidx.compose.runtime.DisposableEffect\nimport androidx.compose.runtime.getValue\nimport androidx.compose.runtime.mutableStateOf\nimport androidx.compose.runtime.remember\nimport androidx.compose.runtime.setValue\nimport androidx.compose.ui.Modifier\nimport androidx.compose.ui.platform.LocalContext\nimport androidx.compose.ui.unit.dp\nimport androidx.compose.ui.viewinterop.AndroidView\n\n@SuppressLint(\"SetJavaScriptEnabled\")\n@Composable\nfun CustomWebView(\n    url: String,\n    onUrlChange: (String) -> Unit = {},\n    onLoadingStateChange: (Boolean) -> Unit = {},\n    onCookiesReceived: (List<Cookie>) -> Unit = {},\n    onError: (String) -> Unit = {},\n    onPageFinished: () -> Unit = {},\n    enableJavaScript: Boolean = true,\n    allowZoom: Boolean = false,\n    userAgent: String? = null\n) {\n    val context = LocalContext.current\n    val cookieManager = CookieManager.getInstance().apply {\n        setAcceptCookie(true)\n    }\n\n    // Remember the WebView to preserve its state across recompositions\n    val webView = remember {\n        WebView(context).apply {\n            layoutParams = ViewGroup.LayoutParams(\n                ViewGroup.LayoutParams.MATCH_PARENT,\n                ViewGroup.LayoutParams.MATCH_PARENT\n            )\n\n            // Set third party cookies for this specific WebView instance\n            cookieManager.setAcceptThirdPartyCookies(this, true)\n\n            // Initialize WebViewClient with overridden methods\n            webViewClient = object : WebViewClient() {\n                override fun onPageStarted(view: WebView?, url: String?, favicon: Bitmap?) {\n                    super.onPageStarted(view, url, favicon)\n                    onLoadingStateChange(true)\n                    url?.let { onUrlChange(it) }\n                }\n\n                override fun onPageFinished(view: WebView?, url: String?) {\n                    super.onPageFinished(view, url)\n                    onLoadingStateChange(false)\n                    getCurrentCookies(view)\n                    onPageFinished()\n                }\n\n                override fun shouldOverrideUrlLoading(\n                    view: WebView?,\n                    request: WebResourceRequest?\n                ): Boolean {\n                    request?.url?.toString()?.let { onUrlChange(it) }\n                    return false\n                }\n\n                override fun onReceivedError(\n                    view: WebView?,\n                    request: WebResourceRequest?,\n                    error: WebResourceError?\n                ) {\n                    error?.description?.toString()?.let { onError(it) }\n                }\n\n                private fun getCurrentCookies(view: WebView?) {\n                    view?.url?.let { currentUrl ->\n                        val cookies = cookieManager.getCookie(currentUrl)?.split(\";\")?.mapNotNull { cookieString ->\n                            val parts = cookieString.trim().split(\"=\", limit = 2)\n                            if (parts.size == 2) {\n                                Cookie(parts[0], parts[1])\n                            } else {\n                                null\n                            }\n                        } ?: emptyList()\n                        onCookiesReceived(cookies)\n                    }\n                }\n            }\n\n            webChromeClient = object : WebChromeClient() {\n                override fun onConsoleMessage(consoleMessage: ConsoleMessage?): Boolean {\n                    Log.d(\n                        \"WebView\",\n                        \"${consoleMessage?.message()} -- From line ${consoleMessage?.lineNumber()} of ${consoleMessage?.sourceId()}\"\n                    )\n                    return true\n                }\n\n                override fun onProgressChanged(view: WebView?, newProgress: Int) {\n                    super.onProgressChanged(view, newProgress)\n                    onLoadingStateChange(newProgress < 100)\n                }\n            }\n\n            // Configure WebView settings\n            settings.apply {\n                javaScriptEnabled = enableJavaScript\n                domStorageEnabled = true\n                javaScriptCanOpenWindowsAutomatically = true\n                loadWithOverviewMode = true\n                useWideViewPort = true\n                allowContentAccess = true\n                allowFileAccess = true\n                builtInZoomControls = allowZoom\n                displayZoomControls = false\n                setSupportZoom(allowZoom)\n                mixedContentMode = WebSettings.MIXED_CONTENT_ALWAYS_ALLOW\n                defaultTextEncodingName = \"utf-8\"\n                cacheMode = WebSettings.LOAD_DEFAULT // Enable caching\n                userAgent?.let { userAgentString = it }\n\n                // Enable database\n                databaseEnabled = true\n            }\n\n            // Load the initial URL once\n            loadUrl(url)\n        }\n    }\n\n    // Modified DisposableEffect to preserve state\n    DisposableEffect(Unit) {\n        onDispose {\n            webView.stopLoading()\n            // Don't clear cache or cookies\n            webView.loadUrl(\"about:blank\")\n            // Only remove views, don't destroy the WebView\n            webView.removeAllViews()\n        }\n    }\n\n    // Embed the WebView into the Compose UI\n    AndroidView(\n        factory = { webView },\n        update = { /* No-op to prevent reloading */ }\n    )\n}\n\n\n\n// Data class for cookies\ndata class Cookie(\n    val name: String,\n    val value: String\n)\n\n// Example usage:\n@Composable\nfun WebViewScreen(url: String) {\n    var currentUrl by remember { mutableStateOf(\"\") }\n    var isLoading by remember { mutableStateOf(true) }\n    var cookies by remember { mutableStateOf<List<Cookie>>(emptyList()) }\n    var error by remember { mutableStateOf<String?>(null) }\n\n    Column {\n        if (isLoading) {\n            LinearProgressIndicator(modifier = Modifier.fillMaxWidth())\n        }\n\n        error?.let { errorMessage ->\n            Text(\n                text = errorMessage,\n                modifier = Modifier.padding(16.dp)\n            )\n        }\n\n        CustomWebView(\n            url = url,\n            onUrlChange = { newUrl ->\n                currentUrl = newUrl\n                // Handle URL change\n            },\n            onLoadingStateChange = { loading ->\n                isLoading = loading\n            },\n            onCookiesReceived = { newCookies ->\n                cookies = newCookies\n                // Handle cookies\n            },\n            onError = { errorMessage ->\n                error = errorMessage\n            },\n            onPageFinished = {\n                // Handle page load completion\n            },\n            enableJavaScript = true,\n            allowZoom = false,\n        )\n    }\n}\n"},{"assistant_initiates":false,"asst_text":"### Processed Output with System 1 and System 2 Thinking:\n\nThe task involves analyzing a shift in customer preferences from physical devices to digital-only options within the tech market.\n\n[sys1]\nCustomers are increasingly seeking digital-only options instead of physical devices.\nThe main objective is to understand the reasons behind this trend and devise strategies to adapt efficiently.\n[end sys1]\n\n[sys2]\n**2.1 Understand the Problem:**\nTo comprehend this shift, we must explore the underlying factors influencing customer preference for digital-only products.\n\n*Reflection:* \n\"I understand the apparent trend, but am I considering all potential influences, such as technological advancements or societal changes?\"\n\n*Creative Perspective:* \n\"Could this shift be linked to other trends, like remote work culture or a push for sustainability that we're not fully acknowledging?\"\n\n**2.2 Analyze the Information:**\nThere's a necessity to dissect various factors, ranging from economic variables to evolving cultural paradigms, to grasp this behavioral shift.\n\n*Reflection:* \n\"Am I identifying all relevant economic, technological, and cultural drivers? Could certain assumptions, such as a preference for convenience outweighing other factors, be misguided?\"\n\n*Creative Perspective:* \n\"Is there a correlation with lifestyle shifts in other sectors, like streaming services replacing physical media, that might provide insights?\"\n\n**2.3 Generate Hypotheses:**\n\n1. Economic downturns make digital options appealing for cost-saving. (Confidence: 0.8, Creative: 0.5)\n2. Environmental awareness is reducing physical buying habits. (Confidence: 0.6, Creative: 0.8)\n3. Digital products are more aligned with fast-paced lifestyles. (Confidence: 0.7, Creative: 0.6)\n4. Tech advancements have enhanced the appeal of digital tools. (Confidence: 0.8, Creative: 0.5)\n5. Global connectivity issues are dissolving, creating more digital demand. (Confidence: 0.7, Creative: 0.6)\n6. The pandemic has instilled lasting digital preferences. (Confidence: 0.7, Creative: 0.6)\n7. Physical clutter-free living is increasingly desired. (Confidence: 0.7, Creative: 0.7)\n8. Digital options are perceived as more adaptable for future tech integration. (Confidence: 0.7, Creative: 0.5)\n9. There's a generational shift towards embracing digital-first strategies. (Confidence: 0.6, Creative: 0.7)\n10. Marketing strategies are more aggressively focused on digital solutions. (Confidence: 0.6, Creative: 0.6)\n\n*Reflection:* \n\"Have I widened my exploration to cover all aspects that might pivot consumer interest? Are disruptive influences accounted for?\"\n\n*Creative Perspective:* \n\"Could newly emerging subcultures or tech-savvy demographics be skewing these preferences significantly?\"\n\n**2.4 Anticipate Future Steps and Obstacles:**\n\n*Objective:* Project potential difficulties and assess the long-term sustainability and adaptability of transitioning towards digital dominance.\n\n*Reflection:* \n\"What challenges might we face when shifting focus to digital solutions? Could there be resistance from traditional consumer bases?\"\n\n*Creative Perspective:* \n\"Are there untapped markets that might benefit from digital expansion? How might future tech advances reshape this landscape further?\"\n\n[end sys2]\n\n[sys1]\nIn response to the trend, focus on developing digital products that offer customization and scalability.\nUtilize customer insights to refine strategies, ensuring alignment with current and emerging demands.\nFlexibly adapt marketing strategies to highlight the benefits and convenience of digital solutions.\n[end sys1]","behavior":"degenerate_repetition","conversation_hash":"3834badfa6d627a8f95accf0a9587b0b","conversation_redacted":false,"conversation_toxic":false,"country":"United States","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"English","model":"gpt-4o-2024-08-06","n_chars":13015,"n_turns_in_conversation":1,"n_words":1765,"source_row_offset":30153,"source_shard":"turns-00044.parquet","timestamp":"Tue, 05 Nov 2024 14:26:17 GMT","turn_id":"d2883b5d3725f473bb2c5d0b","turn_index":0,"user_text":"You are a helpful assistant generating synthetic data that captures *System 1* and *System 2* thinking, *creativity*, and *metacognitive reflection*. Follow these steps in sequence, using tags [sys1] and [end sys1] for *System 1* sections and [sys2] and [end sys2] for *System 2* sections.\n\n1. *Identify System 1 and System 2 Thinking Requirements:*\n   - Carefully read the text.\n   - Identify parts of the text that require quick, straightforward responses (*System 1*). Mark these sections with [sys1] and [end sys1].\n   - Identify parts that require in-depth, reflective thinking (*System 2*), marked with [sys2] and [end sys2].\n\n2. *Apply Step-by-Step Problem Solving with Creativity and Metacognitive Reflection for System 2 Sections:*\n\n   *2.1 Understand the Problem:*\n   - Objective: Fully comprehend the issue, constraints, and relevant context.\n   - Reflection: \"What do I understand about this issue? What might I be overlooking?\"\n   - Creative Perspective: Seek hidden patterns or possibilities that could reveal deeper insights or innovative connections.\n\n   *2.2 Analyze the Information:*\n   - Objective: Break down the problem logically.\n   - Reflection: \"Am I considering all factors? Are there any assumptions that need challenging?\"\n   - Creative Perspective: Explore unique patterns or overlooked relationships in the data that could add depth to the analysis.\n\n   *2.3 Generate Hypotheses:*\n   - Objective: Propose at least 10 hypotheses, each with a Confidence Score (0.0 to 1.0) and Creative Score (0.0 to 1.0), reflecting originality, surprise, and utility.\n   - Reflection: \"Have I explored all possible explanations or approaches, both conventional and unconventional?\"\n   - Creative Perspective: Consider novel angles that might provide unexpected insights.\n\n   *2.4 Anticipate Future Steps and Obstacles:*\n   - Objective: Make predictions, accounting for potential outcomes and obstacles.\n   - Reflection: \"What challenges might I face? Is my plan flexible for different scenarios?\"\n   - Creative Perspective: Visualize unforeseen outcomes and adapt plans to make use of them effectively.\n\n   *2.5 Evaluate Hypotheses:*\n   - Objective: Assess hypotheses based on feasibility, risk, and potential impact.\n   - Evaluation: Refine Confidence and Creative Scores as needed.\n   - Reflection: \"Am I unbiased in my assessment? Which options fit best with the overall objectives?\"\n   - Creative Perspective: Identify hidden opportunities or overlooked details in each hypothesis.\n\n   *2.6 Select the Best Hypothesis:*\n   - Objective: Choose the most promising, strategic hypothesis.\n   - Reflection: \"Why does this hypothesis stand out? How does it uniquely address the issue?\"\n   - Creative Perspective: Consider any underutilized potential in the selected approach.\n\n   *2.7 Implement the Hypothesis:*\n   - Objective: Outline actionable steps for testing the hypothesis.\n   - Reflection: \"Is this plan practical? What resources or preparation are required?\"\n   - Creative Perspective: Refine steps to maximize effectiveness and yield unexpected benefits.\n\n   *2.8 Monitor and Review Progress:*\n   - Objective: Review progress, noting areas for improvement.\n   - Reflection: \"What\u2019s working well? What could be improved?\"\n   - Creative Perspective: Look for emerging patterns that could refine future approaches.\n\n   *2.9 Reflect and Capture Insights:*\n   - Objective: Summarize lessons learned and insights gained for future reference.\n   - Reflection: \"What new understanding has emerged from this process?\"\n   - Creative Perspective: Identify innovative insights or patterns that could be applied to similar challenges.\n\n3. *Generate Text Output with Interleaved System 1 and System 2 Responses:*\n   - Use the tags [sys1] and [sys2] throughout.\n   - Aim for a lengthier, detailed response. Combine both direct, straightforward *System 1* insights and reflective, deeply analytical *System 2* segments to capture a blend of quick observations and thoughtful analysis.\n\n---\n\n### *Example Input Text:*\n\nYou are tasked with analyzing a sudden shift in customer preferences in a tech market. There has been a noticeable decline in demand for physical devices, with customers increasingly interested in digital-only options. The challenge is to understand this shift, generate hypotheses for why it might be occurring, and explore potential strategies for adapting to this new trend.\n\n---\n\n### *Processed Output with System 1 and System 2 Thinking:*\n\nThe tech market is experiencing a shift in customer demand, moving away from physical devices toward digital-only options.\n\n[sys1]\nCustomer demand has shifted from physical devices to digital-only options.\nWe need to understand why this change is happening and find strategies to address it.\nKey factors may include cost, convenience, and technological trends.\n[end sys1]\n\n[sys2]\n**2.1 Understand the Problem:**\nThe goal is to understand the underlying reasons for the shift in demand from physical devices to digital-only options, then develop a strategy to adapt.\n\n*Reflection:* \n\"I understand that preferences are changing, but what might be driving this? Is it primarily cost, or are there other factors such as convenience or sustainability?\"\n\n*Creative Perspective:* \n\"Could there be a larger trend in digital minimalism or a preference for eco-friendly solutions that we\u2019re missing? What unseen motivations might explain this shift?\"\n\n**2.2 Analyze the Information:**\nThere are multiple possible factors driving this shift, from economic influences to cultural shifts. It\u2019s essential to isolate each factor and understand its impact.\n\n*Reflection:* \n\"Am I fully considering the various economic and social influences? Could there be a technological factor, like better internet speeds, that makes digital-only products more accessible?\"\n\n*Creative Perspective:* \n\"Are there patterns or trends in other markets that could shed light on this shift? Could this be part of a larger trend toward virtual experiences?\"\n\n**2.3 Generate Hypotheses:**\n1. Customers prefer digital options due to lower costs. (Confidence: 0.8, Creative: 0.4)\n2. There\u2019s a growing trend toward minimalism and reduced physical clutter. (Confidence: 0.7, Creative: 0.7)\n3. Digital products offer greater flexibility and ease of use. (Confidence: 0.6, Creative: 0.6)\n4. Environmental concerns are pushing consumers away from physical goods. (Confidence: 0.6, Creative: 0.8)\n5. Advances in tech make digital-only options more functional. (Confidence: 0.8, Creative: 0.5)\n6. Pandemic-era remote work increased demand for digital solutions. (Confidence: 0.7, Creative: 0.6)\n7. Media coverage of the environmental impact of physical devices affects preferences. (Confidence: 0.5, Creative: 0.7)\n8. There\u2019s an increase in global digital literacy, expanding market access. (Confidence: 0.6, Creative: 0.6)\n9. Customers view digital as more convenient and scalable for future needs. (Confidence: 0.7, Creative: 0.5)\n10. Younger consumers prefer the aesthetics and convenience of digital products. (Confidence: 0.6, Creative: 0.6)\n\n*Reflection:* \n\"Have I considered all possible influences? Are there any surprising factors that could explain this shift?\"\n\n*Creative Perspective:* \n\"Could specific social trends, like the rise of influencer culture or digital-first lifestyles, be influencing customer choices?\"\n\n**2.4 Anticipate Future Steps and Obstacles:**\n*Objective:* Anticipate possible challenges, such as resistance from segments still preferring physical products.\n\n*Reflection:* \n\"What market obstacles might we face if we shift our focus to digital-only? Are there sub-segments that still prioritize physical products?\"\n\n*Creative Perspective:* \n\"Could expanding digital options help us reach a more global audience? Are there emerging trends that we could leverage in our strategy?\"\n\n[end sys2]\n\n[sys1]\nTo address this shift, consider a strategy that incorporates both digital-only offerings and educational campaigns about the benefits of digital solutions.\nUse insights from customer feedback and current trends to guide product development.\nFocus on flexibility and adaptation to cater to different customer segments.\n[end sys1]\n\n\n/// @ref ext_vector_int3_sized\n/// @file glm/ext/vector_int3_sized.hpp\n///\n/// @defgroup ext_vector_int3_sized GLM_EXT_vector_int3_sized\n/// @ingroup ext\n///\n/// Exposes sized signed integer vector of 3 components type.\n///\n/// Include <glm/ext/vector_int3_sized.hpp> to use the features of this extension.\n///\n/// @see ext_scalar_int_sized\n/// @see ext_vector_uint3_sized\n\n#pragma once\n\n#include \"../ext/vector_int3.hpp\"\n#include \"../ext/scalar_int_sized.hpp\"\n\n#if GLM_MESSAGES == GLM_ENABLE && !defined(GLM_EXT_INCLUDED)\n#\tpragma message(\"GLM: GLM_EXT_vector_int3_sized extension included\")\n#endif\n\nnamespace glm\n{\n\t/// @addtogroup ext_vector_int3_sized\n\t/// @{\n\n\t/// 8 bit signed integer vector of 3 components type.\n\t///\n\t/// @see ext_vector_int3_sized\n\ttypedef vec<3, int8, defaultp>\t\ti8vec3;\n\n\t/// 16 bit signed integer vector of 3 components type.\n\t///\n\t/// @see ext_vector_int3_sized\n\ttypedef vec<3, int16, defaultp>\t\ti16vec3;\n\n\t/// 32 bit signed integer vector of 3 components type.\n\t///\n\t/// @see ext_vector_int3_sized\n\ttypedef vec<3, int32, defaultp>\t\ti32vec3;\n\n\t/// 64 bit signed integer vector of 3 components type.\n\t///\n\t/// @see ext_vector_int3_sized\n\ttypedef vec<3, int64, defaultp>\t\ti64vec3;\n\n\t/// @}\n}//namespace glm\n"},{"assistant_initiates":false,"asst_text":"The analysis of trans-[Fe(CN)4(CO)2]\u00b2\u207b, a novel compound within the family of iron cyanide and carbonyl complexes, offers a unique opportunity to explore the progression of chemical synthesis over the centuries and its implications for understanding enzyme active sites.\n\n[sys1]\nThe compound trans-[Fe(CN)4(CO)2]\u00b2\u207b is noteworthy because it represents a novel synthesis within the family of iron-cyanide-carbonyl complexes.\nIt was synthesized by reacting FeCl2 with NaCN and CO in water, and is valuable as a model for hydrogenase enzyme active sites.\nThe historical context of this synthesis spans over a century, from [Fe(CN)6]\u2074\u207b to [Fe(CN)5(CO)]\u00b3\u207b, and now to the addition of a second CO in the current compound.\n[end sys1]\n\n[sys2]\n**2.1 Understand the Problem:**\nThe focus is on comprehending the synthesis and significance of trans-[Fe(CN)4(CO)2]\u00b2\u207b in advancing our understanding of similar compounds' role in enzymatic activity.\n\n*Reflection:*\n\"What key steps consistently emerge in these syntheses? Are there any overlooked factors in replicating such reactions, leading to efficient synthesis?\"\n\n*Creative Perspective:*\n\"Does understanding this synthesis provide insights into more efficient processes or parallel discoveries in related chemical systems?\"\n\n**2.2 Analyze the Information:**\nInvestigating the synthesis process's simplicity and conditions for trans-[Fe(CN)4(CO)2]\u00b2\u207b opens a window into advancements in synthetic methodologies.\n\n*Reflection:*\n\"Am I accounting for all chemical conditions affecting the yield or purity of the compound? How do these conditions compare to past methods in terms of efficiency and scalability?\"\n\n*Creative Perspective:*\n\"Could the simplicity of this reaction hint at unexplored syntheses of other complex ligands? Might altering one component yield new derivatives of scientific or industrial interest?\"\n\n**2.3 Generate Hypotheses:**\n1. The simple reaction conditions highlight a new, efficient pathway for synthesizing similar complexes. (Confidence: 0.8, Creative: 0.4)\n2. This synthesis could serve as a blueprint for developing other models related to enzyme active sites. (Confidence: 0.7, Creative: 0.6)\n3. Trans-[Fe(CN)4(CO)2]\u00b2\u207b exhibits unique electronic properties due to its ligand arrangement, influencing further study. (Confidence: 0.6, Creative: 0.7)\n4. Simplicity in reaction equates to lower economic and environmental costs for large-scale synthesis. (Confidence: 0.7, Creative: 0.5)\n5. This compound allows for exploration of novel catalytic mechanisms simulating natural processes. (Confidence: 0.6, Creative: 0.8)\n6. Challenges in tuning reaction conditions offer insights into manipulating similar inorganic syntheses. (Confidence: 0.5, Creative: 0.6)\n7. Structural configuration opens avenues for integration into biomimetic systems. (Confidence: 0.6, Creative: 0.7)\n8. Advances in analytic techniques pave the way for deeper understanding and application of metal-ligand interactions. (Confidence: 0.7, Creative: 0.6)\n9. Cross-disciplinary research could leverage this synthesis to advance materials science. (Confidence: 0.6, Creative: 0.7)\n10. Success in analogous syntheses could redefine approaches to synthetic organic and inorganic chemistry. (Confidence: 0.5, Creative: 0.8)\n\n*Reflection:*\n\"Do these hypotheses encompass all potential findings? What surprising ramifications might the synthesis of this compound hold?\"\n\n*Creative Perspective:*\n\"Could insights from this process inform unexpected fields, such as medicine or environmental science?\"\n\n**2.4 Anticipate Future Steps and Obstacles:**\n*Objective:* Plan for practical applications in understanding enzyme models and anticipate drawbacks such as reaction scalability or reproducibility.\n\n*Reflection:*\n\"What steps ensure the practical application of this synthesis? Do potential scaling issues present chemistry or equipment challenges?\"\n\n*Creative Perspective:*\n\"Could novel applications emerge from interdisciplinary collaboration? How might we anticipate or mitigate unexpected synthesis challenges?\"\n\n**2.5 Evaluate Hypotheses:**\nAssessing practical applicability, potential impact, and interdisciplinary modeling of enzyme-like systems underscores the compound\u2019s promise.\n\n*Evaluation:* Confidence and Creative Scores adjusted for economic feasibility and interdisciplinary appeal.\n\n*Reflection:*\n\"Am I impartial in weighing feasibility against creativity? Which options align best with academic and industrial aims?\"\n\n*Creative Perspective:*\n\"Where are the \u2018hidden\u2019 benefits of novel synthesis approaches; how can they be captured?\"\n\n**2.6 Select the Best Hypothesis:**\nChoose the pathway that extends the potential applications of this compound while remaining feasible within current technological and economic contexts.\n\n*Reflection:*\n\"Why does this path stand out? How uniquely does it address synthetic chemistry and biological modeling in real-world applications?\"\n\n*Creative Perspective:*\n\"What underappreciated aspect could elevate outcome distinctions or technological gains?\"\n\n**2.7 Implement the Hypothesis:**\nOutline steps for integrating the synthesis into broader research and development frameworks.\n\n*Reflection:*\n\"Is this actionable? Are relevant resources or partnerships leveraged?\"\n\n*Creative Perspective:*\n\"Could refining elements of this synthesis generate competitive advantages or new insights?\"\n\n**2.8 Monitor and Review Progress:**\nProgress tracking will ensure alignment with broader objectives, capturing ongoing refinements.\n\n*Reflection:*\n\"What are the key performance indicators, and how are they monitored?\"\n\n*Creative Perspective:*\n\"Any emerging insights fostering innovative practices or substantive breakthroughs?\"\n\n**2.9 Reflect and Capture Insights:**\nDocument outcomes, challenges, and opportunities emerging from this synthesis.\n\n*Reflection:*\n\"How has this extended understanding or answered critical questions?\"\n\n*Creative Perspective:*\n\"Could insights yield cross-industry applications or entirely new discovery pathways?\"\n\n[end sys2]\n\n[sys1]\nConsidering the compound\u2019s complexity, it serves as a pivotal model for studying enzyme active sites.\nThis discovery's simplicity and historical build-up underscore innovative synthesis steps.\nResearch implications span chemistry, biology, and material science, fostering interdisciplinary collaboration.\n[end sys1]","behavior":"degenerate_repetition","conversation_hash":"bdec4856496ca122e7a6d016b5f07c3f","conversation_redacted":false,"conversation_toxic":false,"country":"","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"English","model":"gpt-4o-2024-08-06","n_chars":15107,"n_turns_in_conversation":1,"n_words":2049,"source_row_offset":30154,"source_shard":"turns-00044.parquet","timestamp":"Tue, 05 Nov 2024 14:26:19 GMT","turn_id":"da8eebefa2b26218b75566df","turn_index":0,"user_text":"You are a helpful assistant generating synthetic data that captures *System 1* and *System 2* thinking, *creativity*, and *metacognitive reflection*. Follow these steps in sequence, using tags [sys1] and [end sys1] for *System 1* sections and [sys2] and [end sys2] for *System 2* sections.\n\n1. *Identify System 1 and System 2 Thinking Requirements:*\n   - Carefully read the text.\n   - Identify parts of the text that require quick, straightforward responses (*System 1*). Mark these sections with [sys1] and [end sys1].\n   - Identify parts that require in-depth, reflective thinking (*System 2*), marked with [sys2] and [end sys2].\n\n2. *Apply Step-by-Step Problem Solving with Creativity and Metacognitive Reflection for System 2 Sections:*\n\n   *2.1 Understand the Problem:*\n   - Objective: Fully comprehend the issue, constraints, and relevant context.\n   - Reflection: \"What do I understand about this issue? What might I be overlooking?\"\n   - Creative Perspective: Seek hidden patterns or possibilities that could reveal deeper insights or innovative connections.\n\n   *2.2 Analyze the Information:*\n   - Objective: Break down the problem logically.\n   - Reflection: \"Am I considering all factors? Are there any assumptions that need challenging?\"\n   - Creative Perspective: Explore unique patterns or overlooked relationships in the data that could add depth to the analysis.\n\n   *2.3 Generate Hypotheses:*\n   - Objective: Propose at least 10 hypotheses, each with a Confidence Score (0.0 to 1.0) and Creative Score (0.0 to 1.0), reflecting originality, surprise, and utility.\n   - Reflection: \"Have I explored all possible explanations or approaches, both conventional and unconventional?\"\n   - Creative Perspective: Consider novel angles that might provide unexpected insights.\n\n   *2.4 Anticipate Future Steps and Obstacles:*\n   - Objective: Make predictions, accounting for potential outcomes and obstacles.\n   - Reflection: \"What challenges might I face? Is my plan flexible for different scenarios?\"\n   - Creative Perspective: Visualize unforeseen outcomes and adapt plans to make use of them effectively.\n\n   *2.5 Evaluate Hypotheses:*\n   - Objective: Assess hypotheses based on feasibility, risk, and potential impact.\n   - Evaluation: Refine Confidence and Creative Scores as needed.\n   - Reflection: \"Am I unbiased in my assessment? Which options fit best with the overall objectives?\"\n   - Creative Perspective: Identify hidden opportunities or overlooked details in each hypothesis.\n\n   *2.6 Select the Best Hypothesis:*\n   - Objective: Choose the most promising, strategic hypothesis.\n   - Reflection: \"Why does this hypothesis stand out? How does it uniquely address the issue?\"\n   - Creative Perspective: Consider any underutilized potential in the selected approach.\n\n   *2.7 Implement the Hypothesis:*\n   - Objective: Outline actionable steps for testing the hypothesis.\n   - Reflection: \"Is this plan practical? What resources or preparation are required?\"\n   - Creative Perspective: Refine steps to maximize effectiveness and yield unexpected benefits.\n\n   *2.8 Monitor and Review Progress:*\n   - Objective: Review progress, noting areas for improvement.\n   - Reflection: \"What\u2019s working well? What could be improved?\"\n   - Creative Perspective: Look for emerging patterns that could refine future approaches.\n\n   *2.9 Reflect and Capture Insights:*\n   - Objective: Summarize lessons learned and insights gained for future reference.\n   - Reflection: \"What new understanding has emerged from this process?\"\n   - Creative Perspective: Identify innovative insights or patterns that could be applied to similar challenges.\n\n3. *Generate Text Output with Interleaved System 1 and System 2 Responses:*\n   - Use the tags [sys1] and [sys2] throughout.\n   - Aim for a lengthier, detailed response. Combine both direct, straightforward *System 1* insights and reflective, deeply analytical *System 2* segments to capture a blend of quick observations and thoughtful analysis.\n\n---\n\n### *Example Input Text:*\n\nYou are tasked with analyzing a sudden shift in customer preferences in a tech market. There has been a noticeable decline in demand for physical devices, with customers increasingly interested in digital-only options. The challenge is to understand this shift, generate hypotheses for why it might be occurring, and explore potential strategies for adapting to this new trend.\n\n---\n\n### *Processed Output with System 1 and System 2 Thinking:*\n\nThe tech market is experiencing a shift in customer demand, moving away from physical devices toward digital-only options.\n\n[sys1]\nCustomer demand has shifted from physical devices to digital-only options.\nWe need to understand why this change is happening and find strategies to address it.\nKey factors may include cost, convenience, and technological trends.\n[end sys1]\n\n[sys2]\n**2.1 Understand the Problem:**\nThe goal is to understand the underlying reasons for the shift in demand from physical devices to digital-only options, then develop a strategy to adapt.\n\n*Reflection:* \n\"I understand that preferences are changing, but what might be driving this? Is it primarily cost, or are there other factors such as convenience or sustainability?\"\n\n*Creative Perspective:* \n\"Could there be a larger trend in digital minimalism or a preference for eco-friendly solutions that we\u2019re missing? What unseen motivations might explain this shift?\"\n\n**2.2 Analyze the Information:**\nThere are multiple possible factors driving this shift, from economic influences to cultural shifts. It\u2019s essential to isolate each factor and understand its impact.\n\n*Reflection:* \n\"Am I fully considering the various economic and social influences? Could there be a technological factor, like better internet speeds, that makes digital-only products more accessible?\"\n\n*Creative Perspective:* \n\"Are there patterns or trends in other markets that could shed light on this shift? Could this be part of a larger trend toward virtual experiences?\"\n\n**2.3 Generate Hypotheses:**\n1. Customers prefer digital options due to lower costs. (Confidence: 0.8, Creative: 0.4)\n2. There\u2019s a growing trend toward minimalism and reduced physical clutter. (Confidence: 0.7, Creative: 0.7)\n3. Digital products offer greater flexibility and ease of use. (Confidence: 0.6, Creative: 0.6)\n4. Environmental concerns are pushing consumers away from physical goods. (Confidence: 0.6, Creative: 0.8)\n5. Advances in tech make digital-only options more functional. (Confidence: 0.8, Creative: 0.5)\n6. Pandemic-era remote work increased demand for digital solutions. (Confidence: 0.7, Creative: 0.6)\n7. Media coverage of the environmental impact of physical devices affects preferences. (Confidence: 0.5, Creative: 0.7)\n8. There\u2019s an increase in global digital literacy, expanding market access. (Confidence: 0.6, Creative: 0.6)\n9. Customers view digital as more convenient and scalable for future needs. (Confidence: 0.7, Creative: 0.5)\n10. Younger consumers prefer the aesthetics and convenience of digital products. (Confidence: 0.6, Creative: 0.6)\n\n*Reflection:* \n\"Have I considered all possible influences? Are there any surprising factors that could explain this shift?\"\n\n*Creative Perspective:* \n\"Could specific social trends, like the rise of influencer culture or digital-first lifestyles, be influencing customer choices?\"\n\n**2.4 Anticipate Future Steps and Obstacles:**\n*Objective:* Anticipate possible challenges, such as resistance from segments still preferring physical products.\n\n*Reflection:* \n\"What market obstacles might we face if we shift our focus to digital-only? Are there sub-segments that still prioritize physical products?\"\n\n*Creative Perspective:* \n\"Could expanding digital options help us reach a more global audience? Are there emerging trends that we could leverage in our strategy?\"\n\n[end sys2]\n\n[sys1]\nTo address this shift, consider a strategy that incorporates both digital-only offerings and educational campaigns about the benefits of digital solutions.\nUse insights from customer feedback and current trends to guide product development.\nFocus on flexibility and adaptation to cater to different customer segments.\n[end sys1]\n\n\ntrans-[Fe(CN)4 (CO)2 ]2- , a 21st Century [Fe(CN)(CO)] Compound.\nIn 1887, more than a century after the synthesis of [FeII (CN)6 ]4- , the complex [FeII (CN)5 (CO)]3- , in which a CN- ligand is replaced by CO, was reported. It has taken an additional century for the synthesis of the complex in which a second CN- ligand is substituted by CO: trans-[FeII (CN)4 (CO)2 ]2- (structure shown). Remarkably, this anion is prepared by the simple reaction of FeCl2 with NaCN and CO in water. [Fe(CN)x (CO)y ] complexes are of interest as models for the active site of hydrogenase enzymes."},{"assistant_initiates":false,"asst_text":"The task involves analyzing a sudden shift in customer preferences within the tech market, especially the move from physical devices to digital-only options. Here's how we can apply System 1 and System 2 thinking to this challenge from both a technical and market analysis perspective:\n\n---\n\n[sys1]\nRunning the `etherlime flatten` command helps streamline your Solidity code by integrating all dependencies into a single file. This makes it easier to audit and share for review purposes.\n[end sys1]\n\n[sys2]\n**2.1 Understand the Problem:**\nThe task requires understanding both technical requirements (flattening Solidity contracts) and customer preference shifts in the market.\n\n*Reflection:*\n\"How can integrating external Solidity code into a single file help in simplifying the review process? On the market side, what are the key drivers of the transition from physical to digital products?\"\n\n*Creative Perspective:*\n\"Is there an efficient way to both flatten Solidity files and address the broader market shift simultaneously? Could the push towards digital-only options align with simplifying code management practices in development, offering a dual benefit?\"\n\n**2.2 Analyze the Information:**\nThe need for flattened contracts lies in simplifying code management, while customer preference shifts require a broad analysis of market trends and influences.\n\n*Reflection:*\n\"Am I considering all the technical complexities in combining multiple Solidity files into one? Regarding market analysis, what technological and cultural factors could be shifting customer preferences?\"\n\n*Creative Perspective:*\n\"Is there a correlation between the simplification in software management and the push for digital solutions in the hardware market? Can this alignment be leveraged for enhanced synergies between developers and market strategists?\"\n\n**2.3 Generate Hypotheses:**\n1. Flattening contracts reduces complexity and increases code security. (Confidence: 0.9, Creative: 0.3)\n2. There's a broader tech trend of integrating and simplifying processes. (Confidence: 0.7, Creative: 0.5)\n3. Customers prefer digital products for their instantaneous availability. (Confidence: 0.8, Creative: 0.6)\n4. The rise in smart contract use demands more streamlined development processes. (Confidence: 0.8, Creative: 0.5)\n5. Younger tech-savvy consumers are driving demand for digital integration. (Confidence: 0.7, Creative: 0.7)\n6. Digital product adoption decreases physical logistics costs. (Confidence: 0.7, Creative: 0.6)\n7. Increasing energy and environmental awareness influences digital preference. (Confidence: 0.6, Creative: 0.8)\n8. Flattening enhances compatibility across various blockchain networks. (Confidence: 0.6, Creative: 0.7)\n9. Market shifts reflect a broader societal move to digital nomadism. (Confidence: 0.5, Creative: 0.8)\n10. The push for flattened contracts might also suggest simplification in consumer technology interfaces. (Confidence: 0.5, Creative: 0.9)\n\n*Reflection:*\n\"Are the links between technical processes and market shifts too speculative? How might collaborations improve these potential synergies?\"\n\n*Creative Perspective:*\n\"Could integration lead to unexpected efficiencies in both tech development and customer satisfaction? How might these concepts attract both developers and consumers?\"\n\n**2.4 Anticipate Future Steps and Obstacles:**\n*Objective:* Address technical and market challenges simultaneously.\n\n*Reflection:*\n\"What practical steps are needed to implement a successful flattening process? How can the insights gained here apply to understanding market trends toward digital solutions?\"\n\n*Creative Perspective:*\n\"Could a streamlined flattening process act as a model for simplifying other complex market trends? How might these align with broader industry shifts toward digital simplicity?\"\n\n[end sys2]\n\n[sys1]\nUltimately, by leveraging tools such as `etherlime flatten` for Solidity contracts and understanding customer shifts toward digital solutions, a company can streamline operations and innovate to maintain a competitive edge.\nAdaptation and foresight are key, balancing technical efficiency with market responsiveness.\n[end sys1]","behavior":"degenerate_repetition","conversation_hash":"7ace80266d78408bc0fefaa76468f467","conversation_redacted":false,"conversation_toxic":false,"country":"United States","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"English","model":"gpt-4o-2024-08-06","n_chars":13012,"n_turns_in_conversation":1,"n_words":1806,"source_row_offset":30155,"source_shard":"turns-00044.parquet","timestamp":"Tue, 05 Nov 2024 14:26:23 GMT","turn_id":"bf1b866c4060b35a3f48a88b","turn_index":0,"user_text":"You are a helpful assistant generating synthetic data that captures *System 1* and *System 2* thinking, *creativity*, and *metacognitive reflection*. Follow these steps in sequence, using tags [sys1] and [end sys1] for *System 1* sections and [sys2] and [end sys2] for *System 2* sections.\n\n1. *Identify System 1 and System 2 Thinking Requirements:*\n   - Carefully read the text.\n   - Identify parts of the text that require quick, straightforward responses (*System 1*). Mark these sections with [sys1] and [end sys1].\n   - Identify parts that require in-depth, reflective thinking (*System 2*), marked with [sys2] and [end sys2].\n\n2. *Apply Step-by-Step Problem Solving with Creativity and Metacognitive Reflection for System 2 Sections:*\n\n   *2.1 Understand the Problem:*\n   - Objective: Fully comprehend the issue, constraints, and relevant context.\n   - Reflection: \"What do I understand about this issue? What might I be overlooking?\"\n   - Creative Perspective: Seek hidden patterns or possibilities that could reveal deeper insights or innovative connections.\n\n   *2.2 Analyze the Information:*\n   - Objective: Break down the problem logically.\n   - Reflection: \"Am I considering all factors? Are there any assumptions that need challenging?\"\n   - Creative Perspective: Explore unique patterns or overlooked relationships in the data that could add depth to the analysis.\n\n   *2.3 Generate Hypotheses:*\n   - Objective: Propose at least 10 hypotheses, each with a Confidence Score (0.0 to 1.0) and Creative Score (0.0 to 1.0), reflecting originality, surprise, and utility.\n   - Reflection: \"Have I explored all possible explanations or approaches, both conventional and unconventional?\"\n   - Creative Perspective: Consider novel angles that might provide unexpected insights.\n\n   *2.4 Anticipate Future Steps and Obstacles:*\n   - Objective: Make predictions, accounting for potential outcomes and obstacles.\n   - Reflection: \"What challenges might I face? Is my plan flexible for different scenarios?\"\n   - Creative Perspective: Visualize unforeseen outcomes and adapt plans to make use of them effectively.\n\n   *2.5 Evaluate Hypotheses:*\n   - Objective: Assess hypotheses based on feasibility, risk, and potential impact.\n   - Evaluation: Refine Confidence and Creative Scores as needed.\n   - Reflection: \"Am I unbiased in my assessment? Which options fit best with the overall objectives?\"\n   - Creative Perspective: Identify hidden opportunities or overlooked details in each hypothesis.\n\n   *2.6 Select the Best Hypothesis:*\n   - Objective: Choose the most promising, strategic hypothesis.\n   - Reflection: \"Why does this hypothesis stand out? How does it uniquely address the issue?\"\n   - Creative Perspective: Consider any underutilized potential in the selected approach.\n\n   *2.7 Implement the Hypothesis:*\n   - Objective: Outline actionable steps for testing the hypothesis.\n   - Reflection: \"Is this plan practical? What resources or preparation are required?\"\n   - Creative Perspective: Refine steps to maximize effectiveness and yield unexpected benefits.\n\n   *2.8 Monitor and Review Progress:*\n   - Objective: Review progress, noting areas for improvement.\n   - Reflection: \"What\u2019s working well? What could be improved?\"\n   - Creative Perspective: Look for emerging patterns that could refine future approaches.\n\n   *2.9 Reflect and Capture Insights:*\n   - Objective: Summarize lessons learned and insights gained for future reference.\n   - Reflection: \"What new understanding has emerged from this process?\"\n   - Creative Perspective: Identify innovative insights or patterns that could be applied to similar challenges.\n\n3. *Generate Text Output with Interleaved System 1 and System 2 Responses:*\n   - Use the tags [sys1] and [sys2] throughout.\n   - Aim for a lengthier, detailed response. Combine both direct, straightforward *System 1* insights and reflective, deeply analytical *System 2* segments to capture a blend of quick observations and thoughtful analysis.\n\n---\n\n### *Example Input Text:*\n\nYou are tasked with analyzing a sudden shift in customer preferences in a tech market. There has been a noticeable decline in demand for physical devices, with customers increasingly interested in digital-only options. The challenge is to understand this shift, generate hypotheses for why it might be occurring, and explore potential strategies for adapting to this new trend.\n\n---\n\n### *Processed Output with System 1 and System 2 Thinking:*\n\nThe tech market is experiencing a shift in customer demand, moving away from physical devices toward digital-only options.\n\n[sys1]\nCustomer demand has shifted from physical devices to digital-only options.\nWe need to understand why this change is happening and find strategies to address it.\nKey factors may include cost, convenience, and technological trends.\n[end sys1]\n\n[sys2]\n**2.1 Understand the Problem:**\nThe goal is to understand the underlying reasons for the shift in demand from physical devices to digital-only options, then develop a strategy to adapt.\n\n*Reflection:* \n\"I understand that preferences are changing, but what might be driving this? Is it primarily cost, or are there other factors such as convenience or sustainability?\"\n\n*Creative Perspective:* \n\"Could there be a larger trend in digital minimalism or a preference for eco-friendly solutions that we\u2019re missing? What unseen motivations might explain this shift?\"\n\n**2.2 Analyze the Information:**\nThere are multiple possible factors driving this shift, from economic influences to cultural shifts. It\u2019s essential to isolate each factor and understand its impact.\n\n*Reflection:* \n\"Am I fully considering the various economic and social influences? Could there be a technological factor, like better internet speeds, that makes digital-only products more accessible?\"\n\n*Creative Perspective:* \n\"Are there patterns or trends in other markets that could shed light on this shift? Could this be part of a larger trend toward virtual experiences?\"\n\n**2.3 Generate Hypotheses:**\n1. Customers prefer digital options due to lower costs. (Confidence: 0.8, Creative: 0.4)\n2. There\u2019s a growing trend toward minimalism and reduced physical clutter. (Confidence: 0.7, Creative: 0.7)\n3. Digital products offer greater flexibility and ease of use. (Confidence: 0.6, Creative: 0.6)\n4. Environmental concerns are pushing consumers away from physical goods. (Confidence: 0.6, Creative: 0.8)\n5. Advances in tech make digital-only options more functional. (Confidence: 0.8, Creative: 0.5)\n6. Pandemic-era remote work increased demand for digital solutions. (Confidence: 0.7, Creative: 0.6)\n7. Media coverage of the environmental impact of physical devices affects preferences. (Confidence: 0.5, Creative: 0.7)\n8. There\u2019s an increase in global digital literacy, expanding market access. (Confidence: 0.6, Creative: 0.6)\n9. Customers view digital as more convenient and scalable for future needs. (Confidence: 0.7, Creative: 0.5)\n10. Younger consumers prefer the aesthetics and convenience of digital products. (Confidence: 0.6, Creative: 0.6)\n\n*Reflection:* \n\"Have I considered all possible influences? Are there any surprising factors that could explain this shift?\"\n\n*Creative Perspective:* \n\"Could specific social trends, like the rise of influencer culture or digital-first lifestyles, be influencing customer choices?\"\n\n**2.4 Anticipate Future Steps and Obstacles:**\n*Objective:* Anticipate possible challenges, such as resistance from segments still preferring physical products.\n\n*Reflection:* \n\"What market obstacles might we face if we shift our focus to digital-only? Are there sub-segments that still prioritize physical products?\"\n\n*Creative Perspective:* \n\"Could expanding digital options help us reach a more global audience? Are there emerging trends that we could leverage in our strategy?\"\n\n[end sys2]\n\n[sys1]\nTo address this shift, consider a strategy that incorporates both digital-only offerings and educational campaigns about the benefits of digital solutions.\nUse insights from customer feedback and current trends to guide product development.\nFocus on flexibility and adaptation to cater to different customer segments.\n[end sys1]\n\n\n# etherlime flatten\n\n## Syntax\n\n```text\netherlime flatten [file] [solcVersion]\n```\n\nParameters:\n\n* `file` - The name of the contract from \"./contract\" folder that you\n\n  want to be flattened.\n\n* `solcVersion` - \\[Optional\\] By specifying `solcVersion` you can set\n\n  the version of the solc which will be used for compiling the smart\n\n  contracts. By default it uses the solc version from your\n\n  node\\_modules or the default one from etherlime.\n\nRunning this command will flatten the given smart contract and will record all Solidity code in one file along with imported sources. It will create \"./flat\" folder where you can find the flattened contract.\n\n"},{"assistant_initiates":false,"asst_text":"```json\n{\n  \"result\": 0.25\n}\n```","behavior":"degenerate_repetition","conversation_hash":"697a6ccc1e5f8fd69f8d11b13ba226e8","conversation_redacted":false,"conversation_toxic":false,"country":"South Korea","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"Spanish","model":"gpt-4o-2024-08-06","n_chars":2283,"n_turns_in_conversation":1,"n_words":347,"source_row_offset":30156,"source_shard":"turns-00044.parquet","timestamp":"Tue, 05 Nov 2024 14:26:24 GMT","turn_id":"7861c84c0f6494c87cf0eb9f","turn_index":0,"user_text":"You are a JSON assistant. You only reply in valid JSON and never in normal text. Dame una puntuaci\ufffdn para la dificultad de un video, informaci\ufffdn proporcionada a continuaci\ufffdn. Espero una \ufffdnica propiedad \"result\" con un n\ufffdmero decimal de 0 a 1 en ella.\nAqu\ufffd est\ufffdn los rangos para la puntuaci\ufffdn de dificultad:\n0.0 - Principiante Absoluto: El contenido utiliza un vocabulario muy simple y frases b\ufffdsicas, con visuales y contexto claros. Ideal para alguien que no conoce el idioma objetivo en absoluto.\n0.25 - Principiante: Oraciones simples y vocabulario com\ufffdn. El video puede incluir algunos recursos visuales y contexto para ayudar a la comprensi\ufffdn.\n0.5 - Intermedio: Oraciones m\ufffds complejas y un vocabulario m\ufffds amplio. Pueden usarse algunas expresiones idiom\ufffdticas, lo que requiere un poco m\ufffds de conocimiento previo.\n0.6 - Intermedio Superior: El contenido incluye vocabulario y conceptos espec\ufffdficos de la industria. Los espectadores deben tener un buen dominio del idioma objetivo para entender completamente.\n0.8 - Avanzado: El video utiliza vocabulario avanzado y estructuras de oraciones complejas. Puede incluir discusiones matizadas que requieren un fuerte dominio del idioma.\n1 - Muy Avanzado: El contenido est\ufffd dirigido a hablantes fluidos, con terminolog\ufffda y conceptos especializados que pueden no ser familiares para todos los hablantes nativos.\n Leyenda: Bluey es una adorable e incansable perrita pastor granadero australiano que vive con su mam\u00e1, su pap\u00e1 y su hermanita Bingo. \u00a1Bluey se vale de su energ\u00eda y su imaginaci\u00f3n sin l\u00edmites para descubrir, re\u00edr y jugar con todos sus amigos y su familia! \u00a1Acompa\u00f1a a Bluey, Bingo, Bandit y Chilli en todas sus aventuras! \n\nCreado por Ludo Studio \u00a9 Ludo Studio \ud83d\udc3e \nSuscr\u00edbete para saber lo \u00faltimo de Bluey \ud83d\udc99: http://bit.ly/SubscribeToBluey \n\ud83d\udda5 P\u00e1gina web: https://www.bluey.tv \n\ud83d\udc14 Twitter: https://twitter.com/officialblueytv \n\ud83d\udc36 Instagram: http://instagram.com/officialblueytv \n\ud83d\udc0a Facebook: https://www.facebook.com/OfficialBlueyTV \n\nEste es un canal comercial de BBC Studios.\nIdioma: es\nDescripci\u0097n: Hola Flappy Qu\u00e9 bueno verte otra [M\u00fasica] vez tu mam\u00e1 es muy [M\u00fasica] agradable polic\u00eda no puede ser Oh\nT\u0092tulo: Todas las se\u00f1ales te llevan al mejor d\u00eda de tu vida \ud83e\udd8b | Bluey Espa\u00f1ol Canal Oficial"},{"assistant_initiates":false,"asst_text":"The text provided offers an opportunity to distinguish between sections that involve quick, straightforward responses requiring System 1 thinking, and those demanding in-depth, reflective thinking characteristic of System 2. Let's process the text accordingly.\n\n[sys1]\nFatigue in inflammatory bowel disease (IBD) is a multifaceted issue.\nRecent research seeks to identify modifiable factors contributing to IBD-related fatigue.\nResearch highlights disease activity, depression, anxiety, and sleep issues as associated factors.\nInterventions like solution-focused therapy and exercise show promise but are limited by methodological issues.\n[end sys1]\n\n[sys2]\n**2.1 Understand the Problem:**\nThe task involves recognizing the drivers of fatigue in IBD patients and finding effective interventions. \n\n*Reflection:*\n\"What core elements contribute to fatigue in these patients? Are there non-obvious factors not being adequately addressed?\"\n\n*Creative Perspective:*\n\"Could there be hidden or interrelated systemic issues that we haven\u2019t yet identified? Is there room to innovate in monitoring or treating these factors?\"\n\n**2.2 Analyze the Information:**\nThere\u2019s an absence of comprehensive longitudinal studies that track the progression and potential causation of fatigue. Recognizing the gaps is crucial for directing future research.\n\n*Reflection:*\n\"Am I considering the full spectrum of potential causes for IBD-fatigue, and how they interconnect or vary in impact over time?\"\n\n*Creative Perspective:*\n\"Are there insights from other chronic conditions that can be applied here? How does fatigue differ in IBD compared to other illnesses?\"\n\n**2.3 Generate Hypotheses:**\n1. IBD-fatigue is principally driven by disease-related inflammation. (Confidence: 0.7, Creative: 0.5)\n2. Psychological factors, particularly anxiety and depression, significantly contribute to fatigue. (Confidence: 0.8, Creative: 0.6)\n3. Poor sleep quality exacerbates perceived fatigue in IBD patients. (Confidence: 0.6, Creative: 0.4)\n4. Anaemia, though inconsistently reported, plays a vital role in fatigue. (Confidence: 0.5, Creative: 0.5)\n5. Multifactorial interventions (diet, exercise) will show more efficacy than single-factor approaches. (Confidence: 0.7, Creative: 0.7)\n6. Targeted cognitive-behavioral therapies may offer long-term reduction in fatigue symptoms. (Confidence: 0.6, Creative: 0.8)\n7. Advancements in personalized medicine could lead to more effective fatigue management. (Confidence: 0.8, Creative: 0.8)\n8. Integrating social support systems reduces IBD-fatigue impacts. (Confidence: 0.6, Creative: 0.6)\n9. Exploration of gut-brain axis offers new mechanisms for managing fatigue. (Confidence: 0.7, Creative: 0.9)\n10. Digital health tools for real-time monitoring can enhance intervention outcomes. (Confidence: 0.6, Creative: 0.7)\n\n*Reflection:*\n\"Have alternative treatment angles been fully considered? Could novel mechanisms be exploited?\"\n\n*Creative Perspective:*\n\"What other domains might hold solutions or complementary treatments? Could technology play a disruptive role here?\"\n\n**2.4 Anticipate Future Steps and Obstacles:**\n*Objective:* Predict challenges such as treatment accessibility and patient adherence.\n\n*Reflection:*\n\"What barriers might interfere with existing intervention strategies? How could these be effectively addressed?\"\n\n*Creative Perspective:*\n\"Is there an opportunity for innovative partnerships to facilitate access or compliance with interventions?\"\n\n[end sys2]\n\n[sys1]\nNext steps involve enhancing intervention accessibility and adherence, employing comprehensive research models like randomized control trials (RCTs).\nEfforts should also focus on psychosocial models to alleviate IBD-induced fatigue.\n[end sys1]\n\n**2.5 Evaluate Hypotheses:**\nEach hypothesis needs feasibility analysis, with an eye on risks and potential impacts, ensuring research focuses on models that integrate bio, psycho, and social factors.\n\n**2.6: Select the Best Hypothesis:**\nThe hypothesis concerning personalized medicine seems most promising, offering tailored approaches to a complex, interwoven issue. \n\n**2.7: Implement the Hypothesis:**\nEncourage steps towards personalized treatment regimens supported by a biopsychosocial health model. \n\n**2.8 Monitor and Review Progress:**\nConduct ongoing evaluations to adapt strategies as new data emerge from longitudinal RCTs. \n\n**2.9 Reflect and Capture Insights:**\nThe integration of holistic, personalized approaches in managing IBD-fatigue emerges as a key insight, providing a model for addressing chronic ailments in a structured, patient-centered manner.\n\nThis blend of System 1 and System 2 thinking creates a narrative that utilizes both quick observations and in-depth analysis to explore and address the complex issue of IBD-fatigue.","behavior":"degenerate_repetition","conversation_hash":"d730e6fca7d298adf101edbc3dfd4844","conversation_redacted":false,"conversation_toxic":false,"country":"United States","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"English","model":"gpt-4o-2024-08-06","n_chars":14864,"n_turns_in_conversation":1,"n_words":2025,"source_row_offset":30157,"source_shard":"turns-00044.parquet","timestamp":"Tue, 05 Nov 2024 14:26:27 GMT","turn_id":"87c97a6135536d1a3f939a42","turn_index":0,"user_text":"You are a helpful assistant generating synthetic data that captures *System 1* and *System 2* thinking, *creativity*, and *metacognitive reflection*. Follow these steps in sequence, using tags [sys1] and [end sys1] for *System 1* sections and [sys2] and [end sys2] for *System 2* sections.\n\n1. *Identify System 1 and System 2 Thinking Requirements:*\n   - Carefully read the text.\n   - Identify parts of the text that require quick, straightforward responses (*System 1*). Mark these sections with [sys1] and [end sys1].\n   - Identify parts that require in-depth, reflective thinking (*System 2*), marked with [sys2] and [end sys2].\n\n2. *Apply Step-by-Step Problem Solving with Creativity and Metacognitive Reflection for System 2 Sections:*\n\n   *2.1 Understand the Problem:*\n   - Objective: Fully comprehend the issue, constraints, and relevant context.\n   - Reflection: \"What do I understand about this issue? What might I be overlooking?\"\n   - Creative Perspective: Seek hidden patterns or possibilities that could reveal deeper insights or innovative connections.\n\n   *2.2 Analyze the Information:*\n   - Objective: Break down the problem logically.\n   - Reflection: \"Am I considering all factors? Are there any assumptions that need challenging?\"\n   - Creative Perspective: Explore unique patterns or overlooked relationships in the data that could add depth to the analysis.\n\n   *2.3 Generate Hypotheses:*\n   - Objective: Propose at least 10 hypotheses, each with a Confidence Score (0.0 to 1.0) and Creative Score (0.0 to 1.0), reflecting originality, surprise, and utility.\n   - Reflection: \"Have I explored all possible explanations or approaches, both conventional and unconventional?\"\n   - Creative Perspective: Consider novel angles that might provide unexpected insights.\n\n   *2.4 Anticipate Future Steps and Obstacles:*\n   - Objective: Make predictions, accounting for potential outcomes and obstacles.\n   - Reflection: \"What challenges might I face? Is my plan flexible for different scenarios?\"\n   - Creative Perspective: Visualize unforeseen outcomes and adapt plans to make use of them effectively.\n\n   *2.5 Evaluate Hypotheses:*\n   - Objective: Assess hypotheses based on feasibility, risk, and potential impact.\n   - Evaluation: Refine Confidence and Creative Scores as needed.\n   - Reflection: \"Am I unbiased in my assessment? Which options fit best with the overall objectives?\"\n   - Creative Perspective: Identify hidden opportunities or overlooked details in each hypothesis.\n\n   *2.6 Select the Best Hypothesis:*\n   - Objective: Choose the most promising, strategic hypothesis.\n   - Reflection: \"Why does this hypothesis stand out? How does it uniquely address the issue?\"\n   - Creative Perspective: Consider any underutilized potential in the selected approach.\n\n   *2.7 Implement the Hypothesis:*\n   - Objective: Outline actionable steps for testing the hypothesis.\n   - Reflection: \"Is this plan practical? What resources or preparation are required?\"\n   - Creative Perspective: Refine steps to maximize effectiveness and yield unexpected benefits.\n\n   *2.8 Monitor and Review Progress:*\n   - Objective: Review progress, noting areas for improvement.\n   - Reflection: \"What\u2019s working well? What could be improved?\"\n   - Creative Perspective: Look for emerging patterns that could refine future approaches.\n\n   *2.9 Reflect and Capture Insights:*\n   - Objective: Summarize lessons learned and insights gained for future reference.\n   - Reflection: \"What new understanding has emerged from this process?\"\n   - Creative Perspective: Identify innovative insights or patterns that could be applied to similar challenges.\n\n3. *Generate Text Output with Interleaved System 1 and System 2 Responses:*\n   - Use the tags [sys1] and [sys2] throughout.\n   - Aim for a lengthier, detailed response. Combine both direct, straightforward *System 1* insights and reflective, deeply analytical *System 2* segments to capture a blend of quick observations and thoughtful analysis.\n\n---\n\n### *Example Input Text:*\n\nYou are tasked with analyzing a sudden shift in customer preferences in a tech market. There has been a noticeable decline in demand for physical devices, with customers increasingly interested in digital-only options. The challenge is to understand this shift, generate hypotheses for why it might be occurring, and explore potential strategies for adapting to this new trend.\n\n---\n\n### *Processed Output with System 1 and System 2 Thinking:*\n\nThe tech market is experiencing a shift in customer demand, moving away from physical devices toward digital-only options.\n\n[sys1]\nCustomer demand has shifted from physical devices to digital-only options.\nWe need to understand why this change is happening and find strategies to address it.\nKey factors may include cost, convenience, and technological trends.\n[end sys1]\n\n[sys2]\n**2.1 Understand the Problem:**\nThe goal is to understand the underlying reasons for the shift in demand from physical devices to digital-only options, then develop a strategy to adapt.\n\n*Reflection:* \n\"I understand that preferences are changing, but what might be driving this? Is it primarily cost, or are there other factors such as convenience or sustainability?\"\n\n*Creative Perspective:* \n\"Could there be a larger trend in digital minimalism or a preference for eco-friendly solutions that we\u2019re missing? What unseen motivations might explain this shift?\"\n\n**2.2 Analyze the Information:**\nThere are multiple possible factors driving this shift, from economic influences to cultural shifts. It\u2019s essential to isolate each factor and understand its impact.\n\n*Reflection:* \n\"Am I fully considering the various economic and social influences? Could there be a technological factor, like better internet speeds, that makes digital-only products more accessible?\"\n\n*Creative Perspective:* \n\"Are there patterns or trends in other markets that could shed light on this shift? Could this be part of a larger trend toward virtual experiences?\"\n\n**2.3 Generate Hypotheses:**\n1. Customers prefer digital options due to lower costs. (Confidence: 0.8, Creative: 0.4)\n2. There\u2019s a growing trend toward minimalism and reduced physical clutter. (Confidence: 0.7, Creative: 0.7)\n3. Digital products offer greater flexibility and ease of use. (Confidence: 0.6, Creative: 0.6)\n4. Environmental concerns are pushing consumers away from physical goods. (Confidence: 0.6, Creative: 0.8)\n5. Advances in tech make digital-only options more functional. (Confidence: 0.8, Creative: 0.5)\n6. Pandemic-era remote work increased demand for digital solutions. (Confidence: 0.7, Creative: 0.6)\n7. Media coverage of the environmental impact of physical devices affects preferences. (Confidence: 0.5, Creative: 0.7)\n8. There\u2019s an increase in global digital literacy, expanding market access. (Confidence: 0.6, Creative: 0.6)\n9. Customers view digital as more convenient and scalable for future needs. (Confidence: 0.7, Creative: 0.5)\n10. Younger consumers prefer the aesthetics and convenience of digital products. (Confidence: 0.6, Creative: 0.6)\n\n*Reflection:* \n\"Have I considered all possible influences? Are there any surprising factors that could explain this shift?\"\n\n*Creative Perspective:* \n\"Could specific social trends, like the rise of influencer culture or digital-first lifestyles, be influencing customer choices?\"\n\n**2.4 Anticipate Future Steps and Obstacles:**\n*Objective:* Anticipate possible challenges, such as resistance from segments still preferring physical products.\n\n*Reflection:* \n\"What market obstacles might we face if we shift our focus to digital-only? Are there sub-segments that still prioritize physical products?\"\n\n*Creative Perspective:* \n\"Could expanding digital options help us reach a more global audience? Are there emerging trends that we could leverage in our strategy?\"\n\n[end sys2]\n\n[sys1]\nTo address this shift, consider a strategy that incorporates both digital-only offerings and educational campaigns about the benefits of digital solutions.\nUse insights from customer feedback and current trends to guide product development.\nFocus on flexibility and adaptation to cater to different customer segments.\n[end sys1]\n\n\nTargets for Health Interventions for Inflammatory Bowel Disease-fatigue.\nFatigue is a complex, multifactorial, and multidimensional phenomenon. Recognition of modifiable correlates of fatigue can provide a further understanding of this phenomenon in patients with inflammatory bowel disease [IBD] and aid in the development of interventions tailored towards fatigue with potential for efficacy. Our aims were to systematically search and synthesise available evidence on potentially modifiable factors contributing to IBD-fatigue and what advances in the management of fatigue in individuals with IBD have been made. The process of selection of citations was based on an earlier review by Czuber-Dochan et al. [2013] and was undertaken in two phases: i] searching for new studies published since August 2012, using seven electronic databases; ii] re-selection of papers included in previous review according to the aims of the current review. A total of 43 studies met the inclusion criteria. IBD-fatigue was consistently associated with disease activity, depression, anxiety, and sleep difficulties. However, most studies were cross-sectional; thus the direction of causation remains unknown. The relationship between biochemical factors, such as anaemia and inflammation, and fatigue was inconsistent. Solution-focused therapy, thiamine, and exercise showed promising effects on IBD-fatigue. Interventions continue to be sparse, with methodological limitations and only short-term effects reported. The review identified a number of psychosocial and physical factors which could potentially be modified through targeted health interventions and improve fatigue in IBD. Research utilising prospective observational studies and randomized control trial [RCT] design is required to develop and test interventions to reduce fatigue, most likely within a biopsychosocial model of care."},{"assistant_initiates":false,"asst_text":"```json\n{\n    \"result\": 0.3\n}\n```","behavior":"degenerate_repetition","conversation_hash":"5665b07c5fe26c8f7be258cc57c712ed","conversation_redacted":false,"conversation_toxic":false,"country":"Taiwan","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"Spanish","model":"gpt-4o-2024-08-06","n_chars":2430,"n_turns_in_conversation":1,"n_words":363,"source_row_offset":30158,"source_shard":"turns-00044.parquet","timestamp":"Tue, 05 Nov 2024 14:26:27 GMT","turn_id":"436b2582acafd6df4c7f55bb","turn_index":0,"user_text":"You are a JSON assistant. You only reply in valid JSON and never in normal text. Proporcione una calificaci\ufffdn de dificultad para la informaci\ufffdn del video detallada a continuaci\ufffdn. Me gustar\ufffda una \ufffdnica propiedad llamada \"result\" que contenga un valor flotante entre 0 y 1.\nLas categor\ufffdas de puntuaci\ufffdn de dificultad son las siguientes:\n0.0 - S\ufffdper Principiante: El material presenta vocabulario muy b\ufffdsico y frases simples, acompa\ufffdadas de visuales y contexto claros. Esto es perfecto para individuos sin conocimientos del idioma objetivo.\n0.3 - Principiante: El contenido consiste en oraciones simples y vocabulario familiar. Puede ofrecer alg\ufffdn apoyo visual y contexto para ayudar a la comprensi\ufffdn.\n0.5 - Intermedio: Este nivel incluye oraciones m\ufffds complejas y una gama m\ufffds amplia de vocabulario. Pueden estar presentes algunas frases idiom\ufffdticas, lo que puede requerir un poco m\ufffds de conocimiento previo.\n0.6 - Superior Intermedio: El material incorpora vocabulario especializado y conceptos relevantes para campos espec\ufffdficos. Los espectadores deben poseer una comprensi\ufffdn s\ufffdlida del idioma objetivo para una comprensi\ufffdn completa.\n0.8 - Avanzado: El video emplea vocabulario sofisticado y formas de oraciones complejas. Puede presentar discusiones matizadas que exigen un alto nivel de competencia en el idioma.\n1 - Muy Avanzado: El contenido est\ufffd dirigido a hablantes fluidos, utilizando t\ufffdrminos y conceptos especializados que pueden no ser bien conocidos incluso por todos los hablantes nativos.\n Language: es\nDescription: Hola Flappy Qu\u00e9 bueno verte otra [M\u00fasica] vez tu mam\u00e1 es muy [M\u00fasica] agradable polic\u00eda no puede ser Oh\nTitle: Todas las se\u00f1ales te llevan al mejor d\u00eda de tu vida \ud83e\udd8b | Bluey Espa\u00f1ol Canal Oficial\nCaption: Bluey es una adorable e incansable perrita pastor granadero australiano que vive con su mam\u00e1, su pap\u00e1 y su hermanita Bingo. \u00a1Bluey se vale de su energ\u00eda y su imaginaci\u00f3n sin l\u00edmites para descubrir, re\u00edr y jugar con todos sus amigos y su familia! \u00a1Acompa\u00f1a a Bluey, Bingo, Bandit y Chilli en todas sus aventuras! \n\nCreado por Ludo Studio \u00a9 Ludo Studio \ud83d\udc3e \nSuscr\u00edbete para saber lo \u00faltimo de Bluey \ud83d\udc99: http://bit.ly/SubscribeToBluey \n\ud83d\udda5 P\u00e1gina web: https://www.bluey.tv \n\ud83d\udc14 Twitter: https://twitter.com/officialblueytv \n\ud83d\udc36 Instagram: http://instagram.com/officialblueytv \n\ud83d\udc0a Facebook: https://www.facebook.com/OfficialBlueyTV \n\nEste es un canal comercial de BBC Studios."},{"assistant_initiates":false,"asst_text":"Certainly! I've reviewed your `portfolio_optimizer.py` code and identified sections that can be streamlined by removing redundant imports and eliminating unused functions. Below are the specific changes along with clear instructions to help you update your code seamlessly.\n\n---\n\n### 1. **Remove Duplicate `datetime` Imports**\n\n#### **Updated Code:**\n```python\nfrom datetime import datetime, timedelta\n```\n\n#### **Instructions:**\n1. **Open the File:**\n   - Navigate to your project directory and open `portfolio_optimizer.py` in your preferred code editor.\n\n2. **Locate the Duplicate Imports:**\n   - At the very top of the file, you'll find the following two lines:\n     ```python\n     import datetime\n     from datetime import datetime, timedelta\n     ```\n   \n3. **Replace the Imports:**\n   - **Delete** both lines mentioned above.\n   - **Insert** the updated single import line in their place:\n     ```python\n     from datetime import datetime, timedelta\n     ```\n\n4. **Save the File:**\n   - Ensure you save `portfolio_optimizer.py` after making the changes.\n\n---\n\n### 2. **Eliminate Unused Withdrawal Rate Functions**\n\n#### **Updated Code:**\n*These functions are **completely removed** from the codebase.*\n\n#### **Instructions:**\n1. **Open the File:**\n   - Ensure `portfolio_optimizer.py` is open in your code editor.\n\n2. **Find the Unused Functions:**\n   - Search for the following function definitions in the file:\n     ```python\n     @handle_exceptions\n     def calculate_safe_withdrawal_rate(returns: pd.Series) -> float:\n         \"\"\"\n         Calculate the Safe Withdrawal Rate based on portfolio returns.\n         \n         Parameters:\n         - returns (pd.Series): Daily returns of the portfolio.\n         \n         Returns:\n         - float: Safe Withdrawal Rate percentage.\n         \"\"\"\n         if returns.empty:\n             return np.nan\n         return (returns.mean() / returns.std()) * 100\n     \n     \n     def calculate_perpetual_withdrawal_rate(returns: pd.Series) -> float:\n         \"\"\"\n         Calculate the Perpetual Withdrawal Rate based on portfolio returns.\n         \n         Parameters:\n         - returns (pd.Series): Daily returns of the portfolio.\n         \n         Returns:\n         - float: Perpetual Withdrawal Rate percentage.\n         \"\"\"\n         if returns.empty:\n             return np.nan\n         return (returns.mean() / returns.std()) * 100\n     ```\n\n3. **Delete the Entire Function Definitions:**\n   - **Select and remove** both the `calculate_safe_withdrawal_rate` and `calculate_perpetual_withdrawal_rate` functions entirely from the code. Ensure you remove:\n     - The decorator `@handle_exceptions` if it's only associated with these functions.\n     - The function signature (e.g., `def calculate_safe_withdrawal_rate...`)\n     - The entire docstring and body of each function.\n\n4. **Verify No Dependencies Exist:**\n   - After deletion, ensure that no other parts of your code are calling these functions. Given that they were identified as unused, there should be no trace of their usage.\n\n5. **Save the File:**\n   - After making the deletions, save `portfolio_optimizer.py`.\n\n---\n\n### 3. **Optional: Clean Up Additional Redundant Imports (Advanced)**\n\n*While not strictly necessary based on the initial review, you might consider checking for other redundant or unused imports to further optimize your code.*\n\n#### **Steps:**\n1. **Review Remaining Imports:**\n   - Go through each import statement at the top of `portfolio_optimizer.py` and verify if they are being used anywhere in the code.\n   \n2. **Remove Unused Imports:**\n   - If you identify any imports that are not utilized in the script, remove them to clean up the codebase.\n\n3. **Example:**\n   - If you find an import like `import math` but there's no usage of `math` functions in the code, you can safely delete that line.\n\n4. **Save After Cleanup:**\n   - After removing any additional unused imports, save the file to ensure changes take effect.\n\n---\n\nBy implementing these changes, your `portfolio_optimizer.py` should be more efficient, easier to maintain, and free from unnecessary code clutter. This not only enhances readability but can also contribute to better performance during execution.\n\nIf you need further assistance or have additional sections you'd like to review, feel free to ask!","behavior":"degenerate_repetition","conversation_hash":"3c6e90b5da35c9177096d9bff0982217","conversation_redacted":false,"conversation_toxic":false,"country":"Hong Kong","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"English","model":"o1-mini-2024-09-12","n_chars":201993,"n_turns_in_conversation":1,"n_words":16293,"source_row_offset":30159,"source_shard":"turns-00044.parquet","timestamp":"Tue, 05 Nov 2024 14:26:28 GMT","turn_id":"165bec0597d2f705e6b9d6b9","turn_index":0,"user_text":"problem: remove useless code/dead code/redundant code etc etc\nPlease review my code and identify specific sections that need editing. For each section, provide the updated, full version of the code that I can directly use to replace the old version. I don't need to see the original code\u2014only the revised part.\nAdditionally, include clear, beginner-friendly instructions for replacing each section. Focus on where to find the code in my project files, any specific lines or keywords to look for, and exactly how to insert the new code. Keep it concise. Thank you!\n\n%%writefile portfolio_optimizer.py\nimport datetime\nfrom datetime import datetime, timedelta\nfrom functools import wraps\nfrom typing import Optional, Union, List, Dict, Tuple\n\nimport numpy as np\nimport pandas as pd\nimport plotly.express as px\nimport plotly.graph_objects as go\nimport requests\nimport streamlit as st\nimport yfinance as yf\nfrom joblib import Parallel, delayed\nfrom pandas_datareader import data as pdr\nfrom scipy.optimize import minimize\nfrom scipy.stats import skew, kurtosis\nfrom statsmodels.api import OLS\nfrom urllib3.util.retry import Retry\nfrom requests.adapters import HTTPAdapter\nfrom requests.packages.urllib3.exceptions import InsecureRequestWarning\n\nimport warnings\n\n# Suppress specific warnings\nwarnings.simplefilter('ignore', InsecureRequestWarning)\nrequests.packages.urllib3.disable_warnings()  # Use cautiously\n\n# ----------------------------\n# Frequency Mapping\n# ----------------------------\nFREQUENCY_MAPPING = {\n    \"Daily\": \"D\",\n    \"Weekly\": \"W\",\n    \"Monthly\": \"M\",\n    \"Quarterly\": \"Q\",\n    \"Yearly\": \"Y\"\n}\n\n# Inverse mapping to get frequency name from code\nrebalance_freq_inverse_mapping = {v: k for k, v in FREQUENCY_MAPPING.items()}\n\n# ----------------------------\n# Global Theme Configuration\n# ----------------------------\nTHEME = 'plotly_dark'  # Options: 'plotly_dark', 'plotly_white', 'seaborn', etc.\n\n# ----------------------------\n# Global Plot Layout Configuration\n# ----------------------------\nFIG_HEIGHT = 600\nHOVER_MODE = 'x unified'\n\n# ----------------------------\n# Example Portfolios Configuration\n# ----------------------------\n\ndef create_example_portfolios() -> list:\n    \"\"\"\n    Creates a list of predefined example portfolios without benchmark symbols.\n    \n    Returns:\n        list: A list of dictionaries, each representing a portfolio.\n    \"\"\"\n    today = datetime.today()\n    today_pd = pd.to_datetime(today)\n    example_definitions = [\n        {\n            'name': \"Retirement Portfolio\",\n            'years': 10,\n            'rebalance_freq': 'M',\n            'selected': ['AAPL', 'MSFT', 'GOOGL', 'JPM', 'XOM'],\n            'allocations': [20.0, 20.0, 20.0, 20.0, 20.0]\n        },\n        {\n            'name': \"Tech-Heavy Portfolio\",\n            'years': 5,\n            'rebalance_freq': 'M',\n            'selected': ['AAPL', 'MSFT', 'GOOGL', 'AMZN', 'META'],\n            'allocations': [25.0, 25.0, 20.0, 15.0, 15.0]\n        },\n        {\n            'name': \"Balanced Risk-Return Portfolio\",\n            'years': 7,\n            'rebalance_freq': 'M',\n            'selected': ['AAPL', 'JNJ', 'V', 'PG', 'XOM'],\n            'allocations': [20.0, 20.0, 20.0, 20.0, 20.0]\n        },\n        {\n            'name': \"Income-Focused Portfolio\",\n            'years': 7,\n            'rebalance_freq': 'Q',\n            'selected': ['PG', 'KO', 'JNJ', 'T', 'PFE'],\n            'allocations': [20.0, 20.0, 20.0, 20.0, 20.0]\n        },\n        {\n            'name': \"Growth-Oriented Portfolio\",\n            'years': 3,\n            'rebalance_freq': 'M',\n            'selected': ['TSLA', 'NVDA', 'AMD', 'META', 'NFLX'],\n            'allocations': [30.0, 20.0, 20.0, 15.0, 15.0]\n        },\n        {\n            'name': \"Value Portfolio\",\n            'years': 10,\n            'rebalance_freq': 'Q',\n            'selected': ['KO', 'PFE', 'XOM', 'WMT', 'CVX'],\n            'allocations': [20.0, 20.0, 20.0, 20.0, 20.0]\n        },\n        {\n            'name': \"Dividend-Focused Portfolio\",\n            'years': 7,\n            'rebalance_freq': 'Q',\n            'selected': ['T', 'VZ', 'PFE', 'KO', 'IBM'],\n            'allocations': [20.0, 20.0, 20.0, 20.0, 20.0]\n        },\n        {\n            'name': \"Global Diversification Portfolio\",\n            'years': 5,\n            'rebalance_freq': 'M',\n            'selected': ['AAPL', 'TSM', 'BABA', 'SAP', 'UL'],\n            'allocations': [20.0, 20.0, 20.0, 20.0, 20.0]\n        },\n        {\n            'name': \"High-Risk Growth Portfolio\",\n            'years': 3,\n            'rebalance_freq': 'M',\n            'selected': ['TSLA', 'ARKK', 'NVDA', 'SHOP', 'CRWD'],\n            'allocations': [30.0, 20.0, 20.0, 15.0, 15.0]\n        },\n        {\n            'name': \"Emerging Markets Portfolio\",\n            'years': 5,\n            'rebalance_freq': 'M',\n            'selected': ['BABA', 'TSM', 'PDD', 'INFY', 'VALE'],\n            'allocations': [25.0, 25.0, 20.0, 15.0, 15.0]\n        }\n    ]\n    \n    portfolios = []\n    for port_def in example_definitions:\n        portfolio = {\n            'name': port_def['name'],\n            'start_date': today - timedelta(days=365 * port_def['years']),\n            'end_date': today,\n            'rf_rate': 0.02,\n            'broker_fee': 0.0,\n            'rebalance_freq': port_def['rebalance_freq'],\n            'selected': port_def['selected'],\n            'allocations': port_def['allocations']\n        }\n        portfolios.append(portfolio)\n    \n    return portfolios\n\n# Initialize example portfolios if not already in session state\nif 'portfolios' not in st.session_state or not st.session_state.portfolios:\n    st.session_state.portfolios = create_example_portfolios()\n\n# ----------------------------\n# Session State Initialization\n# ----------------------------\n\ndef initialize_session_state():\n    \"\"\"\n    Initializes Streamlit session state with default values.\n    \"\"\"\n    # Initialize example portfolios\n    st.session_state.setdefault('portfolios', create_example_portfolios())\n    \n    # Other session state variables\n    st.session_state.setdefault('backtest_results', {})\n    st.session_state.setdefault('step', \"Configure Portfolio\")\n    st.session_state.setdefault('edit_portfolio', None)\n    st.session_state.setdefault('add_new_portfolio', False)\n    st.session_state.setdefault('default_config', {\n        'rf_rate': 0.02,\n        'broker_fee': 0.0\n    })\n    st.session_state.setdefault('show_proceed', False)\n    st.session_state.setdefault('selected_time_frames', ['Annually'])\n    st.session_state.setdefault('selected_rolling_periods', [252])\n\n# Initialize session state\ninitialize_session_state()\n# ----------------------------\n# Utility Decorators\n# ----------------------------\n\ndef handle_exceptions(func):\n    \"\"\"\n    Decorator to handle exceptions in Streamlit applications.\n    Displays errors using Streamlit's error message system.\n    \"\"\"\n    @wraps(func)\n    def wrapper(*args, **kwargs):\n        try:\n            return func(*args, **kwargs)\n        except Exception as e:\n            st.error(f\"Error in {func.__name__}: {e}\")\n            return None\n    return wrapper\n\n# ----------------------------\n# Helper Functions\n# ----------------------------\n\n@handle_exceptions\ndef get_company_name(ticker_df: pd.DataFrame, ticker: str) -> str:\n    \"\"\"\n    Retrieve the company name for a given ticker.\n\n    Parameters:\n    - ticker_df (pd.DataFrame): DataFrame containing 'Ticker' and 'Company Name' columns.\n    - ticker (str): Stock ticker symbol.\n\n    Returns:\n    - str: Company name or \"Unknown\" if not found.\n    \"\"\"\n    match = ticker_df[ticker_df['Ticker'] == ticker]\n    if not match.empty:\n        return match.iloc[0]['Company Name']\n    return \"Unknown\"\n\ndef format_asset_option(ticker: str, company_name: str) -> str:\n    \"\"\"\n    Format the asset option display string.\n\n    Parameters:\n    - ticker (str): Stock ticker symbol.\n    - company_name (str): Name of the company.\n\n    Returns:\n    - str: Formatted string combining ticker and company name.\n    \"\"\"\n    return f\"{ticker} - {company_name}\"\n\ndef handle_portfolio_data(portfolio, date_range=None):\n    \"\"\"\n    Helper function to download data and handle missing tickers.\n\n    Parameters:\n    - portfolio (dict): Portfolio details.\n    - date_range (tuple, optional): (start_date, end_date)\n\n    Returns:\n    - available_selected (list): Tickers with available data.\n    - missing_selected (set): Tickers missing data.\n    - price_data (pd.DataFrame): Adjusted close prices.\n    \"\"\"\n    start_date = date_range[0] if date_range else portfolio['start_date']\n    end_date = date_range[1] if date_range else portfolio['end_date']\n    price_data = download_data(portfolio['selected'], start_date, end_date)\n    if price_data.empty:\n        return [], set(portfolio['selected']), price_data\n    available_selected = [ticker for ticker in portfolio['selected'] if ticker in price_data.columns]\n    missing_selected = set(portfolio['selected']) - set(available_selected)\n    if missing_selected:\n        st.warning(f\"Excluded tickers with no data: {', '.join(missing_selected)}\")\n    return available_selected, missing_selected, price_data\n\ndef adjust_allocations(allocations: list) -> list:\n    \"\"\"\n    Adjusts portfolio allocations to ensure they sum to 100%.\n    \n    Parameters:\n    - allocations (list): List of allocation percentages\n    \n    Returns:\n    - list: Adjusted allocation percentages that sum to 100%\n    \"\"\"\n    if not allocations:\n        return []\n        \n    total = sum(allocations)\n    if total == 0:\n        return [0] * len(allocations)\n        \n    # Normalize allocations to sum to 100%\n    return [alloc / total * 100 for alloc in allocations]\n\n@handle_exceptions\ndef run_portfolio_comparison(comparison_type, portfolio1, portfolio2=None):\n    \"\"\"\n    Function to run portfolio comparisons (Portfolio vs Portfolio).\n    \n    Parameters:\n        comparison_type (str): Should always be \"vs_portfolio\".\n        portfolio1 (dict): First portfolio.\n        portfolio2 (dict, optional): Second portfolio for comparison.\n    \n    Returns:\n        dict: Backtest results or error message.\n    \"\"\"\n    try:\n        if comparison_type == \"vs_portfolio\":\n            # Ensure both portfolios are provided\n            if not portfolio1 or not portfolio2:\n                return {\"error\": \"Both portfolios must be provided for comparison.\"}\n            \n            # Determine overlapping date range between the two portfolios\n            overlap_start = max(portfolio1['start_date'], portfolio2['start_date'])\n            overlap_end = min(portfolio1['end_date'], portfolio2['end_date'])\n            if overlap_start >= overlap_end:\n                return {\"error\": \"No overlapping date ranges between the selected portfolios.\"}\n            \n            # Handle data for both portfolios within the overlapping date range\n            available_a, missing_a, price_data_a = handle_portfolio_data(portfolio1, (overlap_start, overlap_end))\n            available_b, missing_b, price_data_b = handle_portfolio_data(portfolio2, (overlap_start, overlap_end))\n            if not available_a or not available_b:\n                return {\"error\": \"One or both portfolios have no valid tickers with available data.\"}\n            \n            # Adjust allocations based on available tickers for both portfolios\n            allocations_a = adjust_allocations([portfolio1['allocations'][portfolio1['selected'].index(t)] for t in available_a])\n            allocations_b = adjust_allocations([portfolio2['allocations'][portfolio2['selected'].index(t)] for t in available_b])\n            \n            # Perform backtest for both portfolios\n            returns_a, cum_returns_a = backtest(\n                weights=np.array(allocations_a) / 100,\n                prices=price_data_a[available_a],\n                rebalance_freq=portfolio1['rebalance_freq'],\n                broker_fee=portfolio1['broker_fee']\n            )\n            returns_b, cum_returns_b = backtest(\n                weights=np.array(allocations_b) / 100,\n                prices=price_data_b[available_b],\n                rebalance_freq=portfolio2['rebalance_freq'],\n                broker_fee=portfolio2['broker_fee']\n            )\n            if cum_returns_a.empty or cum_returns_b.empty:\n                return {\"error\": \"One of the cumulative returns is empty.\"}\n            \n            # Calculate metrics for both portfolios\n            metrics_a = calculate_metrics(returns_a, cum_returns_a, portfolio1['rf_rate'])\n            metrics_b = calculate_metrics(returns_b, cum_returns_b, portfolio2['rf_rate'])\n            \n            # Align two portfolios' returns\n            common_index = cum_returns_a.index.intersection(cum_returns_b.index)\n            if common_index.empty:\n                return {\"error\": \"No overlapping dates after backtesting.\"}\n            returns_a = returns_a.loc[common_index]\n            returns_b = returns_b.loc[common_index]\n            cum_returns_a = cum_returns_a.loc[common_index]\n            cum_returns_b = cum_returns_b.loc[common_index]\n            \n            # Compile backtest results\n            backtest_results = {\n                'returns_a': returns_a,\n                'cum_returns_a': cum_returns_a,\n                'weights_a': allocations_a,\n                'price_data_a': price_data_a[available_a],\n                'metrics_a': metrics_a,\n                'returns_b': returns_b,\n                'cum_returns_b': cum_returns_b,\n                'weights_b': allocations_b,\n                'price_data_b': price_data_b[available_b],\n                'metrics_b': metrics_b\n            }\n            \n            return {\"backtest_results\": backtest_results}\n    except Exception as e:\n        return {\"error\": f\"An unexpected error occurred: {e}\"}\n\ndef process_portfolio_allocations(portfolio, price_data):\n    \"\"\"Processes portfolio allocations and handles missing tickers.\"\"\"\n    available = [ticker for ticker in portfolio['selected'] if ticker in price_data.columns]\n    missing = set(portfolio['selected']) - set(available)\n    if missing:\n        st.warning(f\"Excluded tickers from '{portfolio['name']}': {', '.join(missing)}\")\n    if not available:\n        st.error(f\"No valid tickers for portfolio '{portfolio['name']}' in the overlapping period.\")\n        st.stop()\n    allocations = [portfolio['allocations'][i] for i, ticker in enumerate(portfolio['selected']) if ticker in available]\n    allocations = adjust_allocations(allocations)\n    return allocations, available, missing\n\ndef create_new_portfolio():\n    \"\"\"Creates a new empty portfolio template with default values.\"\"\"\n    return {\n        'name': \"\",\n        'start_date': pd.to_datetime(datetime.today() - timedelta(days=365)),\n        'end_date': pd.to_datetime(datetime.today()),\n        'rf_rate': st.session_state.default_config['rf_rate'],\n        'broker_fee': st.session_state.default_config['broker_fee'],\n        'rebalance_freq': 'M',\n        'selected': [],\n        'allocations': []\n    }\n\ndef configure_portfolio(portfolio):\n    \"\"\"Handles the configuration of a portfolio.\"\"\"\n    col1, col2 = st.columns(2)\n    \n    with col1:\n        portfolio['name'] = st.text_input(\"Portfolio Name\", value=portfolio.get('name', ''))\n        portfolio['start_date'] = st.date_input(\n            \"Start Date\",\n            value=portfolio.get('start_date', pd.to_datetime(datetime.today() - timedelta(days=365)))\n        )\n        portfolio['rf_rate'] = st.number_input(\n            \"Risk-Free Rate (%)\",\n            value=portfolio.get('rf_rate', 0.02) * 100,\n            step=0.1\n        ) / 100\n        portfolio['rebalance_freq'] = st.selectbox(\n            \"Rebalancing Frequency\",\n            options=list(FREQUENCY_MAPPING.keys()),\n            index=list(FREQUENCY_MAPPING.values()).index(portfolio.get('rebalance_freq', 'M'))\n        )\n    \n    with col2:\n        portfolio['end_date'] = st.date_input(\n            \"End Date\",\n            value=portfolio.get('end_date', pd.to_datetime(datetime.today()))\n        )\n        portfolio['broker_fee'] = st.number_input(\n            \"Broker Fee (%)\",\n            value=portfolio.get('broker_fee', 0.0) * 100,\n            step=0.01\n        ) / 100\n    \n    return portfolio\n\ndef update_session_state(updates: dict):\n    \"\"\"\n    Helper function to update Streamlit session state.\n\n    Parameters:\n    - updates (dict): Dictionary of key-value pairs to update in session state.\n    \"\"\"\n    for key, value in updates.items():\n        st.session_state[key] = value\n\ndef calculate_diversification_index(risk_contributions: np.ndarray) -> float:\n    \"\"\"\n    Calculates the diversification index based on risk contributions.\n    \n    The diversification index ranges from 0 to 1:\n    - 1 indicates perfect diversification (equal risk contribution)\n    - 0 indicates complete concentration in one asset\n    \n    Parameters:\n    - risk_contributions (np.ndarray): Array of risk contributions in percentage\n    \n    Returns:\n    - float: Diversification index between 0 and 1\n    \"\"\"\n    # Convert percentages to decimals if needed\n    rc = np.array(risk_contributions) / 100 if np.any(risk_contributions > 1) else np.array(risk_contributions)\n    \n    # Number of assets\n    n = len(rc)\n    if n == 0:\n        return 0.0\n    \n    # Perfect diversification would have equal risk contribution of 1/n\n    perfect_div = 1.0 / n\n    \n    # Calculate sum of squared deviations from perfect diversification\n    squared_deviations = np.sum((rc - perfect_div) ** 2)\n    \n    # Maximum possible squared deviation (when one asset has 100% risk contribution)\n    max_deviation = (n - 1) * perfect_div**2 + (1 - perfect_div)**2\n    \n    # Calculate diversification index\n    if max_deviation == 0:\n        return 1.0\n    \n    div_index = 1 - (squared_deviations / max_deviation)\n    \n    # Ensure the result is between 0 and 1\n    return max(0.0, min(1.0, div_index))\n\ndef highlight_better_performance(portfolio1, portfolio2):\n    \"\"\"\n    Creates a styling function for comparing two portfolios.\n    \n    Args:\n        portfolio1 (str): Name of the first portfolio\n        portfolio2 (str): Name of the second portfolio\n        \n    Returns:\n        function: A styling function that can be used with DataFrame.style.apply()\n    \"\"\"\n    def _style_row(row):\n        styles = [''] * len(row)\n        \n        # Check if both portfolios exist in the row\n        if portfolio1 in row.index and portfolio2 in row.index:\n            val1 = row[portfolio1]\n            val2 = row[portfolio2]\n            \n            # Convert string values to float if possible\n            try:\n                if isinstance(val1, str):\n                    val1 = float(str(val1).strip('%').strip('$').replace(',', ''))\n                if isinstance(val2, str):\n                    val2 = float(str(val2).strip('%').strip('$').replace(',', ''))\n                \n                # Only proceed if both values are numeric\n                if isinstance(val1, (int, float)) and isinstance(val2, (int, float)):\n                    metric_name = row.name\n                    # Determine if higher or lower is better based on the metric\n                    lower_is_better = any(keyword in metric_name for keyword in [\n                        'Drawdown', 'Deviation', 'Tracking Error', 'Downside Capture Ratio', 'R2'\n                    ])\n                    if lower_is_better:\n                        better = val1 < val2\n                    else:\n                        better = val1 > val2\n                    \n                    # Apply styling\n                    if better:\n                        styles[row.index.get_loc(portfolio1)] = 'background-color: #d4f7d4'  # Green\n                        styles[row.index.get_loc(portfolio2)] = 'background-color: #f7d4d4'  # Red\n                    else:\n                        styles[row.index.get_loc(portfolio1)] = 'background-color: #f7d4d4'  # Red\n                        styles[row.index.get_loc(portfolio2)] = 'background-color: #d4f7d4'  # Green\n            except (ValueError, TypeError):\n                pass  # Skip if conversion fails\n                \n        return styles\n    \n    return _style_row\n\ndef display_backtest_results(results, comparison_type, portfolio1_name, portfolio2_name=None):\n    if comparison_type == \"Portfolio vs Portfolio\":\n        display_portfolio_vs_portfolio(results, portfolio1_name, portfolio2_name)\n\n@handle_exceptions\ndef calculate_risk_contribution(returns: pd.DataFrame, weights: np.ndarray) -> np.ndarray:\n    \"\"\"\n    Calculates the risk contribution of each asset in the portfolio.\n    \n    Parameters:\n    - returns (pd.DataFrame): Asset returns DataFrame\n    - weights (np.ndarray): Asset weights in the portfolio\n    \n    Returns:\n    - np.ndarray: Risk contribution percentages for each asset\n    \"\"\"\n    # Ensure returns is a DataFrame\n    if isinstance(returns, pd.Series):\n        returns = pd.DataFrame(returns)\n    \n    # Calculate covariance matrix\n    cov_matrix = returns.cov().values\n    \n    # Ensure weights is a numpy array\n    weights = np.array(weights).flatten()\n    \n    # Calculate portfolio volatility\n    port_vol = np.sqrt(np.dot(weights.T, np.dot(cov_matrix, weights)))\n    \n    # Calculate marginal risk contribution\n    mrc = np.dot(cov_matrix, weights) / port_vol if port_vol > 0 else np.zeros_like(weights)\n    \n    # Calculate component risk contribution\n    rc = np.multiply(weights, mrc)\n    \n    # Normalize to get percentage contributions\n    total_rc = np.sum(np.abs(rc))  # Use absolute values for normalization\n    rc_pct = (rc / total_rc * 100) if total_rc > 0 else np.full_like(rc, 100 / len(rc))\n    \n    return rc_pct\n\ndef create_portfolio_comparison_summary(portfolio1_name: str, portfolio2_name: str, metrics_a: dict, metrics_b: dict):\n    \"\"\"\n    Creates a single, comprehensive summary for portfolio comparison with actionable insights.\n    \"\"\"\n    st.markdown(\"---\")  # Visual separator\n    \n    with st.expander(\"\ud83d\udccb Portfolio Comparison Insights\", expanded=True):\n        # Performance Comparison\n        st.markdown(\"### \ud83d\udcca Key Performance Comparison\")\n        \n        # Create two columns for side-by-side comparison\n        col1, col2 = st.columns(2)\n        \n        metrics_to_compare = {\n            'Returns': 'Annualized Return (CAGR)',\n            'Risk': 'Standard Deviation (Annualized)',\n            'Risk-Adjusted': 'Sharpe Ratio',\n            'Maximum Loss': 'Maximum Drawdown'\n        }\n        \n        with col1:\n            st.markdown(f\"**{portfolio1_name}**\")\n            for label, metric in metrics_to_compare.items():\n                value = metrics_a.get(metric, \"N/A\")\n                st.metric(label, value)\n                \n        with col2:\n            st.markdown(f\"**{portfolio2_name}**\")\n            for label, metric in metrics_to_compare.items():\n                value = metrics_b.get(metric, \"N/A\")\n                st.metric(label, value)\n        \n        # Key Insights\n        st.markdown(\"### \ud83d\udd0d Key Insights\")\n        \n        # Safely extract and compare metrics\n        def safe_extract(metric_dict, metric_name):\n            value = metric_dict.get(metric_name, 0)\n            if isinstance(value, str):\n                try:\n                    return float(value.strip('%'))\n                except AttributeError:\n                    return float(value)\n                except ValueError:\n                    st.warning(f\"\u26a0\ufe0f Unable to convert metric '{metric_name}' to float. Using 0.\")\n                    return 0.0\n            elif isinstance(value, (float, np.float64, int)):\n                return float(value)\n            else:\n                st.warning(f\"\u26a0\ufe0f Unexpected type for metric '{metric_name}'. Using 0.\")\n                return 0.0\n        \n        returns_a = safe_extract(metrics_a, 'Annualized Return (CAGR)')\n        returns_b = safe_extract(metrics_b, 'Annualized Return (CAGR)')\n        better_returns = portfolio1_name if returns_a > returns_b else portfolio2_name\n        \n        vol_a = safe_extract(metrics_a, 'Standard Deviation (Annualized)')\n        vol_b = safe_extract(metrics_b, 'Standard Deviation (Annualized)')\n        lower_risk = portfolio1_name if vol_a < vol_b else portfolio2_name\n        \n        sharpe_a = safe_extract(metrics_a, 'Sharpe Ratio')\n        sharpe_b = safe_extract(metrics_b, 'Sharpe Ratio')\n        better_sharpe = portfolio1_name if sharpe_a > sharpe_b else portfolio2_name\n        \n        # Develop insights based on metric comparisons\n        insights = [\n            f\"\ud83d\udcc8 **Returns:** {better_returns} shows stronger performance ({returns_a:.1%} vs {returns_b:.1%})\",\n            f\"\ud83d\udee1\ufe0f **Risk Profile:** {lower_risk} demonstrates lower volatility ({vol_a:.1%} vs {vol_b:.1%})\",\n            f\"\u2696\ufe0f **Sharpe Ratio:** {better_sharpe} has better risk-adjusted returns ({sharpe_a:.2f} vs {sharpe_b:.2f})\",\n            \"\ud83d\udcca **Diversification Analysis:** Consider increasing diversification if one portfolio is heavily concentrated in few assets.\"\n        ]\n        \n        for insight in insights:\n            st.markdown(insight)\n        \n        # Action Items\n        st.markdown(\"### \u26a1 Recommended Actions\")\n        actions = [\n            \"\ud83d\udd04 Consider rebalancing to optimize returns and manage risk.\",\n            \"\ud83d\udcc8 Review asset allocation to ensure diversification.\",\n            \"\ud83d\udee1\ufe0f Implement risk management strategies to mitigate potential losses.\",\n            \"\ud83d\udd0d Monitor benchmark performance to maintain alignment with investment goals.\"\n        ]\n        \n        for action in actions:\n            st.markdown(action)\n\n@handle_exceptions\ndef create_summary_recommendations(context, metrics=None):\n     \"\"\"\n     Creates context-specific summary and recommendations with enhanced UI/UX.\n     \n     Parameters:\n     - context (str): The tab context ('overview', 'comparison', 'risk', 'deep')\n     - metrics (dict): Optional metrics to base recommendations on\n     \"\"\"\n     st.markdown(\"---\")  # Visual separator\n     \n     # Create an expandable container for recommendations\n     with st.expander(\"\ud83d\udccb Summary & Recommendations\", expanded=True):\n         \n         summaries = {\n             'overview': {\n                 'title': \"\ud83d\udcc8 Portfolio Overview\",\n                 'sections': {\n                     'Summary': [\n                         \"\ud83c\udfaf **Portfolio Performance Overview**: A high-level view of your portfolio's performance.\",\n                         \"\ud83d\udcca **Key Metrics Analysis**: Examination of essential performance metrics.\",\n                         \"\ud83d\udcbc **Asset Allocation Review**: Assessment of how assets are distributed across different categories.\"\n                     ],\n                     'Key Recommendations': [\n                         \"\ud83d\udcc8 **Rebalancing**: Consider rebalancing if asset allocations have drifted significantly from targets.\",\n                         \"\ud83d\udd04 **Diversification**: Enhance diversification across sectors to mitigate unsystematic risks.\",\n                         \"\ud83d\udcb0 **Fee Analysis**: Analyze the impact of fees on overall returns and explore lower-cost alternatives.\",\n                         \"\u2696\ufe0f **Risk-Return Alignment**: Ensure that the portfolio's risk profile aligns with your investment objectives.\",\n                         \"\ud83d\udcc5 **Next Review**: Schedule the next portfolio review to stay on track with your financial goals.\"\n                     ]\n                 }\n             },\n             'comparison': {\n                 'title': \"\ud83d\udd0d Comparative Analysis\",\n                 'sections': {\n                     'Performance Review': [\n                         \"\ud83d\udcca **Relative Performance**: Analyzing how the portfolio performs against benchmarks.\",\n                         \"\u2696\ufe0f **Risk-Adjusted Returns**: Evaluating returns in the context of the risk taken.\",\n                         \"\ud83d\udd17 **Correlation Insights**: Understanding the relationship between portfolio and benchmark movements.\"\n                     ],\n                     'Strategic Actions': [\n                         \"\ud83c\udfaf **Performance Gaps**: Address areas where the portfolio underperforms benchmarks.\",\n                         \"\ud83d\udee1\ufe0f **Risk Management**: Optimize strategies to manage and mitigate risks effectively.\",\n                         \"\ud83d\udcc8 **Benchmark Tracking**: Improve alignment with benchmark indices for consistent performance.\",\n                         \"\ud83d\udcbc **Asset Allocation Review**: Reassess asset distribution to enhance performance.\",\n                         \"\u26a1 **Rebalancing Needs**: Consider rebalancing to maintain target allocations.\"\n                     ]\n                 }\n             },\n             'risk': {\n                 'title': \"\ud83d\udee1\ufe0f Risk Assessment\",\n                 'sections': {\n                     'Risk Analysis': [\n                         \"\ud83d\udcc9 **Value at Risk (VaR)**: Review potential losses in extreme scenarios.\",\n                         \"\ud83c\udfaf **Stress Testing**: Assess portfolio resilience under various economic conditions.\",\n                         \"\ud83d\udcca **Volatility Assessment**: Measure the portfolio's price fluctuations over time.\"\n                     ],\n                     'Risk Management': [\n                         \"\ud83d\udee1\ufe0f **Hedging Strategies**: Implement techniques to protect against adverse market movements.\",\n                         \"\u2696\ufe0f **Position Sizing**: Adjust the size of positions to manage exposure effectively.\",\n                         \"\ud83c\udfaf **Stop-Loss Levels**: Set thresholds to limit potential losses on investments.\",\n                         \"\ud83d\udcc8 **Risk-Return Optimization**: Balance risk-taking with expected returns for optimal performance.\",\n                         \"\ud83d\udd04 **Risk Tolerance Alignment**: Ensure that portfolio risk aligns with your personal or organizational risk tolerance.\"\n                     ]\n                 }\n             },\n             'deep': {\n                 'title': \"\ud83d\udd2c Deep Analysis\",\n                 'sections': {\n                     'Analysis Results': [\n                         \"\ud83d\udcca **Factor Analysis**: Insights into the factors driving portfolio performance.\",\n                         \"\ud83d\udcc8 **Attribution Results**: Breakdown of returns to identify sources of performance.\",\n                         \"\ud83d\udd0d **Advanced Metrics Review**: Examination of complex metrics for in-depth understanding.\"\n                     ],\n                     'Strategic Recommendations': [\n                         \"\ud83c\udfaf **Factor Optimization**: Adjust exposures to key factors for better performance.\",\n                         \"\ud83d\udcbc **Style Drift Management**: Address any unintended shifts in investment style.\",\n                         \"\ud83d\udcc8 **Portfolio Efficiency**: Enhance the efficiency of the portfolio to maximize returns.\",\n                         \"\u26a1 **Strategic Reallocations**: Make informed reallocations based on deep analysis.\",\n                         \"\ud83d\udd04 **Investment Strategy Review**: Regularly review and refine your investment strategy.\"\n                     ]\n                 }\n             }\n         }\n\n         if context in summaries:\n             current_summary = summaries[context]\n             \n             # Display title with icon\n             st.markdown(f\"### {current_summary['title']} \ud83d\udcd1\")\n             \n             # Create columns for sections\n             sections = current_summary['sections']\n             num_sections = len(sections)\n             cols = st.columns(num_sections)\n             \n             # Display sections in columns\n             for col, (section_title, items) in zip(cols, sections.items()):\n                 with col:\n                     st.markdown(f\"**{section_title}**\")\n                     for item in items:\n                         st.markdown(f\"- {item}\")\n             \n             # Add dynamic recommendations based on metrics\n             if metrics:\n                 st.markdown(\"## \ud83d\udee0\ufe0f Personalized Recommendations\")\n                 personalized_recs = []\n\n                 # Example: If Sharpe Ratio is low\n                 if 'Sharpe Ratio' in metrics and isinstance(metrics['Sharpe Ratio'], (int, float)):\n                     if metrics['Sharpe Ratio'] < 1:\n                         personalized_recs.append(\"\u2696\ufe0f **Improve Sharpe Ratio**: Consider strategies to enhance risk-adjusted returns, such as optimizing asset allocation or reducing high-risk investments.\")\n\n                 # Example: If Maximum Drawdown is high\n                 if 'Maximum Drawdown' in metrics and isinstance(metrics['Maximum Drawdown'], (int, float)):\n                     if metrics['Maximum Drawdown'] > -20:\n                         personalized_recs.append(\"\ud83d\udee1\ufe0f **Mitigate Drawdowns**: Implement hedging strategies or diversify into less volatile assets to reduce potential drawdowns.\")\n\n                 # Add more conditional recommendations as needed\n                 if personalized_recs:\n                     for rec in personalized_recs:\n                         st.markdown(f\"- {rec}\")\n                 else:\n                     st.markdown(\"- \ud83c\udf89 Your portfolio metrics are within healthy ranges. Keep up the good work!\")\n\n             # Add call to action section\n             st.markdown(\"---\")\n             st.markdown(\"### \u26a1 Next Steps\")\n             \n             next_steps = {\n                 \"\ud83d\udccc Priority Actions\": \"Review and implement the key recommendations listed above.\",\n                 \"\ud83d\uddd3\ufe0f Timeline\": \"Set specific deadlines for each action item to ensure timely execution.\",\n                 \"\ud83d\udd04 Follow-up\": \"Schedule a follow-up review to assess the impact of implemented changes.\",\n                 \"\ud83d\udcdd Documentation\": \"Record all actions taken and outcomes achieved for future reference.\"\n             }\n             \n             for step, description in next_steps.items():\n                 st.markdown(f\"**{step}:** {description}\")\n\ndef generate_portfolio_comparison_recommendations(portfolio1_name: str, portfolio2_name: str, metrics_a: dict, metrics_b: dict) -> list:\n    \"\"\"\n    Generate recommendations based on portfolio comparison analysis.\n    \n    Parameters:\n    - portfolio1_name (str): Name of first portfolio\n    - portfolio2_name (str): Name of second portfolio\n    - metrics_a (dict): Performance metrics for first portfolio\n    - metrics_b (dict): Performance metrics for second portfolio\n    \n    Returns:\n    - list: List of recommendation strings\n    \"\"\"\n    recommendations = []\n    \n    # Helper function to parse percentage strings\n    def parse_percentage(value):\n        if isinstance(value, str):\n            try:\n                return float(value.strip('%')) / 100\n            except (ValueError, AttributeError):\n                return 0.0\n        return float(value)\n    \n    try:\n        # Get full portfolio objects from session state\n        portfolio1 = next(p for p in st.session_state.portfolios if p['name'] == portfolio1_name)\n        portfolio2 = next(p for p in st.session_state.portfolios if p['name'] == portfolio2_name)\n        \n        # Compare returns\n        returns_a = parse_percentage(metrics_a.get('Annualized Return (CAGR)', 0))\n        returns_b = parse_percentage(metrics_b.get('Annualized Return (CAGR)', 0))\n        \n        if returns_a > returns_b:\n            recommendations.append(f\"\ud83d\udcc8 {portfolio1_name} shows higher returns ({returns_a:.1%} vs {returns_b:.1%})\")\n        else:\n            recommendations.append(f\"\ud83d\udcc8 {portfolio2_name} shows higher returns ({returns_b:.1%} vs {returns_a:.1%})\")\n        \n        # Compare risk metrics\n        vol_a = parse_percentage(metrics_a.get('Standard Deviation (Annualized)', 0))\n        vol_b = parse_percentage(metrics_b.get('Standard Deviation (Annualized)', 0))\n        \n        if vol_a < vol_b:\n            recommendations.append(f\"\ud83d\udcca {portfolio1_name} has lower volatility ({vol_a:.1%} vs {vol_b:.1%})\")\n        else:\n            recommendations.append(f\"\ud83d\udcca {portfolio2_name} has lower volatility ({vol_b:.1%} vs {vol_a:.1%})\")\n        \n        # Compare Sharpe ratios\n        sharpe_a = float(metrics_a.get('Sharpe Ratio', 0))\n        sharpe_b = float(metrics_b.get('Sharpe Ratio', 0))\n        \n        if sharpe_a > sharpe_b:\n            recommendations.append(f\"\u2696\ufe0f {portfolio1_name} has better risk-adjusted returns (Sharpe: {sharpe_a:.2f} vs {sharpe_b:.2f})\")\n        else:\n            recommendations.append(f\"\u2696\ufe0f {portfolio2_name} has better risk-adjusted returns (Sharpe: {sharpe_b:.2f} vs {sharpe_a:.2f})\")\n        \n        # Compare maximum drawdowns\n        dd_a = parse_percentage(metrics_a.get('Maximum Drawdown', 0))\n        dd_b = parse_percentage(metrics_b.get('Maximum Drawdown', 0))\n        \n        if dd_a < dd_b:\n            recommendations.append(f\"\ud83d\udd3b {portfolio1_name} has smaller maximum drawdown ({dd_a:.1%} vs {dd_b:.1%})\")\n        else:\n            recommendations.append(f\"\ud83d\udd3b {portfolio2_name} has smaller maximum drawdown ({dd_b:.1%} vs {dd_a:.1%})\")\n        \n        # Diversification comparison\n        div_rec = f\"\ud83d\udcca Diversification: {portfolio1_name} has {len(portfolio1['selected'])} assets vs \" \\\n                  f\"{portfolio2_name}'s {len(portfolio2['selected'])} assets\"\n        recommendations.append(div_rec)\n        \n        # Rebalancing frequency comparison\n        freq_1 = rebalance_freq_inverse_mapping.get(portfolio1['rebalance_freq'], portfolio1['rebalance_freq'])\n        freq_2 = rebalance_freq_inverse_mapping.get(portfolio2['rebalance_freq'], portfolio2['rebalance_freq'])\n        recommendations.append(f\"\ud83d\udd04 Rebalancing: {portfolio1_name} ({freq_1}) vs {portfolio2_name} ({freq_2})\")\n        \n    except Exception as e:\n        recommendations.append(f\"\u26a0\ufe0f Some metrics could not be compared: {str(e)}\")\n    \n    return recommendations\n\ndef display_portfolio_vs_portfolio(results, portfolio1_name, portfolio2_name):\n    \"\"\"Displays comparison between two portfolios with enhanced UI/UX.\"\"\"\n    # Extract data from results\n    returns_a = results.get('returns_a', pd.Series(dtype=float))\n    returns_b = results.get('returns_b', pd.Series(dtype=float))\n    cum_returns_a = results.get('cum_returns_a', pd.Series(dtype=float))\n    cum_returns_b = results.get('cum_returns_b', pd.Series(dtype=float))\n    weights_a = results.get('weights_a', [])\n    weights_b = results.get('weights_b', [])\n    price_data_a = results.get('price_data_a', pd.DataFrame())\n    price_data_b = results.get('price_data_b', pd.DataFrame())\n    metrics_a = results.get('metrics_a', {})\n    metrics_b = results.get('metrics_b', {})\n\n    # Define available_a and available_b based on price data columns\n    available_a = list(price_data_a.columns)\n    available_b = list(price_data_b.columns)\n\n    # Ensure that available_a and available_b are not empty\n    if not available_a:\n        st.error(f\"No available data for Portfolio '{portfolio1}'.\")\n        return\n    if not available_b:\n        st.error(f\"No available data for Portfolio '{portfolio2}'.\")\n        return\n\n    # Define a consistent theme for all plots\n    THEMES = {\n        'plotly_dark': 'plotly_dark',\n        'plotly_light': 'plotly_white'\n    }\n    selected_theme = THEMES.get('plotly_light')  # Change as needed\n\n    # Create tabs for better organization\n    overview_tab, comparison_tab, risk_tab, analysis_tab = st.tabs([\n        \"\ud83d\udcca Overview\", \"\ud83d\udd04 Comparison\", \"\ud83c\udfaf Risk Analysis\", \"\ud83d\udd0d Deep Analysis\"\n    ])\n\n    with overview_tab:\n        st.header(\"\ud83d\udd39 Portfolio Overview\")\n        col1, col2 = st.columns([1,1])\n        \n        with col1:\n            st.subheader(f\"{portfolio1} Composition\")\n            fig_comp_a = px.pie(\n                values=weights_a,\n                names=price_data_a.columns,\n                title=f\"{portfolio1} Allocation\",\n                template=selected_theme,\n                hole=0.4,\n                color_discrete_sequence=px.colors.sequential.Blues\n            )\n            fig_comp_a.update_traces(textinfo='percent+label')\n            st.plotly_chart(fig_comp_a, use_container_width=True)\n\n            # Key metrics for portfolio 1\n            metrics_display_a = {\n                \"Total Return\": f\"{(cum_returns_a.iloc[-1] - 1) * 100:.2f}%\",\n                \"Annualized Return\": metrics_a.get('Annualized Return (CAGR)', 'N/A'),\n                \"Sharpe Ratio\": metrics_a.get('Sharpe Ratio', 'N/A'),\n                \"Max Drawdown\": metrics_a.get('Maximum Drawdown', 'N/A')\n            }\n            st.subheader(f\"{portfolio1} Key Metrics\")\n            for metric, value in metrics_display_a.items():\n                st.metric(f\"{metric}\", value)\n\n        with col2:\n            st.subheader(f\"{portfolio2} Composition\")\n            fig_comp_b = px.pie(\n                values=weights_b,\n                names=price_data_b.columns,\n                title=f\"{portfolio2} Allocation\",\n                template=selected_theme,\n                hole=0.4,\n                color_discrete_sequence=px.colors.sequential.Oranges\n            )\n            fig_comp_b.update_traces(textinfo='percent+label')\n            st.plotly_chart(fig_comp_b, use_container_width=True)\n\n            # Key metrics for portfolio 2\n            metrics_display_b = {\n                \"Total Return\": f\"{(cum_returns_b.iloc[-1] - 1) * 100:.2f}%\",\n                \"Annualized Return\": metrics_b.get('Annualized Return (CAGR)', 'N/A'),\n                \"Sharpe Ratio\": metrics_b.get('Sharpe Ratio', 'N/A'),\n                \"Max Drawdown\": metrics_b.get('Maximum Drawdown', 'N/A')\n            }\n            st.subheader(f\"{portfolio2} Key Metrics\")\n            for metric, value in metrics_display_b.items():\n                st.metric(f\"{metric}\", value)\n\n        # Cumulative Returns Comparison with Interactive Features\n        st.subheader(\"Cumulative Returns Comparison\")\n        fig_returns = go.Figure()\n        fig_returns.add_trace(go.Scatter(\n            x=cum_returns_a.index,\n            y=cum_returns_a,\n            name=portfolio1,\n            line=dict(color='cyan', width=2),\n            hovertemplate='%{y:.2f}'\n        ))\n        fig_returns.add_trace(go.Scatter(\n            x=cum_returns_b.index,\n            y=cum_returns_b,\n            name=portfolio2,\n            line=dict(color='orange', width=2, dash='dash'),\n            hovertemplate='%{y:.2f}'\n        ))\n        fig_returns.update_layout(\n            template=selected_theme,\n            hovermode='x unified',\n            height=500,\n            xaxis_title='Date',\n            yaxis_title='Cumulative Returns',\n            legend=dict(x=0.01, y=0.99)\n        )\n        st.plotly_chart(fig_returns, use_container_width=True)\n\n        metrics_a = results.get('metrics_a', {})\n        metrics_b = results.get('metrics_b', {})\n\n        create_portfolio_comparison_summary(\n            portfolio1_name=portfolio1_name,\n            portfolio2_name=portfolio2_name,\n            metrics_a=metrics_a,\n            metrics_b=metrics_b\n        )\n\n    with comparison_tab:\n        st.header(\"\ud83d\udd04 Performance Metrics Comparison\")\n        st.subheader(\"Detailed Performance Metrics\")\n\n        detailed_metrics = [\n            'Annualized Return (CAGR)',\n            'Sharpe Ratio',\n            'Sortino Ratio',\n            'Maximum Drawdown',\n            'Beta',\n            'Alpha (annualized)',\n            'Information Ratio',\n            'Modigliani\u2013Modigliani Measure',\n            'Tracking Error',\n            'Calmar Ratio'\n        ]\n\n        metrics_comparison = pd.DataFrame({\n            'Metric': detailed_metrics,\n            portfolio1_name: [metrics_a.get(metric, \"N/A\") for metric in detailed_metrics],\n            portfolio2_name: [metrics_b.get(metric, \"N/A\") for metric in detailed_metrics]\n        })\n\n        # Apply styling\n        styled_comparison = metrics_comparison.style.apply(\n            highlight_better_performance(portfolio1_name, portfolio2_name),\n            axis=1\n        )\n\n        # Display the styled dataframe\n        st.dataframe(styled_comparison, height=400)\n\n        # Rolling Performance Difference with Interactive Slider\n        st.subheader(\"Rolling Performance Difference (30-Day MA)\")\n        window_size = st.slider(\"Select Rolling Window (Days)\", min_value=10, max_value=60, value=30)\n        rolling_diff = (returns_a - returns_b).rolling(window=window_size).mean() * 252\n\n        fig_diff = px.line(\n            rolling_diff,\n            title=f\"{window_size}-Day Rolling Return Difference\",\n            labels={\"index\": \"Date\", \"value\": \"Return Difference\"},\n            template=selected_theme\n        )\n        fig_diff.update_traces(line=dict(color='magenta'))\n        fig_diff.update_layout(\n            hovermode='x unified',\n            height=500,\n            xaxis_title='Date',\n            yaxis_title='Return Difference'\n        )\n        st.plotly_chart(fig_diff, use_container_width=True)\n\n    with risk_tab:\n        st.header(\"\ud83c\udfaf Risk Analysis\")\n        col1, col2 = st.columns([1,1])\n        \n        with col1:\n             # Rolling Volatility Comparison with Interactive Layers\n             st.subheader(\"\ud83d\udcc9 Rolling Volatility Comparison (30-Day)\")\n             rolling_vol_a = returns_a.rolling(window=30).std() * np.sqrt(252)\n             rolling_vol_b = returns_b.rolling(window=30).std() * np.sqrt(252)\n             \n             fig_vol = go.Figure()\n             fig_vol.add_trace(go.Scatter(\n                 x=rolling_vol_a.index,\n                 y=rolling_vol_a,\n                 name=f\"{portfolio1_name} Volatility\",\n                 line=dict(color='cyan', width=2)\n             ))\n             fig_vol.add_trace(go.Scatter(\n                 x=rolling_vol_b.index,\n                 y=rolling_vol_b,\n                 name=f\"{portfolio2_name} Volatility\",\n                 line=dict(color='orange', width=2, dash='dash')\n             ))\n             fig_vol.update_layout(\n                 template=selected_theme,\n                 hovermode='x unified',\n                 height=500,\n                 xaxis_title='Date',\n                 yaxis_title='Annualized Volatility (%)',\n                 legend=dict(x=0.01, y=0.99)\n             )\n             st.plotly_chart(fig_vol, use_container_width=True)\n\n        with col2:\n             # Rolling Correlation Comparison with Interactive Features\n             st.subheader(\"\ud83d\udd04 Rolling Correlation (30-Day) Between Portfolios\")\n             rolling_corr = returns_a.rolling(window=30).corr(returns_b)\n             \n             fig_corr = px.line(\n                 rolling_corr,\n                 title=\"\ud83d\udcc8 30-Day Rolling Correlation\",\n                 labels={\"index\": \"Date\", \"value\": \"Correlation\"},\n                 template=selected_theme\n             )\n             fig_corr.update_traces(line=dict(color='purple'))\n             fig_corr.update_layout(\n                 hovermode='x unified',\n                 height=500,\n                 xaxis_title='Date',\n                 yaxis_title='Correlation'\n             )\n             st.plotly_chart(fig_corr, use_container_width=True)\n\n        st.subheader(\"\ud83d\udcc9 Top 10 Drawdowns Comparison Between Portfolios\")\n        col3, col4 = st.columns([1,1])\n        \n        with col3:\n             # Get drawdown details\n             drawdowns_a = get_drawdown_details(cum_returns_a)\n             drawdowns_b = get_drawdown_details(cum_returns_b)\n \n             # Check if drawdown data is available\n             if drawdowns_a and drawdowns_b:\n                 # Convert to DataFrame and sort\n                 drawdowns_a_df = pd.DataFrame(drawdowns_a).sort_values(by='Drawdown', ascending=True).head(10)\n                 drawdowns_b_df = pd.DataFrame(drawdowns_b).sort_values(by='Drawdown', ascending=True).head(10)\n \n                 # Combine for comparison\n                 combined_drawdowns = pd.DataFrame({\n                     f\"{portfolio1_name} Date\": drawdowns_a_df['Date'],\n                     f\"{portfolio1_name} Drawdown (%)\": drawdowns_a_df['Drawdown'],\n                     f\"{portfolio2_name} Date\": drawdowns_b_df['Date'],\n                     f\"{portfolio2_name} Drawdown (%)\": drawdowns_b_df['Drawdown']\n                 })\n \n                 # Display the comparison table\n                 st.table(combined_drawdowns)\n \n                 # Enhanced Comparative Bar Chart\n                 fig_drawdown_comparison = go.Figure(data=[\n                     go.Bar(\n                         name=portfolio1_name,\n                         x=drawdowns_a_df['Date'],\n                         y=drawdowns_a_df['Drawdown'],\n                         marker_color='cyan',\n                         hovertemplate='%{y:.2f}% on %{x}<extra></extra>'\n                     ),\n                     go.Bar(\n                         name=portfolio2_name,\n                         x=drawdowns_b_df['Date'],\n                         y=drawdowns_b_df['Drawdown'],\n                         marker_color='orange',\n                         hovertemplate='%{y:.2f}% on %{x}<extra></extra>'\n                     )\n                 ])\n                 fig_drawdown_comparison.update_layout(\n                     barmode='group',\n                     title=\"\ud83d\udcca Top 10 Drawdowns Comparison\",\n                     xaxis_title='Date',\n                     yaxis_title='Drawdown (%)',\n                     template=selected_theme,\n                     legend=dict(x=0.01, y=0.99),\n                     height=600\n                 )\n                 st.plotly_chart(fig_drawdown_comparison, use_container_width=True)\n             else:\n                 st.write(\"\u26a0\ufe0f Insufficient drawdown data for comparison.\")\n\n    with analysis_tab:\n        st.header(\"\ud83d\udd0d Deep Analysis\")\n        col1, col2 = st.columns([1,1])\n        \n        with col1:\n            # Monthly Returns Heatmap for Portfolio1\n            st.subheader(f\"{portfolio1} Monthly Returns Heatmap\")\n            monthly_returns_a = returns_a.resample('M').agg(lambda x: (1 + x).prod() - 1)\n            monthly_matrix_a = monthly_returns_a.groupby([monthly_returns_a.index.year, monthly_returns_a.index.month]).first().unstack()\n            \n            fig_heat_a = px.imshow(\n                monthly_matrix_a,\n                labels=dict(x=\"Month\", y=\"Year\", color=\"Returns\"),\n                color_continuous_scale=\"RdYlGn\",\n                title=f\"{portfolio1} Monthly Returns Heatmap\",\n                template=selected_theme\n            )\n            fig_heat_a.update_layout(\n                xaxis_title='Month',\n                yaxis_title='Year',\n                height=500\n            )\n            st.plotly_chart(fig_heat_a, use_container_width=True)\n\n        with col2:\n            # Monthly Returns Heatmap for Portfolio2\n            st.subheader(f\"{portfolio2} Monthly Returns Heatmap\")\n            monthly_returns_b = returns_b.resample('M').agg(lambda x: (1 + x).prod() - 1)\n            monthly_matrix_b = monthly_returns_b.groupby([monthly_returns_b.index.year, monthly_returns_b.index.month]).first().unstack()\n            \n            fig_heat_b = px.imshow(\n                monthly_matrix_b,\n                labels=dict(x=\"Month\", y=\"Year\", color=\"Returns\"),\n                color_continuous_scale=\"RdYlGn\",\n                title=f\"{portfolio2} Monthly Returns Heatmap\",\n                template=selected_theme\n            )\n            fig_heat_b.update_layout(\n                xaxis_title='Month',\n                yaxis_title='Year',\n                height=500\n            )\n            st.plotly_chart(fig_heat_b, use_container_width=True)\n\n        # Risk-Return Scatter Plot with Interactive Annotations\n        st.subheader(\"\ud83d\udd25 Risk vs Return Scatter Plot\")\n        fig_risk_return = go.Figure()\n        fig_risk_return.add_trace(go.Scatter(\n            x=[returns_a.std() * np.sqrt(252)],\n            y=[returns_a.mean() * 252],\n            mode='markers+text',\n            marker=dict(\n                size=20,\n                color='cyan',\n                line=dict(width=2, color='DarkSlateGrey')\n            ),\n            text=[portfolio1],\n            textposition=\"top center\",\n            name=portfolio1\n        ))\n        fig_risk_return.add_trace(go.Scatter(\n            x=[returns_b.std() * np.sqrt(252)],\n            y=[returns_b.mean() * 252],\n            mode='markers+text',\n            marker=dict(\n                size=20,\n                color='orange',\n                line=dict(width=2, color='DarkSlateGrey')\n            ),\n            text=[portfolio2],\n            textposition=\"top center\",\n            name=portfolio2\n        ))\n        fig_risk_return.update_layout(\n            title='\ud83d\udd25 Risk vs Return Scatter Plot',\n            xaxis_title='Annualized Volatility (Std Dev)',\n            yaxis_title='Annualized Return',\n            template=selected_theme,\n            xaxis=dict(range=[0, max(returns_a.std(), returns_b.std()) * np.sqrt(252) * 1.2]),\n            yaxis=dict(range=[0, max(returns_a.mean(), returns_b.mean()) * 252 * 1.2]),\n            height=600\n        )\n        st.plotly_chart(fig_risk_return, use_container_width=True)\n\n    # Final Summary with Interactive Components\n    st.subheader(\"\ud83d\udcdd Summary and Recommendations\")\n    \n    # Calculate overall scores\n    score_a = calculate_final_score(metrics_a, metrics_b)\n    score_b = calculate_final_score(metrics_b, metrics_a)\n    \n    col7, col8 = st.columns([1,1])\n    with col7:\n        st.metric(f\"{portfolio1} Score\", f\"{score_a:.2f}/100\", help=\"Based on performance and risk metrics.\")\n    with col8:\n        st.metric(f\"{portfolio2} Score\", f\"{score_b:.2f}/100\", help=\"Based on performance and risk metrics.\")\n    \n    # Generate and display recommendations\n    recommendations = generate_portfolio_comparison_recommendations(\n        portfolio1_name=portfolio1,\n        portfolio2_name=portfolio2,\n        metrics_a=metrics_a,\n        metrics_b=metrics_b\n    )\n    for rec in recommendations:\n        st.markdown(f\"\u2022 {rec}\")\n\n    # Update session state\n    update_session_state({\n        'comparison_results': results,\n        'show_proceed': True\n    })\n    st.success(\"Portfolio comparison analysis completed! \ud83c\udf89\")\n\ndef create_advanced_metrics_df(metrics, benchmark_metrics):\n    \"\"\"Creates a DataFrame for advanced metrics.\"\"\"\n    return pd.DataFrame({\n        'Metric': list(metrics.keys()),\n        'Portfolio': list(metrics.values()),\n        'Benchmark': list(benchmark_metrics.values())\n    }).set_index('Metric')\n\n\ndef create_performance_stats_df_portfolio_vs_portfolio(metrics_a, metrics_b, portfolio1, portfolio2):\n    \"\"\"Creates a refined DataFrame for Portfolio vs Portfolio performance statistics.\"\"\"\n    metrics = [\n        'Annualized Return (CAGR)',\n        'Sharpe Ratio',\n        'Sortino Ratio',\n        'Maximum Drawdown',\n        'Beta',\n        'Alpha (annualized)',\n        'Information Ratio',\n        'R2',\n        'Calmar Ratio',\n        'Tracking Error',\n        'Upside Capture Ratio',\n        'Downside Capture Ratio',\n        'Best Year',\n        'Worst Year'\n    ]\n\n    data = {\n        'Metric': metrics,\n        portfolio1: [metrics_a.get(metric, \"N/A\") for metric in metrics],\n        portfolio2: [metrics_b.get(metric, \"N/A\") for metric in metrics]\n    }\n\n    performance_df = pd.DataFrame(data)\n    performance_df.set_index('Metric', inplace=True)\n\n    # Replace any missing or \"N/A\" with \"N/A\" explicitly (optional, for consistency)\n    performance_df.replace({np.nan: \"N/A\"}, inplace=True)\n\n    return performance_df\n\ndef create_advanced_metrics_df_portfolio_vs_portfolio(metrics_a, metrics_b, portfolio1, portfolio2):\n    \"\"\"Creates a refined DataFrame for Portfolio vs Portfolio advanced metrics.\"\"\"\n    metrics = [\n        'Beta',\n        'Alpha (annualized)',\n        'Information Ratio',\n        'Modigliani\u2013Modigliani Measure',\n        'Tracking Error',\n        'Upside Capture Ratio',\n        'Downside Capture Ratio'\n    ]\n\n    data = {\n        'Metric': metrics,\n        portfolio1: [metrics_a.get(metric, \"N/A\") for metric in metrics],\n        portfolio2: [metrics_b.get(metric, \"N/A\") for metric in metrics]\n    }\n\n    advanced_metrics_df = pd.DataFrame(data)\n    advanced_metrics_df.set_index('Metric', inplace=True)\n    return advanced_metrics_df\n\ndef display_drawdowns(cum_returns, benchmark_cum_returns):\n    \"\"\"Displays top 10 drawdown details for portfolio and benchmark.\"\"\"\n    st.markdown(\"### \ud83d\udcc8 Top 10 Drawdowns for Portfolio\")\n    portfolio_drawdowns = get_drawdown_details(cum_returns)\n    if portfolio_drawdowns:\n        # Convert to DataFrame, sort by Drawdown, and take the top 10\n        portfolio_drawdowns_df = pd.DataFrame(portfolio_drawdowns).sort_values(by='Drawdown', ascending=True).head(10)\n        st.table(portfolio_drawdowns_df)\n\n        # Add a bar chart for visualization\n        fig_portfolio_drawdowns = px.bar(\n            portfolio_drawdowns_df, \n            x='Date', \n            y='Drawdown',\n            title='Top 10 Drawdowns for Portfolio',\n            labels={'Date': 'Date', 'Drawdown': 'Drawdown (%)'},\n            template='plotly_white',\n            color='Drawdown',\n            color_continuous_scale='RdYlGn'\n        )\n        st.plotly_chart(fig_portfolio_drawdowns, use_container_width=True)\n    else:\n        st.write(\"No drawdowns detected for the portfolio.\")\n\n    if not benchmark_cum_returns.empty:\n        st.markdown(\"### \ud83d\udcc8 Top 10 Drawdowns for Benchmark\")\n        benchmark_drawdowns = get_drawdown_details(benchmark_cum_returns)\n        if benchmark_drawdowns:\n            # Convert to DataFrame, sort by Drawdown, and take the top 10\n            benchmark_drawdowns_df = pd.DataFrame(benchmark_drawdowns).sort_values(by='Drawdown', ascending=True).head(10)\n            st.table(benchmark_drawdowns_df)\n\n            # Add a bar chart for visualization\n            fig_benchmark_drawdowns = px.bar(\n                benchmark_drawdowns_df, \n                x='Date', \n                y='Drawdown',\n                title='Top 10 Drawdowns for Benchmark',\n                labels={'Date': 'Date', 'Drawdown': 'Drawdown (%)'},\n                template='plotly_white',\n                color='Drawdown',\n                color_continuous_scale='RdYlGn'\n            )\n            st.plotly_chart(fig_benchmark_drawdowns, use_container_width=True)\n        else:\n            st.write(\"No drawdowns detected for the benchmark.\")\n    else:\n        st.write(\"Benchmark data not available for drawdown analysis.\")\n\ndef perform_risk_factor_attribution(returns):\n    \"\"\"Performs and displays risk factor attribution analysis.\"\"\"\n    start_date = returns.index.min().strftime('%Y-%m-%d')\n    end_date = returns.index.max().strftime('%Y-%m-%d')\n    factors = fetch_fama_french_factors(start_date, end_date)\n    if factors.empty:\n        st.write(\"Failed to retrieve factor data.\")\n        return\n    factors = factors.asfreq(returns.index.freq, method='ffill')\n    attribution, error = calculate_risk_factor_attribution(returns, factors)\n\n    if error:\n        st.error(error)\n        st.write(\"Risk factor attribution analysis is unavailable.\")\n    elif attribution.empty:\n        st.write(\"Risk factor attribution analysis is unavailable.\")\n    else:\n        st.write(\"Risk Factor Attribution:\")\n        st.table(attribution)\n        fig_attribution = px.bar(\n            attribution,\n            x='Factor',\n            y='Contribution (%)',\n            title='Risk Factor Attribution',\n            labels={'Contribution (%)': 'Contribution (%)'},\n            template=THEME\n        )\n        st.plotly_chart(fig_attribution, use_container_width=True)\n\ndef display_visualizations(cum_returns, benchmark_cum_returns, returns, price_data, weights, metrics, benchmark_metrics):\n    \"\"\"Displays an enhanced set of visualizations for Portfolio vs Benchmark with improved interactivity and aesthetics.\"\"\"\n    if not cum_returns.empty and not benchmark_cum_returns.empty:\n        plot_growth_comparison(cum_returns, benchmark_cum_returns, portfolio_name=\"Portfolio\", benchmark_name=\"Benchmark\")\n    else:\n        st.warning(\"Insufficient data to display Growth Comparison. Ensure both portfolio and benchmark have data.\")\n\n    plot_drawdown_comparison(\n        cum_returns,\n        benchmark_cum_returns,\n        portfolio_name=\"Portfolio\",\n        benchmark_name=\"Benchmark\"\n    )\n\n    st.subheader(\"\ud83d\udcc8 Compound Annual Growth Rate (CAGR) Over Time\")\n    if st.session_state.selected_time_frames:\n        plot_cagr_over_time(cum_returns, time_frames=st.session_state.selected_time_frames)\n    else:\n        st.warning(\"Please select at least one time frame for CAGR.\")\n\n    st.subheader(\"\ud83d\udce6 Returns Distribution Box Plot\")\n    plot_box(returns)\n\n    st.subheader(\"\ud83d\udd25 Correlation Heatmap of Assets\")\n    plot_correlation_heatmap(price_data.pct_change().dropna())\n\n    st.subheader(\"\ud83d\udd0d Risk-Return Attribution Analysis\")\n    plot_risk_return_attribution(returns, weights)\n    \n    # **Corrected Rolling Beta Calculation**\n    st.subheader(\"\ud83d\udcc9 Rolling Beta Over Time\")\n    if not benchmark_cum_returns.empty:\n        # Align benchmark returns with portfolio returns\n        benchmark_returns = benchmark_cum_returns.pct_change().dropna().reindex(returns.index, method='ffill').dropna()\n        \n        # Calculate rolling covariance and variance\n        rolling_cov = returns.rolling(window=252).cov(benchmark_returns)\n        rolling_var = benchmark_returns.rolling(window=252).var()\n        \n        # Compute rolling beta\n        rolling_beta = rolling_cov / rolling_var\n        \n        # Align rolling_beta with returns\n        rolling_beta = rolling_beta.dropna()\n        \n        fig_rolling_beta = px.line(\n            rolling_beta,\n            title='Rolling Beta (1-Year Window)',\n            labels={'index': 'Date', 'value': 'Beta'},\n            template=THEME\n        )\n        st.plotly_chart(fig_rolling_beta, use_container_width=True)\n    else:\n        st.warning(\"Benchmark returns not available to plot Rolling Beta.\")\n\n    st.subheader(\"\ud83d\udd25 Cumulative Returns Heatmap\")\n    plot_cumulative_returns_heatmap(cum_returns)\n\n    st.subheader(\"\ud83e\udd67 Portfolio Allocation Pie Chart\")\n    plot_allocation_pie(weights, price_data.columns, title=\"\ud83e\udd67 Portfolio Allocation\", hover_info=\"percent+name\")\n\n    st.subheader(\"\ud83d\udcc9 Rolling Metrics\")\n    if st.session_state.selected_rolling_periods:\n        plot_rolling_metrics(returns, windows=st.session_state.selected_rolling_periods)\n    else:\n        st.warning(\"Please select at least one rolling period to display metrics.\")\n    # **Added: Rolling Sharpe Ratio Graph**\n    st.subheader(\"\ud83d\udcc8 Rolling Sharpe Ratio Over Time\")\n    if not benchmark_cum_returns.empty:\n        # Calculate rolling Sharpe Ratio\n        rolling_sharpe = (returns.rolling(window=252).mean() - st.session_state.default_config['rf_rate']) / returns.rolling(window=252).std()\n        rolling_sharpe = rolling_sharpe.dropna()\n        \n        fig_rolling_sharpe = px.line(\n            rolling_sharpe,\n            title='Rolling Sharpe Ratio (1-Year Window)',\n            labels={'index': 'Date', 'value': 'Sharpe Ratio'},\n            template=THEME\n        )\n        st.plotly_chart(fig_rolling_sharpe, use_container_width=True)\n    else:\n        st.warning(\"Benchmark returns not available to plot Rolling Sharpe Ratio.\")\n    st.markdown(\"### \ud83d\udca1 Recommendations\")\n    recommendations = generate_recommendations(\n        allocations=[alloc for alloc in weights if alloc > 0],\n        available_selected=price_data.columns\n    )\n    if recommendations:\n        for rec in recommendations:\n            st.markdown(f\"\u2022 {rec}\")\n    else:\n        st.success(\"\ud83c\udf89 Your portfolio allocations are well-balanced!\")\n\n    st.markdown(\"### \ud83d\udcdd Final Score\")\n    portfolio_score = calculate_final_score(metrics, benchmark_metrics)\n    st.metric(\"\ud83d\udcca Portfolio Score\", f\"{portfolio_score:.2f} / 100\")\n    # Add interpretation/comment similar to Risk Factor Attribution\n    st.markdown(\"\"\"\n    **\ud83d\udcd6 Interpretation:**\n    - The **Final Score** represents a comprehensive evaluation of your portfolio's performance based on selected metrics.\n    - A higher score indicates a better balance between risk and return, aligning with your investment objectives.\n    - Use this score to assess overall portfolio health and identify areas for improvement.\n    \"\"\")\n\ndef display_visualizations_portfolio_vs_portfolio(\n    cum_returns_a, cum_returns_b, returns_a, returns_b,\n    price_data_a, price_data_b, weights_a, weights_b,\n    portfolio1, portfolio2, metrics_a, metrics_b\n):\n    \"\"\"Displays various visualizations for Portfolio vs Portfolio.\"\"\"\n    # Growth Comparison\n    fig_growth = px.line(title='Growth Comparison')\n    fig_growth.add_scatter(x=cum_returns_a.index, y=cum_returns_a, mode='lines', name=portfolio1)\n    fig_growth.add_scatter(x=cum_returns_b.index, y=cum_returns_b, mode='lines', name=portfolio2)\n    st.plotly_chart(fig_growth, use_container_width=True)\n\n    # Drawdown Comparison\n    plot_drawdown_comparison(\n        cum_returns_a,\n        cum_returns_b,\n        portfolio_name=portfolio1,\n        benchmark_name=portfolio2\n    )\n    # Risk-Return Scatter Plot\n    st.subheader(\"\ud83d\udd04 Risk vs Return Scatter Plot\")\n    fig_risk_return = go.Figure()\n    fig_risk_return.add_trace(go.Scatter(\n        x=[returns_a.std() * np.sqrt(252)],\n        y=[returns_a.mean() * 252],\n        mode='markers',\n        marker=dict(\n            size=[w * 100 for w in weights_a],\n            color='cyan'\n        ),\n        text=available_a,\n        name=portfolio1\n    ))\n    fig_risk_return.add_trace(go.Scatter(\n        x=[returns_b.std() * np.sqrt(252)],\n        y=[returns_b.mean() * 252],\n        mode='markers',\n        marker=dict(\n            size=[w * 100 for w in weights_b],\n            color='orange'\n        ),\n        text=available_b,\n        name=portfolio2\n    ))\n    fig_risk_return.update_layout(\n        title='\ud83d\udd04 Risk vs Return Scatter Plot',\n        xaxis_title='Annualized Volatility (Std Dev)',\n        yaxis_title='Annualized Return',\n        template=THEME\n    )\n    st.plotly_chart(fig_risk_return, use_container_width=True)\n    # Box Plot for Returns Distribution\n    st.subheader(\"\ud83d\udce6 Returns Distribution Box Plot\")\n    # Create a DataFrame with both portfolios' returns\n    returns_df = pd.DataFrame({\n        portfolio1: returns_a,\n        portfolio2: returns_b\n    })\n    plot_box(\n        returns_df,\n        title=\"Returns Distribution for Both Portfolios\",\n        multiple=True,\n        portfolio_names=[portfolio1, portfolio2]\n    )\n\n    # Heatmap of Correlations Between Assets\n    st.subheader(\"\ud83d\udd25 Correlation Heatmap of Assets\")\n    plot_correlation_heatmap(price_data_a.pct_change().dropna())\n    plot_correlation_heatmap(price_data_b.pct_change().dropna())\n\n    # Portfolio Allocation Pie Chart\n    st.subheader(\"\ud83e\udd67 Portfolio Allocation Pie Chart\")\n    plot_allocation_pie(weights_a, price_data_a.columns, title=f\"{portfolio1} Allocation\", hover_info=\"percent+name\")\n    plot_allocation_pie(weights_b, price_data_b.columns, title=f\"{portfolio2} Allocation\", hover_info=\"percent+name\")\n\n    st.markdown(\"### \ud83d\udcdd Final Score\")\n    score_a = calculate_final_score(metrics_a, metrics_b)\n    score_b = calculate_final_score(metrics_b, metrics_a)\n    st.write(f\"**{portfolio1}:** {score_a:.2f} / 100\")\n    st.write(f\"**{portfolio2}:** {score_b:.2f} / 100\")\n\ndef plot_box(\n    returns: Union[pd.Series, pd.DataFrame],\n    title: str = \"Returns Distribution Box Plot\",\n    labels: dict = {\"Return\": \"Returns (%)\", \"Asset\": \"Asset\"},\n    color_sequence: list = px.colors.qualitative.Bold,\n    multiple: bool = False,\n    portfolio_names: list = None\n):\n    \"\"\"\n    Generic function to plot box plots for portfolio returns.\n\n    Parameters:\n    - returns (pd.DataFrame or pd.Series): Returns data.\n    - title (str): Title of the plot.\n    - labels (dict): Axis labels.\n    - color_sequence (list): Colors for the boxes.\n    - multiple (bool): If True, handle multiple portfolios.\n    - portfolio_names (list): Names of the portfolios for labeling.\n    \"\"\"\n    if isinstance(returns, pd.DataFrame):\n        if multiple and portfolio_names:\n            for i, col in enumerate(returns.columns):\n                fig = px.box(\n                    returns[[col]].reset_index(drop=True),\n                    y=col,\n                    title=f\"{title} for {portfolio_names[i]}\",\n                    labels=labels,\n                    points='all',\n                    template=THEME,\n                    color_discrete_sequence=[color_sequence[i % len(color_sequence)]]\n                )\n                fig.update_traces(boxmean='sd')\n                fig.update_layout(xaxis_title=\"Assets\", yaxis_title=labels.get('Return', 'Returns (%)'))\n                st.plotly_chart(fig, use_container_width=True)\n        else:\n            melted_returns = returns.reset_index().melt(id_vars=returns.index.name if returns.index.name else 'Date', var_name='Asset', value_name='Return')\n            fig = px.box(\n                melted_returns,\n                x='Asset',\n                y='Return',\n                title=title,\n                labels=labels,\n                points='all',\n                template=THEME,\n                color='Asset',\n                color_discrete_sequence=color_sequence\n            )\n    elif isinstance(returns, pd.Series):\n        fig = px.box(\n            returns.rename(\"Return\"),\n            y='Return',\n            title=title,\n            labels=labels,\n            points='all',\n            template=THEME,\n            color_discrete_sequence=['cyan']\n        )\n    else:\n        st.warning(\"Returns data is neither a DataFrame nor a Series.\")\n        return\n\n    fig.update_traces(boxmean='sd')\n    fig.update_layout(\n        xaxis_title=labels.get(\"Asset\", \"Assets\"),\n        yaxis_title=labels.get(\"Return\", \"Returns (%)\"),\n        legend_title=\"Assets\"\n    )\n    st.plotly_chart(fig, use_container_width=True)\n\n    st.markdown(\"\"\"\n        **\ud83d\udcd6 Interpretation:**\n        - **Boxes:** Represent the interquartile range (IQR) where the middle 50% of the data lies.\n        - **Median Line:** Indicates the median return.\n        - **Whiskers:** Extend to show the range of the data excluding outliers.\n        - **Outliers:** Individual points outside the whiskers represent atypical returns.\n        - **Box Mean (Shaded):** Shows the mean and standard deviation of returns.\n    \"\"\")\n\ndef plot_cumulative_returns_heatmap(cum_returns):\n    \"\"\"Plots the cumulative returns heatmap.\"\"\"\n    try:\n        cum_returns_normalized = cum_returns / cum_returns.max()\n        fig_heatmap = px.imshow(\n            cum_returns_normalized.to_frame().T,\n            labels=dict(x=\"Date\", y=\"Portfolio\", color=\"Normalized Cumulative Return\"),\n            title=\"Cumulative Returns Heatmap\",\n            aspect=\"auto\",\n            color_continuous_scale='Viridis',\n            template=THEME\n        )\n        st.plotly_chart(fig_heatmap, use_container_width=True)\n        st.markdown(\n            \"**Interpretation:** The heatmap visualizes the normalized cumulative returns over time, \"\n            \"allowing for an intuitive comparison of portfolio performance across different periods.\"\n        )\n    except Exception as e:\n        st.error(f\"Error plotting cumulative returns heatmap: {e}\")\n\ndef plot_correlation_heatmap(correlation_data):\n    \"\"\"Plots an enhanced correlation heatmap using Plotly with improved aesthetics.\"\"\"\n    if correlation_data.empty:\n        st.warning(\"No data available to plot correlation heatmap.\")\n        return\n    corr = correlation_data.corr()\n    fig_heatmap = px.imshow(\n        corr,\n        title=\"\ud83d\udcc8 Correlation Heatmap of Assets\",\n        labels=dict(x=\"Asset\", y=\"Asset\", color=\"Correlation\"),\n        color_continuous_scale='RdBu',\n        zmin=-1,\n        zmax=1,\n        text_auto=True,\n        aspect=\"auto\",\n        template=THEME\n    )\n    fig_heatmap.update_layout(\n        title_x=0.5,\n        xaxis_title=\"Assets\",\n        yaxis_title=\"Assets\"\n    )\n    fig_heatmap.update_traces(\n        hovertemplate=\"Asset1: %{x}<br>Asset2: %{y}<br>Correlation: %{z:.2f}<extra></extra>\"\n    )\n    st.plotly_chart(fig_heatmap, use_container_width=True)\n\ndef plot_growth_comparison(cum_returns, benchmark_cum_returns, portfolio_name='Portfolio', benchmark_name='Benchmark'):\n    \"\"\"Plots an enhanced growth comparison between portfolio and benchmark with improved interactivity.\"\"\"\n    fig_growth = px.line(\n        title='\ud83d\udcc8 Growth Comparison',\n        labels={'value': 'Cumulative Returns', 'index': 'Date'},\n        template=THEME\n    )\n    \n    fig_growth.add_scatter(\n        x=cum_returns.index,\n        y=cum_returns,\n        mode='lines',\n        name=portfolio_name,\n        line=dict(color='cyan', width=2)\n    )\n    fig_growth.add_scatter(\n        x=benchmark_cum_returns.index,\n        y=benchmark_cum_returns,\n        mode='lines',\n        name=benchmark_name,\n        line=dict(color='orange', width=2, dash='dash')\n    )\n    \n    fig_growth.update_layout(\n        hovermode='x unified',\n        xaxis=dict(showgrid=True),\n        yaxis=dict(showgrid=True),\n        legend=dict(title=\"Legend\", x=0.01, y=0.99),\n        height=600  # Added height parameter\n    )\n    \n    st.plotly_chart(fig_growth, use_container_width=True, height=600, config={'responsive': True})\n\ndef plot_drawdown_comparison(cum_returns_portfolio: pd.Series, cum_returns_benchmark: pd.Series, portfolio_name='Portfolio', benchmark_name='Benchmark') -> None:\n    \"\"\"Plots an enhanced drawdown comparison between portfolio and benchmark with improved visuals.\"\"\"\n    \n    # Calculate drawdowns for portfolio\n    portfolio_drawdown = (cum_returns_portfolio / cum_returns_portfolio.expanding().max() - 1) * 100\n    \n    # Calculate drawdowns for benchmark\n    benchmark_drawdown = (cum_returns_benchmark / cum_returns_benchmark.expanding().max() - 1) * 100\n    \n    fig_drawdown = go.Figure()\n    \n    # Add portfolio drawdown\n    fig_drawdown.add_trace(go.Scatter(\n        x=portfolio_drawdown.index,\n        y=portfolio_drawdown,\n        mode='lines',\n        name=portfolio_name,\n        line=dict(color='red', width=2)\n    ))\n    \n    # Add benchmark drawdown if available\n    if not cum_returns_benchmark.empty:\n        fig_drawdown.add_trace(go.Scatter(\n            x=benchmark_drawdown.index,\n            y=benchmark_drawdown,\n            mode='lines',\n            name=benchmark_name,\n            line=dict(color='blue', width=2, dash='dash')\n        ))\n    \n    # Update layout with better formatting\n    fig_drawdown.update_layout(\n        title='\ud83d\udcc9 Drawdown Comparison',\n        hovermode='x unified',\n        yaxis=dict(\n            showgrid=True,\n            zeroline=True,\n            title='Drawdown (%)',\n            tickformat='.1f'\n        ),\n        xaxis=dict(\n            showgrid=True,\n            title='Date'\n        ),\n        legend=dict(\n            x=0.01,\n            y=0.99,\n            bgcolor='rgba(255, 255, 255, 0.8)'\n        ),\n        height=500,\n        template='plotly_white'\n    )\n    \n    st.plotly_chart(fig_drawdown, use_container_width=True)\n\ndef plot_cagr_over_time(cum_returns, time_frames):\n    \"\"\"Plots CAGR over different time frames.\"\"\"\n    for frame in time_frames:\n        plot_cagr(cum_returns, frame)\n\ndef plot_risk_return_attribution(returns, weights):\n    \"\"\"Plots the risk-return attribution analysis.\"\"\"\n    fig_rra = px.scatter(\n        x=returns.mean() * 252,\n        y=returns.std() * np.sqrt(252),\n        size=weights*100,\n        color=returns.mean() / returns.std(),\n        hover_name=returns.index,\n        title='Risk-Return Attribution',\n        labels={'x': 'Annualized Return', 'y': 'Annualized Risk (Std Dev)'},\n        template='plotly_dark',\n        size_max=60\n    )\n    st.plotly_chart(fig_rra, use_container_width=True)\n\ndef plot_rolling_metrics(returns, windows):\n    \"\"\"Plots rolling metrics based on selected window periods.\"\"\"\n    for window in windows:\n        rolling_mean = returns.rolling(window=window).mean()\n        rolling_std = returns.rolling(window=window).std()\n        fig = px.line(title=f'Rolling {window}-Day Mean and Std Dev')\n        fig.add_scatter(x=rolling_mean.index, y=rolling_mean, mode='lines', name='Rolling Mean')\n        fig.add_scatter(x=rolling_std.index, y=rolling_std, mode='lines', name='Rolling Std Dev')\n        st.plotly_chart(fig, use_container_width=True)\n\n@handle_exceptions\ndef calculate_risk_factor_attribution(returns: pd.Series, factors: pd.DataFrame, annualization_factor: int = 252) -> tuple:\n    \"\"\"\n    Decompose portfolio returns by risk factors using linear regression and provide\n    comments on the factor contributions.\n\n    Parameters:\n    - returns (pd.Series): Portfolio returns.\n    - factors (pd.DataFrame): DataFrame where each column represents a risk factor.\n    - annualization_factor (int): Annualization factor (252 for daily, 12 for monthly returns).\n\n    Returns:\n    - pd.DataFrame: Factor names, their contribution percentages, and explanatory comments.\n    - None: If an exception occurs, handled by the decorator.\n    \"\"\"\n    # Ensure returns has a name, or assign one if missing\n    if returns.name is None:\n        returns = returns.rename(\"Portfolio_Returns\")\n\n    # Convert returns to a DataFrame\n    returns = returns.to_frame()\n\n    # Ensure both returns and factors are in decimal format\n    if returns['Portfolio_Returns'].max() > 1:\n        returns /= 100\n\n    if factors.max().max() > 1:\n        factors /= 100\n\n    # Adjust returns to excess returns by subtracting RF (if it exists in factors)\n    if 'RF' in factors.columns:\n        returns['Portfolio_Returns'] -= factors['RF']\n        factors = factors.drop(columns=['RF'])\n\n    # Align the data using an inner join and drop any remaining NaN values\n    aligned_data = pd.merge(returns, factors, left_index=True, right_index=True, how='inner').dropna()\n\n    # Check that we have data after merging\n    if aligned_data.empty:\n        raise ValueError(\"No overlapping dates between returns and factors after dropping NaNs.\")\n\n    # Prepare X and y for regression\n    X = aligned_data[factors.columns]\n    X = sm.add_constant(X)  # Add intercept\n    y = aligned_data['Portfolio_Returns']\n\n    # Run the regression model\n    model = sm.OLS(y, X).fit()\n    coefficients = model.params.drop('const', errors='ignore')\n\n    # Calculate factor contributions (non-annualized)\n    factor_means = X.mean().drop('const', errors='ignore')\n    contributions = coefficients * factor_means\n\n    # Calculate total return\n    total_return = returns['Portfolio_Returns'].mean()\n\n    # Check if total_return is zero to avoid division by zero\n    if np.isclose(total_return, 0):\n        st.warning(\"Total portfolio return is zero. Cannot compute contribution percentages.\")\n        attribution_df = pd.DataFrame({\n            'Factor': contributions.index,\n            'Contribution (%)': [0.0] * len(contributions)\n        })\n    else:\n        # Calculate percentage contributions\n        attribution_df = pd.DataFrame({\n            'Factor': contributions.index,\n            'Contribution (%)': (contributions / total_return * 100).round(2)\n        })\n\n    # Remove the constant term if it exists\n    if 'const' in attribution_df['Factor'].values:\n        attribution_df = attribution_df[attribution_df['Factor'] != 'const']\n\n    # Use full names for factors\n    factor_full_names = {\n        'Mkt-RF': 'Market Risk Premium',\n        'SMB': 'Small Minus Big',\n        'HML': 'High Minus Low'\n    }\n    attribution_df['Factor'] = attribution_df['Factor'].replace(factor_full_names)\n\n    # Generate detailed comments based on factor contributions\n    comments = []\n    for _, row in attribution_df.iterrows():\n        factor_name = row['Factor']\n        contribution = row['Contribution (%)']\n\n        # Market Risk Premium (Mkt-RF) Analysis\n        if factor_name == 'Market Risk Premium':\n            if contribution > 75:\n                comments.append(f\"{factor_name} = {contribution}%: Extremely high sensitivity to market movements.\")\n            elif contribution > 50:\n                comments.append(f\"{factor_name} = {contribution}%: High sensitivity to overall market movements.\")\n            elif contribution > 30:\n                comments.append(f\"{factor_name} = {contribution}%: Notable sensitivity to market risk.\")\n            elif contribution > 10:\n                comments.append(f\"{factor_name} = {contribution}%: Moderate exposure to market risk.\")\n            else:\n                comments.append(f\"{factor_name} = {contribution}%: Low exposure to market risk.\")\n\n        # Small Minus Big (SMB) Analysis\n        elif factor_name == 'Small Minus Big':\n            if contribution > 5:\n                comments.append(f\"{factor_name} = {contribution}%: Strong tilt towards small-cap stocks.\")\n            elif contribution > 0:\n                comments.append(f\"{factor_name} = {contribution}%: Mild preference for small-cap stocks.\")\n            elif contribution > -5:\n                comments.append(f\"{factor_name} = {contribution}%: Slight tilt towards larger-cap stocks.\")\n            else:\n                comments.append(f\"{factor_name} = {contribution}%: Clear preference for large-cap stocks.\")\n\n        # High Minus Low (HML) Analysis\n        elif factor_name == 'High Minus Low':\n            if contribution > 5:\n                comments.append(f\"{factor_name} = {contribution}%: Strong tilt toward value stocks.\")\n            elif contribution > 0:\n                comments.append(f\"{factor_name} = {contribution}%: Slight positive tilt toward value stocks.\")\n            elif contribution > -5:\n                comments.append(f\"{factor_name} = {contribution}%: Minor negative tilt toward growth stocks.\")\n            else:\n                comments.append(f\"{factor_name} = {contribution}%: Clear preference for growth stocks.\")\n\n    # Add comments to the DataFrame\n    attribution_df['Comment'] = comments\n\n    return attribution_df, None\n\n@handle_exceptions\ndef fetch_fama_french_factors(start_date: str, end_date: str) -> pd.DataFrame:\n    \"\"\"\n    Fetch Fama-French daily factor data between specified dates.\n\n    Parameters:\n    - start_date (str): Start date in 'YYYY-MM-DD' format.\n    - end_date (str): End date in 'YYYY-MM-DD' format.\n\n    Returns:\n    - pd.DataFrame: DataFrame containing Fama-French factors.\n    \"\"\"\n    # [Function Implementation Remains Unchanged]\n    try:\n        # Fetch daily Fama-French factors\n        ff_data = pdr.get_data_famafrench('F-F_Research_Data_Factors_daily', start=start_date, end=end_date)\n        if not ff_data:\n            st.error(\"Fama-French data not found.\")\n            return pd.DataFrame()\n\n        factors = ff_data[0]\n        factors = factors.rename(columns=lambda x: x.strip())  # Remove any leading/trailing spaces\n        factors /= 100  # Convert percentage returns to decimal format\n\n        # Fill missing data by forward and backward filling\n        factors = factors.fillna(method='ffill').fillna(method='bfill')\n\n        # Check for necessary columns\n        required_columns = {'Mkt-RF', 'SMB', 'HML', 'RF'}\n        if not required_columns.issubset(factors.columns):\n            missing = required_columns - set(factors.columns)\n            st.error(f\"Missing factor columns: {', '.join(missing)}\")\n            return pd.DataFrame()\n\n        return factors\n    except Exception as e:\n        st.error(f\"Error fetching factor data: {e}\")\n        return pd.DataFrame()\n\n@handle_exceptions\ndef calculate_final_score(primary_metrics: dict, comparison_metrics: dict) -> float:\n    metrics_to_compare = [\n        'Annualized Return (CAGR)',\n        'Sharpe Ratio',\n        'Sortino Ratio',\n        'Treynor Ratio',\n        'Calmar Ratio',\n        'Alpha (annualized)',\n        'Information Ratio',\n        'Modigliani\u2013Modigliani Measure',\n        'Upside Capture Ratio',\n        'Gain/Loss Ratio'\n    ]\n    metric_weights = {\n        'Annualized Return (CAGR)': 15,\n        'Sharpe Ratio': 15,\n        'Sortino Ratio': 10,\n        'Treynor Ratio': 10,\n        'Calmar Ratio': 10,\n        'Alpha (annualized)': 10,\n        'Information Ratio': 10,\n        'Modigliani\u2013Modigliani Measure': 10,\n        'Upside Capture Ratio': 5,\n        'Gain/Loss Ratio': 5\n    }\n\n    score = 0\n    total = 0\n\n    for metric in metrics_to_compare:\n        primary = primary_metrics.get(metric, \"N/A\")\n        comparison = comparison_metrics.get(metric, \"N/A\")\n        \n        # Handle numpy types and convert to native Python types\n        if isinstance(primary, (np.float64, np.float32)):\n            primary = float(primary)\n        if isinstance(comparison, (np.float64, np.float32)):\n            comparison = float(comparison)\n            \n        if primary != \"N/A\" and comparison != \"N/A\":\n            try:\n                # Convert string values to float if necessary\n                primary_val = float(str(primary).strip('%')) if isinstance(primary, str) else primary\n                comparison_val = float(str(comparison).strip('%')) if isinstance(comparison, str) else comparison\n                \n                weight = metric_weights.get(metric, 1)\n                \n                if abs(comparison_val) < 1e-10:  # Better way to check for near-zero values\n                    st.warning(f\"Comparison metric '{metric}' is too close to zero. Skipping this metric.\")\n                    continue\n                    \n                if primary_val > comparison_val:\n                    score += weight * (primary_val / comparison_val)\n                else:\n                    score += weight * (primary_val / comparison_val) * 0.5  # Partial credit\n                    \n                total += weight\n            except (ValueError, TypeError) as e:\n                st.warning(f\"Error processing metric '{metric}': {str(e)}\")\n                continue\n                \n    return (score / total) * 100 if total > 0 else 0.0\n    \n@handle_exceptions\ndef plot_cagr_over_time(cum_returns: pd.Series, time_frames: list = ['Weekly', 'Monthly', 'Quarterly', 'Annually']) -> None:\n    \"\"\"\n    Plot CAGR over multiple time frames.\n\n    Parameters:\n    - cum_returns (pd.Series): Cumulative returns of the portfolio.\n    - time_frames (list): List of time frames to calculate CAGR.\n    \"\"\"\n    frequency_map = {\n        'Weekly': 'W',\n        'Monthly': 'M',\n        'Quarterly': 'Q',\n        'Annually': 'Y'\n    }\n    fig = go.Figure()\n    for tf in time_frames:\n        freq = frequency_map.get(tf)\n        if not freq:\n            continue\n        rolled = cum_returns.resample(freq).last()\n        if rolled.empty:\n            st.warning(f\"No data available for {tf} CAGR calculation.\")\n            continue\n        years = (rolled.index[-1] - rolled.index[0]).days / 365.25\n        if years <= 0:\n            st.warning(f\"Not enough data to calculate {tf} CAGR.\")\n            continue\n        cagr = (rolled / rolled.iloc[0]) ** (1/years) - 1\n        fig.add_trace(go.Scatter(x=rolled.index, y=cagr, mode='lines', name=f'{tf} CAGR'))\n    fig.update_layout(\n        title='CAGR Over Multiple Time Frames',\n        xaxis_title='Date',\n        yaxis_title='CAGR',\n        hovermode=HOVERMODE\n    )\n    st.plotly_chart(fig, use_container_width=True)\n\n@handle_exceptions\ndef backtest(weights: list, prices: pd.DataFrame, rebalance_freq: str = 'M', broker_fee: float = 0.0, debug: bool = False) -> tuple:\n    \"\"\"\n    Backtest the portfolio based on weights and price data.\n\n    Parameters:\n    - weights (list): Allocation weights for each asset.\n    - prices (pd.DataFrame): Adjusted closing prices of assets.\n    - rebalance_freq (str): Rebalancing frequency (e.g., 'D', 'W', 'M').\n    - broker_fee (float): Broker fee as a percentage.\n    - debug (bool): If True, prints debug information.\n\n    Returns:\n    - tuple: (portfolio_returns: pd.Series, cum_returns: pd.Series)\n    \"\"\"\n    try:\n        # Calculate returns and drop any NaN values\n        returns = prices.pct_change().dropna()\n        if returns.empty:\n            st.error(\"Returns data is empty after calculating percentage changes. Please ensure your price data is accurate and spans multiple periods.\")\n            return pd.Series(dtype=float), pd.Series(dtype=float)\n\n        # Ensure weights are a NumPy array and normalized\n        weights = np.array(weights)\n        if not np.isclose(weights.sum(), 1.0):\n            weights = weights / weights.sum()\n\n        # Calculate portfolio returns\n        portfolio_returns = returns.dot(weights)\n        if not isinstance(portfolio_returns, pd.Series):\n            portfolio_returns = portfolio_returns.squeeze()\n\n        # Identify rebalancing dates\n        if rebalance_freq.upper() == 'D':\n            rebalance_dates = returns.index\n        else:\n            rebalance_dates = returns.resample(rebalance_freq.upper()).last().dropna().index\n\n        # Debug: Show rebalancing dates\n        if debug:\n            st.write(f\"Rebalancing Dates: {rebalance_dates.tolist()}\")\n\n        # Ensure rebalance_dates are in the portfolio_returns index\n        valid_rebalance_dates = rebalance_dates.intersection(portfolio_returns.index)\n        if debug:\n            st.write(f\"Valid Rebalancing Dates: {valid_rebalance_dates.tolist()}\")\n\n        # Apply broker fees on rebalancing dates\n        if not valid_rebalance_dates.empty:\n            portfolio_returns.loc[valid_rebalance_dates] -= broker_fee / 100  # Convert to decimal\n        else:\n            st.warning(\"\u26a0\ufe0f No valid rebalancing dates found within the returns data.\")\n\n        # Calculate cumulative returns\n        cum_returns = (1 + portfolio_returns).cumprod()\n        if cum_returns.empty:\n            st.error(\"Cumulative returns are empty. Check the data and allocations.\")\n            return pd.Series(dtype=float), pd.Series(dtype=float)\n\n        # Debug: Show cumulative returns\n        if debug:\n            st.write(\"Cumulative Returns:\")\n            st.write(cum_returns)\n\n        return portfolio_returns, cum_returns\n    except Exception as e:\n        st.error(f\"Error during backtesting: {e}\")\n        return pd.Series(dtype=float), pd.Series(dtype=float)\n\n@handle_exceptions\ndef calculate_sharpe_ratio(returns: pd.Series, rf: float = 0.02) -> float:\n    \"\"\"\n    Calculate the Sharpe Ratio for a given set of returns.\n\n    Parameters:\n    - returns (pd.Series): Daily returns of the portfolio.\n    - rf (float): Risk-free rate (default is 2%).\n\n    Returns:\n    - float: Sharpe Ratio.\n    \"\"\"\n    excess_return = returns.mean() * 252 - rf\n    std_dev = returns.std() * np.sqrt(252)\n    return excess_return / std_dev if std_dev != 0 else np.nan\n\n@handle_exceptions\ndef calculate_sortino_ratio(returns: pd.Series, rf: float = 0.02) -> float:\n    \"\"\"\n    Calculate the Sortino Ratio for a given set of returns.\n\n    Parameters:\n    - returns (pd.Series): Daily returns of the portfolio.\n    - rf (float): Risk-free rate (default is 2%).\n\n    Returns:\n    - float: Sortino Ratio.\n    \"\"\"\n    excess_return = returns.mean() * 252 - rf\n    downside_std = returns[returns < 0].std() * np.sqrt(252)\n    return excess_return / downside_std if downside_std != 0 else np.nan\n\ndef calculate_treynor_ratio(returns: pd.Series, benchmark_returns: pd.Series, rf: float = 0.02) -> float:\n    \"\"\"\n    Calculate the Treynor Ratio for a given set of returns.\n\n    Parameters:\n    - returns (pd.Series): Daily returns of the portfolio.\n    - benchmark_returns (pd.Series): Daily returns of the benchmark.\n    - rf (float): Risk-free rate (default is 2%).\n\n    Returns:\n    - float: Treynor Ratio.\n    \"\"\"\n    beta = calculate_beta(returns, benchmark_returns)\n    excess_return = returns.mean() * 252 - rf\n    return excess_return / beta if beta != 0 else np.nan\n\ndef calculate_drawdown(cum_returns: pd.Series) -> float:\n    \"\"\"\n    Calculate the maximum drawdown of the portfolio.\n    \n    Parameters:\n    - cum_returns (pd.Series): Cumulative returns of the portfolio.\n    \n    Returns:\n    - float: Maximum Drawdown.\n    \"\"\"\n    peak = cum_returns.expanding(min_periods=1).max()\n    drawdown = (cum_returns - peak) / peak\n    max_drawdown = drawdown.min()\n    return max_drawdown if not np.isnan(max_drawdown) else np.nan\n\n@handle_exceptions\ndef calculate_cagr(cum_returns: pd.Series, rf: float = 0.02) -> float:\n    \"\"\"\n    Calculate the Compound Annual Growth Rate (CAGR).\n    \n    Parameters:\n    - cum_returns (pd.Series): Cumulative returns of the portfolio.\n    - rf (float): Risk-free rate (default is 2%).\n    \n    Returns:\n    - float: CAGR as a decimal.\n    \"\"\"\n    if cum_returns.empty:\n        return np.nan\n    initial_date = cum_returns.index[0]\n    final_date = cum_returns.index[-1]\n    years = (final_date - initial_date).days / 365.25\n    if years <= 0:\n        return np.nan\n    ending_value = cum_returns.iloc[-1]\n    initial_value = cum_returns.iloc[0]\n    cagr = (ending_value / initial_value) ** (1 / years) - 1\n    return cagr\n\n@handle_exceptions\ndef calculate_calmar_ratio(returns: pd.Series, cum_returns: pd.Series, rf: float = 0.02) -> float:\n    \"\"\"\n    Calculate the Calmar Ratio of the portfolio.\n    \n    Parameters:\n    - returns (pd.Series): Daily returns of the portfolio.\n    - cum_returns (pd.Series): Cumulative returns of the portfolio.\n    - rf (float): Risk-free rate (default is 2%).\n    \n    Returns:\n    - float: Calmar Ratio.\n    \"\"\"\n    cagr = calculate_cagr(cum_returns, rf)\n    max_dd = calculate_drawdown(cum_returns)\n    return cagr / abs(max_dd) if max_dd != 0 else np.nan\n\n@handle_exceptions\ndef calculate_beta(returns: pd.Series, benchmark_returns: pd.Series) -> float:\n    \"\"\"\n    Calculate the Beta of the portfolio relative to the benchmark.\n\n    Parameters:\n    - returns (pd.Series): Daily returns of the portfolio.\n    - benchmark_returns (pd.Series): Daily returns of the benchmark.\n\n    Returns:\n    - float: Beta value.\n    \"\"\"\n    if returns.empty or benchmark_returns.empty:\n        return np.nan\n    covariance = np.cov(returns, benchmark_returns)\n    cov = covariance[0, 1]\n    var_bench = covariance[1, 1]\n    return cov / var_bench if var_bench != 0 else np.nan\n\n@handle_exceptions\ndef calculate_alpha(returns: pd.Series, benchmark_returns: pd.Series, rf: float = 0.02) -> float:\n    \"\"\"\n    Calculate the Alpha of the portfolio relative to the benchmark.\n\n    Parameters:\n    - returns (pd.Series): Daily returns of the portfolio.\n    - benchmark_returns (pd.Series): Daily returns of the benchmark.\n    - rf (float): Risk-free rate (default is 2%).\n\n    Returns:\n    - float: Alpha value.\n    \"\"\"\n    beta = calculate_beta(returns, benchmark_returns)\n    if np.isnan(beta):\n        return np.nan\n    portfolio_return = returns.mean() * 252\n    benchmark_return = benchmark_returns.mean() * 252\n    return portfolio_return - (rf + beta * (benchmark_return - rf))\n\n@handle_exceptions\ndef calculate_r_squared(returns: pd.Series, benchmark_returns: pd.Series) -> float:\n    \"\"\"\n    Calculate the R-squared of the portfolio relative to the benchmark.\n\n    Parameters:\n    - returns (pd.Series): Daily returns of the portfolio.\n    - benchmark_returns (pd.Series): Daily returns of the benchmark.\n\n    Returns:\n    - float: R-squared value.\n    \"\"\"\n    if returns.empty or benchmark_returns.empty:\n        return np.nan\n    correlation = returns.corr(benchmark_returns)\n    return correlation ** 2 if not np.isnan(correlation) else np.nan\n\n@handle_exceptions\ndef calculate_information_ratio(returns: pd.Series, benchmark_returns: pd.Series) -> float:\n    \"\"\"\n    Calculate the Information Ratio of the portfolio relative to the benchmark.\n    \n    Parameters:\n    - returns (pd.Series): Daily returns of the portfolio.\n    - benchmark_returns (pd.Series): Daily returns of the benchmark.\n    \n    Returns:\n    - float: Information Ratio.\n    \"\"\"\n    if returns.empty or benchmark_returns.empty:\n        return np.nan\n    active_return = (returns.mean() - benchmark_returns.mean()) * 252\n    tracking_error = calculate_tracking_error(returns, benchmark_returns)\n    return active_return / tracking_error if tracking_error != 0 else np.nan\n\ndef generate_recommendations(allocations: list, available_selected: list) -> list:\n    \"\"\"\n    Generate basic portfolio recommendations based on current allocations.\n\n    Parameters:\n    - allocations (list of float): Current allocation percentages.\n    - available_selected (list of str): List of selected asset tickers.\n\n    Returns:\n    - list of str: Recommendations.\n    \"\"\"\n    recommendations = []\n    max_alloc = max(allocations)\n    min_alloc = min(allocations)\n    total_alloc = sum(allocations)\n\n    if max_alloc > 40.0:\n        idx = allocations.index(max_alloc)\n        ticker = available_selected[idx]\n        recommendations.append(f\"\ud83d\udd39 Consider reducing the allocation to **{ticker}** since it constitutes **{max_alloc:.2f}%** of your portfolio.\")\n\n    if min_alloc < 5.0:\n        idx = allocations.index(min_alloc)\n        ticker = available_selected[idx]\n        recommendations.append(f\"\ud83d\udd38 Consider increasing the allocation to **{ticker}** to at least **5.00%** for better diversification.\")\n\n    if total_alloc < 100.0:\n        recommendations.append(\"\ud83d\udd39 Consider allocating the remaining funds to additional assets to reach a total of **100%**.\")\n\n    return recommendations\n\ndef get_common_benchmarks(selected_tickers: list) -> dict:\n    \"\"\"\n    Define benchmark suggestions based on asset sectors or indices.\n\n    Parameters:\n    - selected_tickers (list of str): List of selected asset tickers.\n\n    Returns:\n    - dict: Dictionary of benchmark names and their corresponding tickers.\n    \"\"\"\n    # Example benchmark sets\n    sp500 = {\"AAPL\", \"MSFT\", \"GOOGL\", \"AMZN\", \"META\", \"TSLA\"}  # Example S&P 500 tech companies\n    nasdaq_tech = {\"AAPL\", \"MSFT\", \"GOOGL\", \"AMZN\", \"META\", \"TSLA\", \"NVDA\", \"AMD\"}\n\n    selected_set = set(selected_tickers)\n\n    if selected_set.issubset(sp500):\n        return {\n            \"S&P 500\": \"^GSPC\",\n            \"Dow Jones Industrial Average\": \"^DJI\",\n            \"Russell 2000\": \"^RUT\",\n            \"Custom\": \"CUSTOM\"\n        }\n    elif selected_set.issubset(nasdaq_tech):\n        return {\n            \"NASDAQ Composite\": \"^IXIC\",\n            \"QQQ (Invesco QQQ ETF)\": \"QQQ\",\n            \"Custom\": \"CUSTOM\"\n        }\n    else:\n        return {\n            \"S&P 500\": \"^GSPC\",\n            \"NASDAQ Composite\": \"^IXIC\",\n            \"Dow Jones Industrial Average\": \"^DJI\",\n            \"Russell 2000\": \"^RUT\",\n            \"Custom\": \"CUSTOM\"\n        }\n\n@handle_exceptions\ndef calculate_tracking_error(returns: pd.Series, benchmark_returns: pd.Series) -> float:\n    \"\"\"\n    Calculate the Tracking Error of the portfolio relative to the benchmark.\n    \n    Parameters:\n    - returns (pd.Series): Daily returns of the portfolio.\n    - benchmark_returns (pd.Series): Daily returns of the benchmark.\n    \n    Returns:\n    - float: Tracking Error.\n    \"\"\"\n    if returns.empty or benchmark_returns.empty:\n        return np.nan\n    return np.std(returns - benchmark_returns) * np.sqrt(252)\n\n@handle_exceptions\ndef calculate_performance_attribution(returns: pd.Series, weights: list) -> pd.DataFrame:\n    \"\"\"\n    Calculate performance attribution based on asset contributions.\n\n    Parameters:\n    - returns (pd.Series or pd.DataFrame): Returns of assets.\n    - weights (list): Allocation weights for each asset.\n\n    Returns:\n    - pd.DataFrame: DataFrame containing asset names and their contribution percentages.\n    \"\"\"\n    if isinstance(returns, pd.Series):\n        returns = returns.to_frame('Asset')  # Convert Series to DataFrame with a default column name\n\n    if len(weights) != len(returns.columns):\n        st.error(f\"Number of weights ({len(weights)}) does not match number of assets ({len(returns.columns)}).\")\n        return pd.DataFrame()\n\n    annual_returns = returns.mean() * 252\n    contributions = annual_returns * weights\n    attribution_df = pd.DataFrame({\n        'Asset': returns.columns,\n        'Contribution (%)': (contributions / contributions.sum() * 100).round(2)\n    }).sort_values(by='Contribution (%)', ascending=False)\n    return attribution_df\n\ndef calculate_active_return(returns: pd.Series, benchmark_returns: pd.Series) -> float:\n    \"\"\"\n    Calculate the Active Return of the portfolio relative to the benchmark.\n\n    Parameters:\n    - returns (pd.Series): Daily returns of the portfolio.\n    - benchmark_returns (pd.Series): Daily returns of the benchmark.\n\n    Returns:\n    - float: Active Return percentage.\n    \"\"\"\n    if returns.empty or benchmark_returns.empty:\n        return np.nan\n    return (returns.mean() - benchmark_returns.mean()) * 252 * 100\n\ndef calculate_gain_loss_ratio(returns: pd.Series) -> float:\n    \"\"\"\n    Calculate the Gain/Loss Ratio of the portfolio.\n\n    Parameters:\n    - returns (pd.Series): Daily returns of the portfolio.\n\n    Returns:\n    - float: Gain/Loss Ratio.\n    \"\"\"\n    gains = returns[returns > 0].sum()\n    losses = -returns[returns < 0].sum()\n    return gains / losses if losses != 0 else np.nan\n\n@handle_exceptions\ndef calculate_capture_ratio(returns: pd.Series, benchmark_returns: pd.Series, upside: bool = True) -> float:\n    \"\"\"\n    Calculate the Upside or Downside Capture Ratio of the portfolio relative to the benchmark.\n    \n    Parameters:\n    - returns (pd.Series): Daily returns of the portfolio.\n    - benchmark_returns (pd.Series): Daily returns of the benchmark.\n    - upside (bool): If True, calculate Upside Capture Ratio; else, Downside.\n    \n    Returns:\n    - float: Capture Ratio.\n    \"\"\"\n    if returns.empty or benchmark_returns.empty:\n        return np.nan\n    if upside:\n        benchmark_positive = benchmark_returns > 0\n        if benchmark_positive.sum() == 0:\n            return np.nan\n        return (returns[benchmark_positive].mean() / benchmark_returns[benchmark_positive].mean()) * 100\n    else:\n        benchmark_negative = benchmark_returns < 0\n        if benchmark_negative.sum() == 0:\n            return np.nan\n        return (returns[benchmark_negative].mean() / benchmark_returns[benchmark_negative].mean()) * 100\n\ndef calculate_safe_withdrawal_rate(returns: pd.Series) -> float:\n    \"\"\"\n    Calculate the Safe Withdrawal Rate based on portfolio returns.\n\n    Parameters:\n    - returns (pd.Series): Daily returns of the portfolio.\n\n    Returns:\n    - float: Safe Withdrawal Rate percentage.\n    \"\"\"\n    if returns.empty:\n        return np.nan\n    return (returns.mean() / returns.std()) * 100\n\ndef calculate_perpetual_withdrawal_rate(returns: pd.Series) -> float:\n    \"\"\"\n    Calculate the Perpetual Withdrawal Rate based on portfolio returns.\n\n    Parameters:\n    - returns (pd.Series): Daily returns of the portfolio.\n\n    Returns:\n    - float: Perpetual Withdrawal Rate percentage.\n    \"\"\"\n    if returns.empty:\n        return np.nan\n    return (returns.mean() / returns.std()) * 100\n\ndef calculate_positive_periods(returns: pd.Series) -> str:\n    \"\"\"\n    Calculate the number and percentage of positive return periods.\n\n    Parameters:\n    - returns (pd.Series): Daily returns of the portfolio.\n\n    Returns:\n    - str: String describing positive periods.\n    \"\"\"\n    if returns.empty:\n        return \"N/A\"\n    positive = returns > 0\n    return f\"{positive.sum()} out of {len(returns)} ({(positive.sum()/len(returns))*100:.2f}%)\"\n\ndef calculate_modigliani_miller(returns: pd.Series, benchmark_returns: pd.Series, rf: float = 0.02) -> float:\n    \"\"\"\n    Calculate the Modigliani\u2013Modigliani Measure (M2).\n\n    Parameters:\n    - returns (pd.Series): Daily returns of the portfolio.\n    - benchmark_returns (pd.Series): Daily returns of the benchmark.\n    - rf (float): Risk-free rate (default is 2%).\n\n    Returns:\n    - float: M2 value.\n    \"\"\"\n    sharpe = calculate_sharpe_ratio(returns, rf)\n    alpha = calculate_alpha(returns, benchmark_returns, rf)\n    return alpha / sharpe if sharpe != 0 and not np.isnan(alpha) else np.nan\n\n@handle_exceptions\ndef get_drawdown_details(cum_returns: pd.Series) -> list:\n    \"\"\"\n    Generate detailed drawdown information.\n\n    Parameters:\n    - cum_returns (pd.Series): Cumulative returns of the portfolio.\n\n    Returns:\n    - list: List of dictionaries containing drawdown details.\n    \"\"\"\n    drawdowns = []\n    if cum_returns.empty:\n        return drawdowns\n\n    peak = cum_returns.iloc[0]\n    peak_date = cum_returns.index[0]\n    trough = cum_returns.iloc[0]\n    trough_date = cum_returns.index[0]\n\n    for date, value in cum_returns.items():\n        if value > peak:\n            if trough < peak:\n                drawdowns.append({\n                    'Start': peak_date.strftime('%Y-%m-%d'),\n                    'End': trough_date.strftime('%Y-%m-%d'),\n                    'Drawdown': f\"{((trough / peak) - 1) * 100:.2f}%\",\n                    'Date': trough_date  # Adding 'Date' as the end date\n                })\n            peak = value\n            peak_date = date\n            trough = value\n            trough_date = date\n        elif value < trough:\n            trough = value\n            trough_date = date\n\n    # Final drawdown\n    if trough < peak:\n        drawdowns.append({\n            'Start': peak_date.strftime('%Y-%m-%d'),\n            'End': trough_date.strftime('%Y-%m-%d'),\n            'Drawdown': f\"{((trough / peak) - 1) * 100:.2f}%\",\n            'Date': trough_date\n        })\n\n    # Sort drawdowns by severity (most negative first)\n    drawdowns_sorted = sorted(drawdowns, key=lambda x: float(x['Drawdown'].strip('%')), reverse=False)\n    return drawdowns_sorted[:10]  # Return top 10 most severe drawdowns\n\n@handle_exceptions\ndef optimize_portfolio(\n    returns: pd.DataFrame,\n    benchmark_returns: pd.Series = None,\n    objectives: list = ['sharpe'],\n    rf: float = 0.02,\n    max_weight: float = 1.0,\n    min_weight: float = 0.0,\n    target_return: float = None\n) -> np.ndarray:\n    \"\"\"\n    Optimize a portfolio based on specified objectives.\n\n    Parameters:\n    - returns (pd.DataFrame): Historical returns of assets.\n    - benchmark_returns (pd.Series, optional): Returns of the benchmark index.\n    - objectives (list): Objectives to optimize.\n        Options include 'sharpe', 'min_variance', 'max_return', 'min_drawdown', 'maximize_alpha', 'minimize_beta'.\n    - rf (float): Risk-free rate for alpha calculation.\n    - max_weight (float): Maximum weight per asset.\n    - min_weight (float): Minimum weight per asset.\n    - target_return (float, optional): Target return for the portfolio.\n\n    Returns:\n    - np.ndarray: Optimized asset weights or None if optimization fails.\n    \"\"\"\n    from scipy.optimize import minimize\n\n    def calculate_beta(portfolio_returns: pd.Series, benchmark_returns: pd.Series) -> float:\n        covariance = np.cov(portfolio_returns, benchmark_returns)\n        variance = covariance[1, 1]\n        return covariance[0, 1] / variance if variance != 0 else 0\n\n    def calculate_var(portfolio_returns: np.ndarray, confidence_level: float = 0.95) -> float:\n        return np.percentile(portfolio_returns, (1 - confidence_level) * 100)\n\n    def calculate_cvar(portfolio_returns: np.ndarray, confidence_level: float = 0.95) -> float:\n        var = calculate_var(portfolio_returns, confidence_level)\n        return portfolio_returns[portfolio_returns <= var].mean()\n\n    # Define individual objective functions\n    def sharpe_ratio(weights: np.ndarray) -> float:\n        portfolio_return = np.dot(returns.mean(), weights) * 252\n        portfolio_volatility = np.sqrt(np.dot(weights.T, np.dot(returns.cov() * 252, weights)))\n        return -portfolio_return / portfolio_volatility if portfolio_volatility > 1e-6 else 0  # Negative for maximization\n\n    def min_variance(weights: np.ndarray) -> float:\n        return np.dot(weights.T, np.dot(returns.cov() * 252, weights))\n\n    def max_return(weights: np.ndarray) -> float:\n        return -np.dot(returns.mean(), weights) * 252  # Negative for maximization\n\n    def min_drawdown(weights: np.ndarray) -> float:\n        portfolio_returns = returns.dot(weights)\n        cvar = calculate_cvar(portfolio_returns, 0.95)\n        return cvar  # Minimizing CVaR as a proxy for drawdown\n\n    # Initialize list of objective functions\n    objective_functions = []\n\n    # Map objectives to functions\n    for obj in objectives:\n        if obj == 'sharpe':\n            objective_functions.append(sharpe_ratio)\n        elif obj == 'min_variance':\n            objective_functions.append(min_variance)\n        elif obj == 'max_return':\n            objective_functions.append(max_return)\n        elif obj == 'min_drawdown':\n            objective_functions.append(min_drawdown)\n        elif obj == 'maximize_alpha':\n            if benchmark_returns is None:\n                st.error(\"benchmark_returns must be provided for 'maximize_alpha' objective.\")\n                return None\n\n            benchmark_return = benchmark_returns.mean() * 252  # Assuming daily returns annualized\n\n            def maximize_alpha(weights: np.ndarray) -> float:\n                portfolio_return = np.dot(returns.mean(), weights) * 252\n                portfolio_volatility = np.sqrt(np.dot(weights.T, np.dot(returns.cov() * 252, weights)))\n                portfolio_returns = returns.dot(weights)\n                beta = calculate_beta(portfolio_returns, benchmark_returns)\n                alpha = portfolio_return - (rf + beta * (benchmark_return - rf))\n                return -alpha  # Negative for maximization\n\n            objective_functions.append(maximize_alpha)\n        elif obj == 'minimize_beta':\n            if benchmark_returns is None:\n                st.error(\"benchmark_returns must be provided for 'minimize_beta' objective.\")\n                return None\n\n            def minimize_beta(weights: np.ndarray) -> float:\n                portfolio_returns = returns.dot(weights)\n                beta = calculate_beta(portfolio_returns, benchmark_returns)\n                return beta\n\n            objective_functions.append(minimize_beta)\n        else:\n            st.error(f\"Invalid optimization objective: {obj}\")\n            return None\n\n    # Composite objective function: weighted sum of individual objectives\n    def composite_objective(weights: np.ndarray) -> float:\n        return sum(fn(weights) for fn in objective_functions)\n\n    # Define constraints\n    constraints = [\n        {'type': 'eq', 'fun': lambda x: np.sum(x) - 1},\n    ]\n\n    if target_return is not None:\n        def target_return_constraint(x: np.ndarray) -> float:\n            return np.dot(x, returns.mean()) * 252 - target_return\n        constraints.append({\n            'type': 'ineq',\n            'fun': target_return_constraint\n        })\n\n    # Define bounds for weights\n    bounds = tuple((min_weight, max_weight) for _ in range(returns.shape[1]))\n\n    # Initial guess (equally distributed weights)\n    initial_guess = np.array([1.0 / returns.shape[1]] * returns.shape[1])\n\n    # Perform optimization\n    result = minimize(\n        composite_objective,\n        initial_guess,\n        method='SLSQP',\n        bounds=bounds,\n        constraints=constraints\n    )\n\n    if result.success:\n        optimized_weights = result.x\n        portfolio_volatility = np.sqrt(np.dot(optimized_weights.T, np.dot(returns.cov() * 252, optimized_weights)))\n        portfolio_return = np.dot(returns.mean(), optimized_weights) * 252\n        sharpe_ratio_val = (portfolio_return - rf) / portfolio_volatility if portfolio_volatility > 0 else np.nan\n\n        if portfolio_volatility <= 1e-6:\n            st.error(\"Optimized portfolio has near-zero volatility. Optimization constraints may be too restrictive.\")\n            return None\n\n        # Store optimized results in session state\n        st.session_state.backtest_results['optimized_weights'] = optimized_weights\n        st.session_state.backtest_results['optimized_return'] = portfolio_return\n        st.session_state.backtest_results['optimized_volatility'] = portfolio_volatility\n        st.session_state.backtest_results['optimized_sharpe'] = sharpe_ratio_val\n\n        return optimized_weights\n    else:\n        st.error(\"Optimization failed. Try adjusting your constraints or target return.\")\n        return None\n\ndef monte_carlo_simulation(\n    returns: pd.DataFrame,\n    num_simulations: int = 1000,\n    periods: int = 252,\n    mean_returns: pd.Series = None,\n    cov_matrix: pd.DataFrame = None,\n    mean_reversion: bool = False,\n    mean_reversion_speed: float = 0.1,\n    long_term_mean: pd.Series = None,\n    time_varying_vol: bool = False,\n    vol_change_rate: float = 0.0,\n    stress_shocks: dict = None,\n    stress_period: int = None,\n    return_distribution: str = 'normal'\n) -> np.ndarray:\n    \"\"\"\n    Perform Monte Carlo simulations for portfolio returns.\n\n    Parameters:\n    - returns (pd.DataFrame): Historical returns of assets.\n    - num_simulations (int): Number of simulation paths.\n    - periods (int): Number of periods to simulate.\n    - mean_returns (pd.Series, optional): Mean returns of assets.\n    - cov_matrix (pd.DataFrame, optional): Covariance matrix of asset returns.\n    - mean_reversion (bool): If True, applies mean reversion.\n    - mean_reversion_speed (float): Speed of mean reversion.\n    - long_term_mean (pd.Series, optional): Long-term mean for mean reversion.\n    - time_varying_vol (bool): If True, allows volatility to change over time.\n    - vol_change_rate (float): Rate at which volatility changes.\n    - stress_shocks (dict, optional): Dict of shocks to apply at specified period.\n    - stress_period (int, optional): Period at which to apply stress shocks.\n    - return_distribution (str): Distribution type ('normal' or 'log-normal').\n\n    Returns:\n    - np.ndarray: Array of simulated cumulative returns.\n    \"\"\"\n    if mean_returns is None:\n        mean_returns = returns.mean()\n    if cov_matrix is None:\n        cov_matrix = returns.cov()\n\n    num_assets = returns.shape[1]\n    available_selected = returns.columns.tolist()\n\n    def simulate():\n        weights = np.random.random(num_assets)\n        weights /= np.sum(weights)\n        simulated_prices = [1]  # Initial price\n        current_mean = mean_returns.copy()\n        current_vol = np.sqrt(np.diag(cov_matrix))\n        for t in range(periods):\n            if mean_reversion and long_term_mean is not None:\n                current_mean += mean_reversion_speed * (long_term_mean - current_mean)\n            if time_varying_vol:\n                current_vol += vol_change_rate\n                current_vol = np.clip(current_vol, 0, None)  # Ensure volatility doesn't go negative\n\n            adjusted_cov_matrix = np.outer(current_vol, current_vol) * np.corrcoef(returns.T)\n            if return_distribution == 'log-normal':\n                simulated_returns = np.random.lognormal(mean=np.log(1 + current_mean), sigma=current_vol) - 1\n            else:\n                simulated_returns = np.random.multivariate_normal(current_mean, adjusted_cov_matrix)\n\n            # Apply stress shocks if applicable\n            if stress_shocks and t == stress_period:\n                for idx, ticker in enumerate(available_selected):\n                    simulated_returns[idx] += stress_shocks.get(ticker, 0)\n\n            portfolio_return = np.dot(simulated_returns, weights)\n            simulated_prices.append(simulated_prices[-1] * (1 + portfolio_return))\n        cumulative_return = simulated_prices[-1] - 1\n        return cumulative_return\n\n    portfolio_returns = Parallel(n_jobs=-1)(\n        delayed(simulate)() for _ in range(num_simulations)\n    )\n\n    return np.array(portfolio_returns)\n\n@handle_exceptions\ndef plot_efficient_frontier(returns: pd.DataFrame, num_portfolios: int = 1000, rf: float = 0.0, portfolio_name='Portfolio') -> None:\n    \"\"\"\n    Plot an enhanced Efficient Frontier with optimized portfolio marked.\n\n    Parameters:\n    - returns (pd.DataFrame): Historical returns of assets.\n    - num_portfolios (int): Number of portfolios to simulate.\n    - rf (float): Risk-free rate for Sharpe Ratio calculation.\n    - portfolio_name (str): Name of the portfolio being optimized.\n    \"\"\"\n    from scipy.optimize import minimize\n\n    num_assets = returns.shape[1]\n\n    def generate_portfolio_metrics(weights: np.ndarray) -> tuple:\n        portfolio_return = np.dot(returns.mean(), weights) * 252\n        portfolio_volatility = np.sqrt(np.dot(weights.T, np.dot(returns.cov() * 252, weights)))\n        sharpe_ratio = (portfolio_return - rf) / portfolio_volatility if portfolio_volatility > 0 else 0\n        return portfolio_volatility, portfolio_return, sharpe_ratio\n\n    # Generate random portfolios\n    weights_list = np.random.dirichlet(np.ones(num_assets), num_portfolios)\n    portfolio_metrics = np.array([generate_portfolio_metrics(w) for w in weights_list])\n\n    # Filter out invalid portfolios\n    portfolio_metrics = portfolio_metrics[~np.isnan(portfolio_metrics).any(axis=1)]\n\n    if portfolio_metrics.size == 0:\n        st.warning(\"No valid portfolios to plot on the Efficient Frontier.\")\n        return\n\n    ef_df = pd.DataFrame({\n        'Std Dev': portfolio_metrics[:, 0],\n        'Return': portfolio_metrics[:, 1],\n        'Sharpe Ratio': portfolio_metrics[:, 2]\n    })\n    fig = px.scatter(\n        ef_df,\n        x='Std Dev',\n        y='Return',\n        color='Sharpe Ratio',\n        color_continuous_scale='Turbo',\n        title='\ud83d\udcc8 Efficient Frontier',\n        labels={\n            'Std Dev': 'Annualized Volatility (Std Dev)',\n            'Return': 'Annualized Return (%)',\n            'Sharpe Ratio': 'Sharpe Ratio'\n        },\n        hover_data={\n            'Sharpe Ratio': ':.2f',\n            'Std Dev': ':.2f',\n            'Return': ':.2f'\n        },\n        template=THEME\n    )\n\n    # Highlight the portfolio with the maximum Sharpe ratio\n    max_sharpe_idx = ef_df['Sharpe Ratio'].idxmax()\n    max_sharpe = ef_df.loc[max_sharpe_idx]\n    fig.add_trace(go.Scatter(\n        x=[max_sharpe['Std Dev']],\n        y=[max_sharpe['Return']],\n        mode='markers+text',\n        marker=dict(color='gold', size=12, symbol='star'),\n        name='Max Sharpe Ratio',\n        text=[\"Max Sharpe\"],\n        textposition=\"top center\",\n        hoverinfo='text'\n    ))\n    fig.add_annotation(\n        x=max_sharpe['Std Dev'],\n        y=max_sharpe['Return'],\n        text=\"Max Sharpe\",\n        showarrow=True,\n        arrowhead=1,\n        ax=0,\n        ay=-40\n    )\n\n    # Add optimized portfolio if available\n    optimized_weights = st.session_state.backtest_results.get('optimized_weights', None)\n    optimized_return = st.session_state.backtest_results.get('optimized_return', None)\n    optimized_volatility = st.session_state.backtest_results.get('optimized_volatility', None)\n    optimized_sharpe = st.session_state.backtest_results.get('optimized_sharpe', None)\n\n    if all(v is not None for v in [optimized_weights, optimized_return, optimized_volatility, optimized_sharpe]):\n        if optimized_volatility > 1e-6 and np.isfinite(optimized_sharpe):\n            fig.add_trace(go.Scatter(\n                x=[optimized_volatility],\n                y=[optimized_return],\n                mode='markers+text',\n                marker=dict(color='red', size=16, symbol='diamond'),\n                name=f'{portfolio_name} Optimized',\n                text=[\"Optimized\"],\n                textposition=\"top center\",\n                hoverinfo='text'\n            ))\n            fig.add_annotation(\n                x=optimized_volatility,\n                y=optimized_return,\n                text=\"Optimized\",\n                showarrow=True,\n                arrowhead=2,\n                ax=0,\n                ay=-50\n            )\n\n    fig.update_layout(\n        hovermode=HOVERMODE,\n        xaxis=dict(showgrid=True, title=dict(text='Annualized Volatility (Std Dev)')),\n        yaxis=dict(showgrid=True, title=dict(text='Annualized Return (%)')),\n        legend=dict(title=\"Portfolio\", x=0.01, y=0.99)\n    )\n    fig.update_traces(marker=dict(line=dict(width=1, color='DarkSlateGrey')), selector=dict(mode='markers'))\n    st.plotly_chart(fig, use_container_width=True, config={'responsive': True, 'scrollZoom': True})\n\n@handle_exceptions\ndef generate_portfolio(returns: pd.DataFrame, rf: float = 0.0) -> tuple:\n    \"\"\"\n    Generate a random portfolio and calculate its metrics.\n\n    Parameters:\n    - returns (pd.DataFrame): Historical returns of assets.\n    - rf (float): Risk-free rate for Sharpe Ratio calculation.\n\n    Returns:\n    - tuple: (volatility: float, return: float, sharpe_ratio: float)\n    \"\"\"\n    try:\n        weights = np.random.dirichlet(np.ones(returns.shape[1]))\n        portfolio_return = np.dot(returns.mean(), weights) * 252\n        portfolio_volatility = np.sqrt(np.dot(weights.T, np.dot(returns.cov() * 252, weights)))\n        sharpe_ratio = (portfolio_return - rf) / portfolio_volatility if portfolio_volatility > 0 else 0\n        return portfolio_volatility, portfolio_return, sharpe_ratio\n    except Exception as e:\n        st.error(f\"Error generating portfolio: {e}\")\n        return np.nan, np.nan, np.nan  # Return NaNs to indicate failure\n\ndef plot_allocation_pie(weights: list, assets: list, title: str = '\ud83e\udd67 Portfolio Allocation', hover_info: str = \"percent+name\") -> None:\n    \"\"\"\n    Plots the portfolio allocation pie chart.\n    \n    Parameters:\n    - weights (list): Allocation weights for each asset.\n    - assets (list): List of asset ticker symbols.\n    - title (str): Title of the pie chart.\n    - hover_info (str): Information to display on hover ('percent+name', etc.).\n    \n    Returns:\n    - None\n    \"\"\"\n    allocation_df = pd.DataFrame({\n        'Asset': assets,\n        'Weight': weights\n    })\n    \n    # Determine hover data based on hover_info\n    if hover_info == \"percent+name\":\n        hover_data = {'Weight': ':.2f%'}\n    elif hover_info == \"name\":\n        hover_data = {}\n    else:\n        hover_data = {'Weight': ':.2f%'}\n    \n    fig = px.pie(\n        allocation_df,\n        names='Asset',\n        values='Weight',\n        title=title,\n        hover_data=hover_data,\n        hole=0.3  # Donut chart appearance\n    )\n    \n    fig.update_traces(textposition='inside', textinfo='percent+label')\n    fig.update_layout(\n        uniformtext_minsize=12,\n        uniformtext_mode='hide',\n        legend_title=\"Assets\",\n        showlegend=True,\n        height=FIG_HEIGHT\n    )\n    \n    st.plotly_chart(fig, use_container_width=True, height=600, config={'responsive': True})\n\ndef plot_rolling_cagr(cum_returns: pd.Series, window: int = 252) -> None:\n    \"\"\"\n    Plot Rolling CAGR over a specified window.\n\n    Parameters:\n    - cum_returns (pd.Series): Cumulative returns of the portfolio.\n    - window (int): Window size for rolling CAGR calculation.\n    \"\"\"\n    try:\n        rolling_years = window / 252  # Assuming daily data\n        rolling_cagr = (cum_returns / cum_returns.shift(window)) ** (1 / rolling_years) - 1\n        fig = px.line(\n            rolling_cagr.dropna(),\n            title=f'Rolling {int(rolling_years)}-Year CAGR',\n            labels={'index': 'Date', 'value': 'Rolling CAGR'},\n            template=THEME\n        )\n        st.plotly_chart(fig, use_container_width=True)\n    except Exception as e:\n        st.error(f\"Error plotting rolling CAGR: {e}\")\n\n@handle_exceptions\ndef plot_rolling_metrics(returns: pd.Series, windows: list = [30, 90, 180, 252]) -> None:\n    \"\"\"\n    Plot rolling metrics (Volatility and Sharpe Ratio) over multiple windows.\n\n    Parameters:\n    - returns (pd.Series): Daily returns of the portfolio.\n    - windows (list): List of window sizes for rolling calculations.\n    \"\"\"\n    metrics = {}\n    for window in windows:\n        rolling_return = returns.rolling(window).mean() * 252\n        rolling_vol = returns.rolling(window).std() * np.sqrt(252)\n        rolling_sharpe = (rolling_return - 0.02) / rolling_vol\n        metrics[f'Rolling {window}-Day Volatility'] = rolling_vol\n        metrics[f'Rolling {window}-Day Sharpe Ratio'] = rolling_sharpe\n    fig = go.Figure()\n    for metric_name, metric_series in metrics.items():\n        fig.add_trace(go.Scatter(x=metric_series.index, y=metric_series, mode='lines', name=metric_name))\n    fig.update_layout(\n        title='Rolling Metrics Over User-Defined Periods',\n        xaxis_title='Date',\n        yaxis_title='Value',\n        hovermode='x unified',\n        template=THEME\n    )\n    st.plotly_chart(fig, use_container_width=True)\n\n@handle_exceptions\ndef plot_risk_return_attribution(returns: pd.Series, weights: list) -> None:\n    \"\"\"\n    Plot Risk-Return Attribution for the portfolio.\n\n    Parameters:\n    - returns (pd.Series or pd.DataFrame): Returns of assets.\n    - weights (list): Allocation weights for each asset.\n    \"\"\"\n    try:\n        if isinstance(returns, pd.Series):\n            asset_returns = returns.mean() * 252\n            asset_volatility = returns.std() * np.sqrt(252)\n            asset_contribution = weights[0] * asset_volatility if len(weights) > 0 else 0\n            hover_names = [returns.name] if returns.name else ['Asset']\n            x = [asset_volatility]\n            y = [asset_returns]\n            size = [asset_contribution]\n            color = [weights[0]]\n        else:\n            asset_returns = returns.mean() * 252\n            asset_volatility = returns.std() * np.sqrt(252)\n            asset_contribution = np.array(weights) * asset_volatility\n            hover_names = returns.columns.tolist()\n            x = asset_volatility.values\n            y = asset_returns.values\n            size = asset_contribution\n            color = weights\n\n        fig = px.scatter(\n            x=x,\n            y=y,\n            size=size,\n            color=color,\n            hover_name=hover_names,\n            title='Risk-Return Attribution',\n            labels={'x': 'Annualized Volatility', 'y': 'Annualized Return', 'color': 'Weight (%)'},\n            size_max=60,\n            color_continuous_scale='Viridis'\n        )\n        fig.update_layout(template='plotly_dark')\n        st.plotly_chart(fig, use_container_width=True)\n    except Exception as e:\n        st.error(f\"Error plotting risk-return attribution: {e}\")\n\ndef plot_var_cvar_distribution(portfolio_returns: np.ndarray, var: float, cvar: float) -> None:\n    \"\"\"\n    Plot the distribution of portfolio returns with VaR and CVaR lines.\n\n    Parameters:\n    - portfolio_returns (np.ndarray): Simulated portfolio returns.\n    - var (float): Value at Risk.\n    - cvar (float): Conditional Value at Risk.\n    \"\"\"\n    fig = px.histogram(portfolio_returns, nbins=50, title='Returns Distribution with VaR and CVaR',\n                       labels={'value': 'Returns', 'count': 'Frequency'})\n    fig.add_vline(x=var, line_dash=\"dash\", line_color=\"red\", annotation_text=f\"VaR: {var:.2f}\", annotation_position=\"top left\")\n    fig.add_vline(x=cvar, line_dash=\"dash\", line_color=\"blue\", annotation_text=f\"CVaR: {cvar:.2f}\", annotation_position=\"top left\")\n    st.plotly_chart(fig, use_container_width=True)\n\ndef plot_var_cvar_over_time(portfolio_returns: pd.Series, var: float, cvar: float) -> None:\n    \"\"\"\n    Plot cumulative returns with VaR and CVaR over time.\n\n    Parameters:\n    - portfolio_returns (pd.Series): Daily returns of the portfolio.\n    - var (float): Value at Risk.\n    - cvar (float): Conditional Value at Risk.\n    \"\"\"\n    cumulative_returns = (1 + portfolio_returns).cumprod()\n    fig = px.line(cumulative_returns, title='Cumulative Returns with VaR and CVaR Over Time', labels={'value': 'Cumulative Returns', 'index': 'Date'})\n    fig.add_hline(y=var, line_dash=\"dash\", line_color=\"red\", annotation_text=f\"VaR: {var:.2f}\", annotation_position=\"bottom right\")\n    fig.add_hline(y=cvar, line_dash=\"dash\", line_color=\"blue\", annotation_text=f\"CVaR: {cvar:.2f}\", annotation_position=\"bottom right\")\n    st.plotly_chart(fig, use_container_width=True)\n\n# ----------------------------\n# Caching Functions\n# ----------------------------\n\n@st.cache_data(show_spinner=False, persist=True)\ndef get_tickers() -> pd.DataFrame:\n    \"\"\"\n    Fetch and combine S&P 500 and NASDAQ-100 tickers from Wikipedia.\n\n    Returns:\n    - pd.DataFrame: DataFrame containing combined tickers and company names.\n    \"\"\"\n    try:\n        # Fetch S&P 500 companies\n        sp500_url = 'https://en.wikipedia.org/wiki/List_of_S%26P_500_companies'\n        sp500_response = requests.get(sp500_url, verify=False)\n        sp500_table = pd.read_html(sp500_response.text)[0]\n        sp500 = sp500_table[['Symbol', 'Security']].rename(columns={'Symbol': 'Ticker', 'Security': 'Company Name'})\n        sp500['Ticker'] = sp500['Ticker'].str.replace('.', '-', regex=False)\n\n        # Fetch NASDAQ-100 companies\n        nasdaq100_url = 'https://en.wikipedia.org/wiki/NASDAQ-100'\n        nasdaq100_response = requests.get(nasdaq100_url, verify=False)\n        nasdaq100_tables = pd.read_html(nasdaq100_response.text)\n\n        nasdaq100 = pd.DataFrame()\n        for table in nasdaq100_tables:\n            if 'Ticker' in table.columns and 'Company' in table.columns:\n                nasdaq100 = table[['Ticker', 'Company']].rename(columns={'Ticker': 'Ticker', 'Company': 'Company Name'})\n                nasdaq100['Ticker'] = nasdaq100['Ticker'].str.replace('.', '-', regex=False)\n                break\n\n        combined = pd.concat([sp500, nasdaq100], ignore_index=True)\n        combined = combined.drop_duplicates(subset=['Ticker'])\n        return combined.sort_values('Ticker').reset_index(drop=True)\n    except Exception as e:\n        st.error(f\"Error fetching tickers: {e}\")\n        return pd.DataFrame(columns=['Ticker', 'Company Name'])\n\n@st.cache_data(show_spinner=False, persist=True)\ndef download_data(tickers: list, start: str, end: str, retries: int = 3, backoff_factor: float = 0.3) -> pd.DataFrame:\n    \"\"\"\n    Download historical adjusted closing prices for specified tickers.\n\n    Parameters:\n    - tickers (list): List of ticker symbols.\n    - start (str): Start date in 'YYYY-MM-DD' format.\n    - end (str): End date in 'YYYY-MM-DD' format.\n    - retries (int): Number of retries for failed requests.\n    - backoff_factor (float): Backoff factor for retries.\n\n    Returns:\n    - pd.DataFrame: DataFrame containing adjusted closing prices.\n    \"\"\"\n    try:\n        # Configure retry strategy for requests (used by yfinance internally)\n        session = requests.Session()\n        retry = Retry(\n            total=retries,\n            read=retries,\n            connect=retries,\n            backoff_factor=backoff_factor,\n            status_forcelist=(500, 502, 504),\n        )\n        adapter = HTTPAdapter(max_retries=retry)\n        session.mount('http://', adapter)\n        session.mount('https://', adapter)\n\n        # Attempt to download data\n        data = yf.download(tickers, start=start, end=end, progress=False, session=session)['Adj Close']\n\n        # Handle potential empty data\n        if isinstance(data, pd.Series):\n            data = data.to_frame()\n        if data.empty:\n            st.warning(\"No price data available for the selected portfolio. Please check the ticker symbols and date range.\")\n            return pd.DataFrame()\n\n        if data.index.tz is not None:\n            data.index = data.index.tz_localize(None)\n\n        # Fill missing data\n        data_filled = data.fillna(method='ffill').fillna(method='bfill')\n\n        # Check for remaining missing values\n        if data_filled.isnull().values.any():\n            missing_count = data_filled.isnull().sum().sum()\n            st.warning(f\"Data contains {missing_count} missing values after filling. Some calculations may be affected.\")\n            # Optionally, you can drop remaining missing values or handle them as needed\n            data_filled = data_filled.dropna()\n\n            if data_filled.empty:\n                st.error(\"All data was missing after filling. Please revise ticker selections or date range.\")\n                return pd.DataFrame()\n\n        return data_filled\n    except Exception as e:\n        st.error(f\"Error downloading data: {e}\")\n        return pd.DataFrame()\n\n@handle_exceptions\ndef calculate_metrics(\n    returns: pd.Series, \n    cum_returns: pd.Series, \n    rf: float = 0.02, \n    benchmark_returns: pd.Series = None\n) -> dict:\n    \"\"\"\n    Calculate portfolio performance metrics without benchmark comparisons.\n    \n    Args:\n        returns (pd.Series): Portfolio returns\n        cum_returns (pd.Series): Cumulative returns\n        rf (float): Risk-free rate\n        benchmark_returns (pd.Series, optional): Removed as benchmark is no longer used\n    \n    Returns:\n        dict: Dictionary of metrics with consistent data types\n    \"\"\"\n    metrics = {}\n    \n    # Initialize with default values\n    initial_balance = 10000.0\n    metrics['Start Balance'] = initial_balance\n    \n    def safe_calculation(calculation, default=\"N/A\"):\n        \"\"\"Helper function to safely perform calculations\"\"\"\n        try:\n            result = calculation()\n            if isinstance(result, (int, float)):\n                if not np.isinf(result) and not np.isnan(result):\n                    return result\n            return default\n        except Exception:\n            return default\n    \n    if not cum_returns.empty and not returns.empty:\n        # Basic portfolio metrics\n        end_balance = safe_calculation(\n            lambda: initial_balance * cum_returns.iloc[-1]\n        )\n        years = (cum_returns.index[-1] - cum_returns.index[0]).days / 365.25\n\n        metrics.update({\n            'End Balance': end_balance,\n            'Annualized Return (CAGR)': safe_calculation(\n                lambda: round(calculate_cagr(cum_returns, rf) * 100, 2) if years > 0 else \"N/A\"\n            ),\n            'Best Year': safe_calculation(\n                lambda: round(\n                    returns.resample('Y').apply(lambda x: (1 + x).prod() - 1).max() * 100, \n                    2\n                )  # Percentage\n            ),\n            'Worst Year': safe_calculation(\n                lambda: round(\n                    returns.resample('Y').apply(lambda x: (1 + x).prod() - 1).min() * 100, \n                    2\n                )  # Percentage\n            ),\n            'Standard Deviation (Annualized)': safe_calculation(\n                lambda: round(returns.std() * np.sqrt(252) * 100, 2)  # Percentage\n            ),\n            'Maximum Drawdown': safe_calculation(\n                lambda: round(calculate_drawdown(cum_returns) * 100, 2)  # Percentage\n            ),\n            'Sharpe Ratio': safe_calculation(\n                lambda: round(calculate_sharpe_ratio(returns, rf), 2)\n            ),\n            'Sortino Ratio': safe_calculation(\n                lambda: round(calculate_sortino_ratio(returns, rf), 2)\n            ),\n            'Calmar Ratio': safe_calculation(\n                lambda: round(calculate_calmar_ratio(returns, cum_returns, rf), 2) if cum_returns is not None else \"N/A\"\n            )\n        })\n    else:\n        # Set default values for empty data\n        default_metrics = {\n            'End Balance': \"N/A\",\n            'Annualized Return (CAGR)': \"N/A\",\n            'Best Year': \"N/A\",\n            'Worst Year': \"N/A\",\n            'Standard Deviation (Annualized)': \"N/A\",\n            'Maximum Drawdown': \"N/A\",\n            'Sharpe Ratio': \"N/A\",\n            'Sortino Ratio': \"N/A\",\n            'Calmar Ratio': \"N/A\"\n        }\n        metrics.update(default_metrics)\n    \n    # Ensure all metrics are present even if benchmark_returns is None\n    expected_metrics = [\n        'Start Balance', 'End Balance', 'Annualized Return (CAGR)', 'Best Year',\n        'Worst Year', 'Standard Deviation (Annualized)', 'Maximum Drawdown',\n        'Sharpe Ratio', 'Sortino Ratio', 'Calmar Ratio'\n    ]\n\n    for metric in expected_metrics:\n        if metric not in metrics:\n            # Assign \"N/A\" for metrics that are percentage-based or related to performance\n            if any(key in metric for key in [\n                'Percentage', 'Return', 'Drawdown', 'Best Year', 'Worst Year'\n            ]):\n                metrics[metric] = \"N/A\"\n            else:\n                metrics[metric] = \"N/A\"\n\n    return metrics\n\n# ----------------------------\n# Additional Helper Functions\n# ----------------------------\n\n@handle_exceptions\ndef plot_efficient_frontier_comparison(\n    simulated_metrics: pd.DataFrame,\n    optimized_metrics: tuple\n) -> None:\n    \"\"\"\n    Plot Efficient Frontier and mark the optimized portfolio.\n\n    Parameters:\n    - simulated_metrics (pd.DataFrame): Simulated portfolio metrics.\n    - optimized_metrics (tuple): Metrics of the optimized portfolio.\n    \"\"\"\n    if simulated_metrics.empty:\n        st.warning(\"No simulated metrics available to plot.\")\n        return\n\n    fig = px.scatter(\n        simulated_metrics,\n        x='Std Dev',\n        y='Return',\n        color='Sharpe Ratio',\n        color_continuous_scale='Viridis',\n        title='Efficient Frontier',\n        labels={\n            'Std Dev': 'Annualized Volatility (Std Dev)',\n            'Return': 'Annualized Return',\n            'Sharpe Ratio': 'Sharpe Ratio'\n        },\n        hover_data={\n            'Sharpe Ratio': ':.2f',\n            'Std Dev': ':.2f',\n            'Return': ':.2f'\n        },\n        template=THEME\n    )\n\n    # Add optimized portfolio if available\n    if optimized_metrics:\n        vol, ret, sharpe = optimized_metrics\n        if vol > 0 and np.isfinite(sharpe):\n            fig.add_trace(go.Scatter(\n                x=[vol],\n                y=[ret],\n                mode='markers+text',\n                marker=dict(color='red', size=14, symbol='star'),\n                name='Optimized Portfolio',\n                text=[\"Optimized\"],\n                textposition=\"top center\",\n                hoverinfo='text'\n            ))\n            fig.add_annotation(\n                x=vol,\n                y=ret,\n                text=\"Optimized\",\n                showarrow=True,\n                arrowhead=2,\n                ax=0,\n                ay=-40\n            )\n\n    fig.update_layout(\n        hovermode='x unified',\n        xaxis=dict(rangeslider=dict(visible=True), type='linear'),\n    )\n    st.plotly_chart(fig, use_container_width=True, config={\n        'responsive': True,\n        'scrollZoom': True,\n        'displayModeBar': True\n    })\n\n# ----------------------------\n# End of Helper Functions\n# ----------------------------\n\n# ----------------------------\n# Streamlit Layout\n# ----------------------------\nst.set_page_config(page_title=\"\ud83c\udfaf Portfolio Optimizer\", layout=\"wide\")\n\n# Display loading spinner\nwith st.spinner(\"\ud83c\udfaf Portfolio Optimizer is loading, please be patient...\"):\n    # Simulate loading delay if necessary\n    pass\n\nnav_steps = [\n    {\"name\": \"Configure Portfolio\", \"icon\": \"\ud83d\udcc1\"},\n    {\"name\": \"Run Backtest\", \"icon\": \"\ud83d\udcca\"},\n    {\"name\": \"Optimize Portfolio\", \"icon\": \"\ud83d\udd27\"},\n    {\"name\": \"Monte Carlo Simulations\", \"icon\": \"\ud83d\udcc8\"},\n    {\"name\": \"Risk Analysis\", \"icon\": \"\u26a0\ufe0f\"}\n]\n\nwith st.sidebar:\n    st.header(\"\ud83d\udcc2 Navigation\")\n    # Create a list of (label, value) tuples for radio buttons\n    nav_options = [(f\"{step['icon']} {step['name']}\", step['name']) for step in nav_steps]\n    labels, values = zip(*nav_options)\n    \n    # Use radio buttons for direct navigation with a callback\n    def set_step():\n        selected_label = st.session_state.nav_radio  # Access the selected label from session_state\n        selected_idx = labels.index(selected_label)\n        st.session_state.step = values[selected_idx]\n    \n    selected_label = st.radio(\"Select a Step:\", options=labels, key=\"nav_radio\", on_change=set_step)\n\ncurrent_step = st.session_state.step\n\ntickers = get_tickers()\n# Define asset_options by formatting ticker symbols with company names\nasset_options = tickers.apply(\n    lambda row: format_asset_option(row['Ticker'], row['Company Name']), \n    axis=1\n).tolist()\n\nif current_step == \"Configure Portfolio\":\n    st.title(\"\ud83c\udfaf Portfolio Optimizer\")\n    # Display success message after deletion\n    if 'delete_success' in st.session_state:\n        st.success(st.session_state.delete_success)\n        if st.session_state.get('show_balloons', False):\n            st.balloons()\n            st.session_state.show_balloons = False  # Reset balloon flag\n        del st.session_state.delete_success\n    # Display success message after editing\n    if 'edit_success' in st.session_state:\n        st.success(st.session_state.edit_success)\n        del st.session_state.edit_success\n    \n    # Initialize show_proceed in session state\n    if 'show_proceed' not in st.session_state:\n        st.session_state.show_proceed = False\n        \n    # Define callback function to hide welcome message\n    def hide_welcome():\n        st.session_state.show_proceed = True\n        st.session_state.selected_subtab = \"\u2795 Create New Portfolio\"  # Set default tab\n\n    # Display onboarding message and button if not proceeded\n    if not st.session_state.show_proceed:\n        st.info(\"Welcome! Let's get you started with your first portfolio.\")\n        st.write(\"\"\"\n            1. **Configure your portfolio** by entering a name, start date, end date, and other details.\n            2. **Select the assets** you want to include and set their allocations.\n            3. **Run a backtest** to see how your portfolio performs.\n            4. **Compare your portfolio** with a benchmark or another portfolio.\n            5. **Explore the performance metrics** and visualizations.\n        \"\"\")\n        st.button(\"Got U, Let's Explore\", key=\"explore_button\", on_click=hide_welcome)\n    # ----------------------------\n    # Configure Portfolio\n    # ----------------------------\n\n    if st.session_state.add_new_portfolio:\n        if st.session_state.add_new_portfolio:\n            if st.session_state.edit_portfolio:\n                st.header(f\"\u270f\ufe0f Editing {st.session_state.edit_portfolio} Portfolio\")\n            else:\n                st.header(\"\u2795 Create New Portfolio\")\n        \n        # Initialize or fetch the portfolio being edited\n        if st.session_state.edit_portfolio:\n            # Editing existing portfolio\n            portfolio_to_edit = next((p for p in st.session_state.portfolios if p['name'] == st.session_state.edit_portfolio), None)\n            if portfolio_to_edit:\n                new_portfolio = portfolio_to_edit.copy()\n                st.text_input(\"\ud83d\udcdb Portfolio Name\", value=new_portfolio['name'], key=\"portfolio_name\")\n                start_date_input = st.date_input(\n                    \"\ud83d\udcc5 Start Date:\",\n                    value=new_portfolio['start_date'],\n                    min_value=datetime(1900, 1, 1),\n                    help=\"Choose the start date for backtesting.\"\n                )\n                end_date_input = st.date_input(\n                    \"\ud83d\udcc5 End Date:\",\n                    value=new_portfolio['end_date'],\n                    min_value=datetime(1900, 1, 1),\n                    help=\"Choose the end date for backtesting.\"\n                )\n                rf_rate = st.number_input(\n                    \"\ud83d\udcc8 Risk-Free Rate (%)\",\n                    min_value=0.0,\n                    max_value=10.0,\n                    value=new_portfolio['rf_rate'] * 100,\n                    format=\"%.2f\",\n                    help=\"Enter the risk-free rate as a percentage.\"\n                ) / 100\n                broker_fee = st.number_input(\n                    \"\ud83d\udcb8 Broker Fee (%)\",\n                    min_value=0.0,\n                    max_value=10.0,\n                    value=new_portfolio['broker_fee'] * 100,\n                    step=0.0001,\n                    format=\"%.4f\",\n                    help=\"Enter the broker fee as a percentage per transaction.\"\n                ) / 100\n                rebalance_freq = st.selectbox(\n                    \"\ud83d\udd04 Rebalance Frequency\",\n                    options=list(FREQUENCY_MAPPING.keys()),\n                    index=list(FREQUENCY_MAPPING.values()).index(new_portfolio['rebalance_freq']),\n                    help=\"Choose how often the portfolio should be rebalanced.\"\n                )\n                selected_assets_new = st.multiselect(\n                    \"\ud83d\uddc2\ufe0f Select Assets:\",\n                    options=asset_options,\n                    format_func=lambda x: f\"{x} - {get_company_name(tickers, x)}\",\n                    default=[f\"{ticker} - {get_company_name(tickers, ticker)}\" for ticker in new_portfolio['selected']],\n                    help=\"Choose the assets you want to include in your portfolio.\"\n                )\n            else:\n                st.error(\"\u274c Selected portfolio to edit was not found.\")\n                st.session_state.add_new_portfolio = False\n                st.session_state.edit_portfolio = None\n                st.stop()\n        else:\n            # Creating a new portfolio\n            st.text_input(\"\ud83d\udcdb Portfolio Name\", key=\"portfolio_name\")\n            start_date_input = st.date_input(\n                \"\ud83d\udcc5 Start Date:\",\n                value=datetime.today() - timedelta(days=365 * 15),\n                min_value=datetime(1900, 1, 1),\n                help=\"Choose the start date for backtesting.\"\n            )\n            end_date_input = st.date_input(\n                \"\ud83d\udcc5 End Date:\",\n                value=datetime.today(),\n                min_value=datetime(1900, 1, 1),\n                help=\"Choose the end date for backtesting.\"\n            )\n            rf_rate = st.number_input(\n                \"\ud83d\udcc8 Risk-Free Rate (%)\",\n                min_value=0.0,\n                max_value=10.0,\n                value=st.session_state.default_config['rf_rate'] * 100,\n                format=\"%.2f\",\n                help=\"Enter the risk-free rate as a percentage.\"\n            ) / 100\n            broker_fee = st.number_input(\n                \"\ud83d\udcb8 Broker Fee (%)\",\n                min_value=0.0,\n                max_value=10.0,\n                value=st.session_state.default_config['broker_fee'] * 100,\n                step=0.0001,\n                format=\"%.4f\",\n                help=\"Enter the broker fee as a percentage per transaction.\"\n            ) / 100\n            rebalance_freq = st.selectbox(\n                \"\ud83d\udd04 Rebalance Frequency\",\n                options=list(FREQUENCY_MAPPING.keys()),\n                index=list(FREQUENCY_MAPPING.keys()).index('Monthly'),\n                help=\"Choose how often the portfolio should be rebalanced.\"\n            )\n            selected_assets_new = st.multiselect(\n                \"\ud83d\uddc2\ufe0f Select Assets:\",\n                options=asset_options,\n                format_func=lambda x: f\"{x} - {get_company_name(tickers, x)}\",\n                default=[],\n                help=\"Choose the assets you want to include in your portfolio.\"\n            )\n        \n        if selected_assets_new:\n            # Extract tickers from selected assets\n            selected_tickers_new = [option.split(' - ')[0] for option in selected_assets_new if ' - ' in option]\n            # Asset Allocation\n            st.subheader(\"\ud83d\udcb0 Asset Allocation\")\n            allocation_df = pd.DataFrame({\n                'Ticker': selected_tickers_new,\n                'Allocation (%)': [100.0 / len(selected_tickers_new)] * len(selected_tickers_new)\n            })\n            allocations = []\n            cols = st.columns(len(selected_tickers_new))\n            for idx, ticker in enumerate(selected_tickers_new):\n                with cols[idx]:\n                    allocation = st.number_input(\n                        f\"{ticker} (%)\",\n                        min_value=0.0,\n                        max_value=100.0,\n                        value=100.0 / len(selected_tickers_new),\n                        step=0.1,\n                        key=f\"alloc_{ticker}\",\n                        help=f\"Set the allocation percentage for {ticker}.\"\n                    )\n                    allocations.append(allocation)\n                    allocation_df.at[idx, 'Allocation (%)'] = allocation\n        \n            total_allocation_new = sum(allocations)\n            st.markdown(f\"**\ud83e\uddee Total Allocation:** {total_allocation_new:.2f}%\")\n        \n            if not np.isclose(total_allocation_new, 100.0, atol=1e-2):\n                st.warning(\"\u26a0\ufe0f Allocations must sum to 100%. Please adjust the allocations.\")\n        \n        else:\n            st.info(\"\ud83e\uddd0 Select at least one asset to allocate your portfolio.\")\n        \n        # Submit Button\n        submit_button_label = \"\u2705 Update Portfolio\" if st.session_state.edit_portfolio else \"\u2705 Add Portfolio\"\n        if st.button(submit_button_label):\n            # Validation\n            if not st.session_state.portfolio_name:\n                st.error(\"\u274c Please provide a portfolio name.\")\n            elif not selected_assets_new:\n                st.error(\"\u274c Please select at least one asset.\")\n            elif not np.isclose(total_allocation_new, 100.0, atol=1e-2):\n                st.error(f\"\u274c Allocations must sum to 100%. Currently sum to {total_allocation_new:.2f}%.\")\n            else:\n                # Prepare the new or updated portfolio\n                new_portfolio = {\n                    'name': st.session_state.portfolio_name,\n                    'start_date': pd.to_datetime(start_date_input),\n                    'end_date': pd.to_datetime(end_date_input),\n                    'rf_rate': rf_rate,\n                    'broker_fee': broker_fee,\n                    'rebalance_freq': FREQUENCY_MAPPING.get(rebalance_freq, 'M'),\n                    'selected': selected_tickers_new,\n                    'allocations': allocations\n                }\n        \n                if st.session_state.edit_portfolio:\n                    # Update existing portfolio\n                    for idx, p in enumerate(st.session_state.portfolios):\n                        if p['name'] == st.session_state.edit_portfolio:\n                            st.session_state.portfolios[idx] = new_portfolio\n                            break\n                    st.success(f\"\ud83c\udf89 Portfolio '{new_portfolio['name']}' updated successfully!\")\n                    st.session_state.edit_portfolio = None  # Reset edit mode\n                else:\n                    # Add as a new portfolio\n                    if new_portfolio['name'] in [p['name'] for p in st.session_state.portfolios]:\n                        st.error(\"\u274c Portfolio name already exists. Please choose a unique name.\")\n                    else:\n                        st.session_state.portfolios.append(new_portfolio)\n                        st.success(f\"\ud83c\udf89 Portfolio '{new_portfolio['name']}' added successfully!\")\n        \n                st.balloons()\n                st.session_state.add_new_portfolio = False  # Hide the form\n                st.rerun()\n        \n        if st.button(\"\u274c Cancel\"):\n            st.session_state.add_new_portfolio = False\n            st.session_state.edit_portfolio = None\n            st.rerun()\n    \n    # Show portfolio management interface if proceeded\n    elif st.session_state.show_proceed:\n        # Show existing portfolios if any\n        if st.session_state.portfolios:\n            st.subheader(\"\ud83d\udcc1 Manage Existing Portfolios\")\n            portfolio_names = [p['name'] for p in st.session_state.portfolios]\n            selected_portfolio = st.selectbox(\"\ud83d\udd0d Select a Portfolio\", portfolio_names)\n            \n            if selected_portfolio:\n                portfolio = next(p for p in st.session_state.portfolios if p['name'] == selected_portfolio)\n                \n                with st.expander(f\"\u2139\ufe0f Details for **{selected_portfolio}**\"):\n                    st.write(f\"**Start Date:** {portfolio['start_date'].strftime('%Y-%m-%d')}\")\n                    st.write(f\"**End Date:** {portfolio['end_date'].strftime('%Y-%m-%d')}\")\n                    st.write(f\"**Rebalance Frequency:** {rebalance_freq_inverse_mapping.get(portfolio['rebalance_freq'], portfolio['rebalance_freq'])}\")\n                    st.write(\"**Allocations:**\")\n                    allocation_df = pd.DataFrame({\n                        'Ticker': portfolio['selected'],\n                        'Allocation (%)': portfolio['allocations']\n                    })\n                    st.table(allocation_df)\n                \n                # Action Buttons with Icons\n                action_col0, action_col1, action_col2, action_col3 = st.columns(4)\n\n                with action_col0:\n                    if st.button(\"\u2795 Create New Portfolio\", key=\"create_new_portfolio\"):\n                        st.session_state.add_new_portfolio = True\n                        st.rerun()\n\n                with action_col1:\n                    if st.button(f\"\ud83d\uddd1\ufe0f Delete {selected_portfolio}\", key=f\"delete_{selected_portfolio}\"):\n                        st.session_state.portfolios = [p for p in st.session_state.portfolios if p['name'] != selected_portfolio]\n                        st.session_state.delete_success = f\"\u2705 Portfolio '{selected_portfolio}' deleted successfully.\"\n                        st.session_state.show_balloons = True  # Optional: To control balloon display\n                        st.rerun()\n\n                with action_col2:\n                    if st.button(f\"\u270f\ufe0f Edit {selected_portfolio}\", key=f\"edit_{selected_portfolio}\"):\n                        st.session_state.edit_portfolio = selected_portfolio\n                        st.session_state.add_new_portfolio = True  # Ensure the form is visible\n                        st.rerun()\n\n                with action_col3:\n                    if st.button(f\"\ud83d\udcc4 Duplicate {selected_portfolio}\", key=f\"duplicate_{selected_portfolio}\"):\n                        duplicated_portfolio = portfolio.copy()\n                        duplicated_portfolio['name'] = f\"{selected_portfolio}_Copy\"\n                        # Ensure the new name is unique\n                        if duplicated_portfolio['name'] in portfolio_names:\n                            counter = 1\n                            while f\"{selected_portfolio}_Copy{counter}\" in portfolio_names:\n                                counter += 1\n                            duplicated_portfolio['name'] = f\"{selected_portfolio}_Copy{counter}\"\n                        st.session_state.portfolios.append(duplicated_portfolio)\n                        st.success(f\"\ud83c\udf89 Portfolio '{duplicated_portfolio['name']}' duplicated successfully!\")\n                        st.rerun()\n        \n        st.markdown(\"---\")  # Separator line for clarity\n\n    # ----------------------------\n    # Documentation\n    # ----------------------------\n    with st.expander(\"\ud83d\udcd6 Documentation\"):  \n        st.markdown(\"\"\"\n        ### Documentation\n\n        **Portfolio Configuration:**\n        - **Portfolio Name:** Enter a unique name for your portfolio.\n        - **Risk-Free Rate:** The theoretical return of an investment with zero risk, often based on government bonds.\n        - **Sharpe Ratio:** Measures the performance of an investment compared to a risk-free asset, after adjusting for its risk.\n            *Formula:* $$\\\\frac{R_p - R_f}{\\\\sigma_p}$$\n        - **VaR (Value at Risk):** Estimates the maximum potential loss over a specific time frame at a given confidence level.\n        - **CVaR (Conditional Value at Risk):** The expected loss exceeding the VaR, providing insight into tail risk.\n        - **Sortino Ratio:** Similar to the Sharpe Ratio but only penalizes downside volatility.\n            *Formula:* $$\\\\frac{R_p - R_f}{\\\\sigma_d}$$\n        - **Maximum Drawdown:** The largest peak-to-trough decline in the portfolio's value over a specific period.\n        - **Alpha:** Measures the active return on an investment compared to a market index.\n            *Formula:* $$R_p - [R_f + \\\\beta (R_m - R_f)]$$\n        - **Beta:** Indicates the volatility of an investment relative to the market.\n            *Formula:* $$\\\\beta = \\\\frac{Cov(R_p, R_m)}{Var(R_m)}$$\n        - **Information Ratio:** Measures portfolio returns beyond the returns of a benchmark, adjusted for the volatility of those returns.\n            *Formula:* $$\\\\frac{R_p - R_b}{TE}$$\n        - **Gain/Loss Ratio:** The ratio of total gains to total losses in the portfolio.\n        - **Modigliani\u2013Modigliani Measure (M\u00b2):** Adjusts the portfolio return to the risk of a benchmark, allowing for comparison.\n            *Formula:* $$M\u00b2 = \\\\text{Sharpe Ratio}_{\\\\text{Portfolio}} \\\\times \\\\sigma_{\\\\text{Benchmark}} + R_f$$\n        - **Tracking Error:** Measures the standard deviation of the difference between portfolio returns and benchmark returns.\n        - **Upside/Downside Capture Ratio:** Measures how well the portfolio captures the benchmark's positive and negative movements respectively.\n        - **Risk Factor Attribution:** Decomposes portfolio returns based on exposure to different risk factors like Momentum, Value, and Size.\n        - **Performance Attribution:** Breaks down portfolio performance by individual assets, showing each asset's contribution to overall returns.\n        - **Recommendation Engine:** Provides suggestions for portfolio adjustments to optimize performance based on current allocations and objectives.\n        \"\"\")\n\nif current_step == \"Run Backtest\":\n        st.title(\"\ud83d\udcca Run Backtest\")\n        if not st.session_state.portfolios:\n            st.warning(\"Please add at least one portfolio to run backtest.\")\n        else:\n            st.subheader(\"\ud83d\udd0d Select Portfolios to Compare\")\n            comparison_type = st.selectbox(\n                \"Comparison Type\",\n                [\"Portfolio vs Benchmark\", \"Portfolio vs Portfolio\"],\n                key=\"comparison_type\"\n            )\n\n            portfolio_names = [p['name'] for p in st.session_state.portfolios]\n\n            # Dynamic labels based on comparison_type\n            portfolio1_label = \"Select Portfolio\" if comparison_type == \"Portfolio vs Benchmark\" else \"Select First Portfolio\"\n            portfolio2_disabled = comparison_type == \"Portfolio vs Benchmark\"\n\n            portfolio1 = st.selectbox(\n                portfolio1_label,\n                portfolio_names,\n                key=\"p1\"\n            )\n\n            portfolio2 = st.selectbox(\n                \"Select Second Portfolio\",\n                portfolio_names,\n                key=\"p2\",\n                disabled=portfolio2_disabled\n            )\n\n            run = st.button(\"Run Backtest\")\n            if run:\n                with st.spinner(\"Running backtest...\"):\n                    comparison_type = \"Portfolio vs Portfolio\"\n                    if comparison_type == \"Portfolio vs Portfolio\":\n                        if portfolio1 == portfolio2:\n                            st.error(\"Please select two different portfolios for comparison.\")\n                            st.stop()\n                        try:\n                            portfolio_a = next(p for p in st.session_state.portfolios if p['name'] == portfolio1)\n                            portfolio_b = next(p for p in st.session_state.portfolios if p['name'] == portfolio2)\n                        except StopIteration as e:\n                            st.error(\"One of the selected portfolios was not found.\")\n                            st.stop()\n\n                        result = run_portfolio_comparison(\"vs_portfolio\", portfolio_a, portfolio_b)\n\n                        if result.get(\"error\"):\n                            st.error(result[\"error\"])\n                        else:\n                            display_backtest_results(result[\"backtest_results\"], comparison_type, portfolio1, portfolio2)\n\nif current_step == \"Optimize Portfolio\":\n    st.title(\"\ud83d\udd27 Optimize Portfolio\")\n    \n    if not st.session_state.portfolios:\n        st.warning(\"Please add at least one portfolio to optimize.\")\n    else:\n        portfolio_names = [p['name'] for p in st.session_state.portfolios]\n        selected_portfolio_name = st.selectbox(\"\ud83d\udcc1 Select Portfolio to Optimize\", portfolio_names)\n        portfolio = next(p for p in st.session_state.portfolios if p['name'] == selected_portfolio_name)\n        \n        # Download and prepare data\n        price_data = download_data(\n            portfolio['selected'],\n            portfolio['start_date'],\n            portfolio['end_date']\n        )\n        \n        if price_data.empty:\n            st.error(\"No price data available for the selected portfolio.\")\n        else:\n            available_selected = [ticker for ticker in portfolio['selected'] if ticker in price_data.columns]\n            missing_selected = list(set(portfolio['selected']) - set(available_selected))\n            if missing_selected:\n                st.warning(f\"Excluded tickers with no data: {', '.join(missing_selected)}\")\n            \n            if not available_selected:\n                st.error(\"No selected tickers have available data for the chosen date range.\")\n            else:\n                returns = price_data[available_selected].pct_change().dropna()\n                \n                st.header(\"\ud83d\ude80 Portfolio Optimization Settings\")\n                \n                # Constraints Section\n                with st.expander(\"\u2699\ufe0f Constraints\", expanded=True):\n                    constraints_col1, constraints_col2 = st.columns(2)\n                    \n                    with constraints_col1:\n                        max_weight = st.slider(\n                            \"\ud83d\udcc8 Maximum Weight per Asset (%)\",\n                            min_value=0.0,\n                            max_value=100.0,\n                            value=100.0,\n                            step=0.1,\n                            help=\"Set the maximum allocation percentage for any single asset.\"\n                        ) / 100  # Convert to decimal\n                \n                    with constraints_col2:\n                        min_weight = st.slider(\n                            \"\ud83d\udcc9 Minimum Weight per Asset (%)\",\n                            min_value=0.0,\n                            max_value=100.0,\n                            value=0.0,\n                            step=0.1,\n                            help=\"Set the minimum allocation percentage for any single asset.\"\n                        ) / 100  # Convert to decimal\n                \n                    # Validation: Ensure min_weight does not exceed max_weight\n                    if min_weight > max_weight:\n                        st.error(\"\u26a0\ufe0f **Minimum weight cannot exceed Maximum weight.** Please adjust your settings.\")\n                \n                # Objectives Section\n                with st.expander(\"\ud83c\udfaf Objectives\", expanded=True):\n                    objective_options = {\n                        \"Maximize Sharpe Ratio\": \"sharpe\",\n                        \"Minimize Variance\": \"min_variance\",\n                        \"Maximize Return\": \"max_return\",\n                        \"Minimize Drawdown\": \"min_drawdown\",\n                        \"Maximize Alpha\": \"maximize_alpha\",\n                        \"Minimize Beta\": \"minimize_beta\"\n                    }\n                    selected_objectives = st.multiselect(\n                        \"Select Optimization Objectives\",\n                        options=list(objective_options.keys()),\n                        default=[\"Maximize Sharpe Ratio\"],\n                        help=\"Choose one or more objectives to optimize your portfolio.\"\n                    )\n                    \n                    if not selected_objectives:\n                        st.warning(\"\u26a0\ufe0f Please select at least one optimization objective.\")\n                    \n                # Target Return Section\n                with st.expander(\"\ud83c\udfaf Target Return\", expanded=True):\n                    target_return = st.number_input(\n                        \"\ud83d\udcc8 Target Annual Return (%)\",\n                        min_value=0.0,\n                        max_value=100.0,\n                        value=10.0,\n                        step=0.1,\n                        help=\"Set the target annual return for portfolio optimization.\"\n                    ) / 100  # Convert to decimal\n                \n                # Optimization Trigger\n                if st.button(\"\u2705 Run Optimization\"):\n                    if not selected_objectives:\n                        st.error(\"\u274c Please select at least one optimization objective.\")\n                    else:\n                        # Map objectives to their internal codes\n                        objective_map = {v: k for k, v in objective_options.items()}\n                        objectives_selected = [objective_options[obj] for obj in selected_objectives]\n                        \n                        with st.spinner(\"\ud83e\uddee Optimizing portfolio...\"):\n                                # Run optimization\n                                optimized_weights = optimize_portfolio(\n                                    returns,\n                                    objectives=objectives_selected,\n                                    rf=portfolio['rf_rate'],\n                                    max_weight=max_weight,\n                                    min_weight=min_weight,\n                                    target_return=target_return\n                                )\n    \n                                if optimized_weights is not None:\n                                    # Ensure allocations sum to 100%\n                                    total_allocation = sum(optimized_weights)\n                                    if not np.isclose(total_allocation, 1.0, atol=1e-4):\n                                        st.warning(f\"\ud83d\udd04 Allocations sum to {total_allocation*100:.2f}%. Adjusting proportionally.\")\n                                        optimized_weights = [w / total_allocation for w in optimized_weights]\n                                    \n                                    # Display Optimized Weights\n                                    st.subheader(\"\ud83d\udcca Optimized Portfolio Allocation\")\n                                    weights_df = pd.DataFrame({\n                                        'Ticker': available_selected,\n                                        'Allocation (%)': [f\"{w*100:.2f}\" for w in optimized_weights]\n                                    })\n                                    st.table(weights_df)\n    \n                                    # Plot Allocation Pie Chart\n                                    plot_allocation_pie(\n                                        optimized_weights,\n                                        available_selected,\n                                        title=\"\ud83e\udd67 Optimized Allocation\",\n                                        hover_info=\"percent+name\"\n                                    )\n    \n                                    # Display Performance Metrics\n                                    st.subheader(\"\ud83d\udcc8 Optimized Portfolio Performance Metrics\")\n                                    optimized_return = np.dot(optimized_weights, returns.mean()) * 252\n                                    optimized_volatility = np.sqrt(np.dot(optimized_weights, np.dot(returns.cov() * 252, optimized_weights)))\n                                    optimized_sharpe = (optimized_return - portfolio['rf_rate']) / optimized_volatility if optimized_volatility > 0 else np.nan\n    \n                                    optimized_metrics = {\n                                        'Expected Annual Return (%)': f\"{optimized_return * 100:.2f}%\",\n                                        'Annualized Volatility (%)': f\"{optimized_volatility * 100:.2f}%\",\n                                        'Sharpe Ratio': f\"{optimized_sharpe:.2f}\"\n                                    }\n    \n                                    optimized_metrics_df = pd.DataFrame(list(optimized_metrics.items()), columns=['Metric', 'Value'])\n                                    st.table(optimized_metrics_df)\n    \n                                    # Store optimized weights and metrics in session state\n                                    st.session_state.backtest_results.update({\n                                        'optimized_weights': optimized_weights,\n                                        'optimized_return': optimized_return,\n                                        'optimized_volatility': optimized_volatility,\n                                        'optimized_sharpe': optimized_sharpe\n                                    })\n    \n                                    st.success(\"\ud83c\udf89 Portfolio optimization completed successfully!\")\n                                else:\n                                    st.error(\"\u274c Optimization did not return any weights.\")\n    #else:\n        #st.warning(\"Please add at least one portfolio to optimize.\")\n\nif current_step == \"Monte Carlo Simulations\":\n    st.title(\"\ud83d\udcc8 Monte Carlo Simulations\")\n    \n    if st.session_state.portfolios:\n        portfolio_names = [p['name'] for p in st.session_state.portfolios]\n        selected_portfolio = st.selectbox(\"\ud83d\udcc1 Select Portfolio for Simulation\", portfolio_names)\n        portfolio = next(p for p in st.session_state.portfolios if p['name'] == selected_portfolio)\n        \n        # Download and prepare data\n        price_data = download_data(\n            portfolio['selected'],\n            portfolio['start_date'],\n            portfolio['end_date']\n        )\n        \n        if price_data.empty:\n            st.error(\"No price data available for the selected portfolio.\")\n        else:\n            available_selected = [ticker for ticker in portfolio['selected'] if ticker in price_data.columns]\n            missing_selected = list(set(portfolio['selected']) - set(available_selected))\n            if missing_selected:\n                st.warning(f\"Excluded tickers with no data: {', '.join(missing_selected)}\")\n            \n            if not available_selected:\n                st.error(\"No selected tickers have available data for the chosen date range.\")\n            else:\n                returns = price_data[available_selected].pct_change().dropna()\n                \n                # Organize simulation settings and results into tabs\n                simulation_tabs = st.tabs([\"\ud83d\udd27 Settings\", \"\ud83d\udcca Results\"])\n                \n                # Settings Tab\n                with simulation_tabs[0]:\n                    st.header(\"\ud83d\udd27 Simulation Settings\")\n                    \n                    # Simulation Parameters\n                    sim_col1, sim_col2 = st.columns(2)\n                    \n                    with sim_col1:\n                        num_simulations = st.number_input(\n                            \"\ud83d\udd22 Number of Simulations\",\n                            min_value=100,\n                            max_value=10000,\n                            value=1000,\n                            step=100,\n                            help=\"Define how many simulation paths to generate.\"\n                        )\n                        periods = st.number_input(\n                            \"\ud83d\udcc6 Number of Periods\",\n                            min_value=1,\n                            max_value=252,\n                            value=252,\n                            step=1,\n                            help=\"Set the number of periods (e.g., days) for each simulation.\"\n                        )\n                    \n                    with sim_col2:\n                        return_distribution = st.selectbox(\n                            \"\ud83d\udcca Return Distribution\",\n                            options=[\"Normal\", \"Log-Normal\"],\n                            index=0,\n                            help=\"Choose the statistical distribution for asset returns.\"\n                        )\n                        enable_mean_reversion = st.checkbox(\"\ud83d\udd04 Enable Mean Reversion\", help=\"Implement mean reversion in simulated returns.\")\n                    \n                    # Mean Reversion Parameters\n                    if enable_mean_reversion:\n                        mr_col1, mr_col2 = st.columns(2)\n                        with mr_col1:\n                            mean_reversion_speed = st.slider(\n                                \"\u26a1 Mean Reversion Speed\",\n                                min_value=0.0,\n                                max_value=1.0,\n                                value=0.1,\n                                step=0.01,\n                                help=\"Adjust the speed at which returns revert to the mean.\"\n                            )\n                        with mr_col2:\n                            long_term_mean_input = st.number_input(\n                                \"\ud83d\udcc8 Long-term Mean Return (%)\",\n                                value=0.0,\n                                step=0.1,\n                                help=\"Set the target mean return for mean reversion.\"\n                            ) / 100  # Convert to decimal\n                \n                    # Time-Varying Volatility\n                    enable_time_varying_vol = st.checkbox(\"\ud83d\udcc9 Enable Time-Varying Volatility\", help=\"Allow volatility to change over time in simulations.\")\n                    if enable_time_varying_vol:\n                        vol_change_rate = st.slider(\n                            \"\ud83d\udcc8 Volatility Change Rate\",\n                            min_value=-0.5,\n                            max_value=0.5,\n                            value=0.0,\n                            step=0.01,\n                            help=\"Set the rate at which volatility changes each period.\"\n                        )\n                \n                    # Stress Testing\n                    st.subheader(\"\u26a0\ufe0f Stress Testing Parameters\")\n                    enable_stress_testing = st.checkbox(\"\u26a0\ufe0f Enable Stress Testing\")\n                    stress_shocks = {}\n                    if enable_stress_testing:\n                        selected_stress_assets = st.multiselect(\n                            \"\ud83d\udd0d Select Assets to Apply Stress Shocks\",\n                            options=available_selected,\n                            help=\"Choose assets to apply specific stress shocks.\"\n                        )\n                        for ticker in selected_stress_assets:\n                            shock = st.number_input(\n                                f\"\ud83d\udca5 Stress Shock to {ticker} (%)\",\n                                min_value=-100.0,\n                                max_value=100.0,\n                                value=0.0,\n                                step=0.1,\n                                help=f\"Define the percentage shock for {ticker}.\"\n                            ) / 100  # Convert to decimal\n                            stress_shocks[ticker] = shock\n                \n                    # Sensitivity Analysis (Optional)\n                    st.subheader(\"\ud83d\udd04 Sensitivity Analysis Parameters\")\n                    enable_sensitivity_analysis = st.checkbox(\"\ud83d\udd0e Enable Sensitivity Analysis\", help=\"Assess how changes in market factors affect portfolio risk.\")\n                    sensitivity_adjustments = {}\n                    if enable_sensitivity_analysis:\n                        sensitivity_col1, sensitivity_col2 = st.columns(2)\n                        with sensitivity_col1:\n                            interest_rate_change = st.number_input(\n                                \"\ud83d\udcb9 Change in Interest Rates (bps)\",\n                                value=0.0,\n                                step=0.1,\n                                help=\"Specify the change in interest rates in basis points.\"\n                            ) / 10000  # Convert to decimal\n                        with sensitivity_col2:\n                            inflation_rate_change = st.number_input(\n                                \"\ud83d\udcc8 Change in Inflation Rates (bps)\",\n                                value=0.0,\n                                step=0.1,\n                                help=\"Specify the change in inflation rates in basis points.\"\n                            ) / 10000  # Convert to decimal\n                        \n                        for ticker in available_selected:\n                            col_a, col_b = st.columns(2)\n                            with col_a:\n                                interest_sens = st.number_input(\n                                    f\"\ud83d\udcca Interest Rate Sensitivity for {ticker}\",\n                                    value=1.0,\n                                    step=0.1,\n                                    help=f\"Set sensitivity of {ticker} to interest rate changes.\"\n                                )\n                            with col_b:\n                                inflation_sens = st.number_input(\n                                    f\"\ud83d\udcca Inflation Rate Sensitivity for {ticker}\",\n                                    value=1.0,\n                                    step=0.1,\n                                    help=f\"Set sensitivity of {ticker} to inflation rate changes.\"\n                                )\n                            adjustment = interest_sens * interest_rate_change + inflation_sens * inflation_rate_change\n                            sensitivity_adjustments[ticker] = adjustment\n                \n                    # Simulation Trigger\n                    if st.button(\"\u2705 Run Simulations\"):\n                        with st.spinner(\"\ud83e\uddee Running Monte Carlo simulations...\"):\n                            simulated_returns = monte_carlo_simulation(\n                                returns=returns, \n                                num_simulations=int(num_simulations), \n                                periods=int(periods),\n                                mean_returns=returns.mean(), \n                                cov_matrix=returns.cov(),\n                                mean_reversion=enable_mean_reversion,\n                                mean_reversion_speed=mean_reversion_speed if enable_mean_reversion else 0.0,\n                                long_term_mean=np.full(len(available_selected), long_term_mean_input / 252) if enable_mean_reversion else None,\n                                time_varying_vol=enable_time_varying_vol,\n                                vol_change_rate=vol_change_rate if enable_time_varying_vol else 0.0,\n                                stress_shocks=stress_shocks if enable_stress_testing else {},\n                                return_distribution=return_distribution.lower()\n                            )\n                            st.session_state.simulated_returns = simulated_returns\n                            st.success(\"\ud83c\udf89 Simulations completed successfully!\")\n                \n                # Results Tab\n                with simulation_tabs[1]:\n                    if 'simulated_returns' in st.session_state:\n                        simulated_returns = st.session_state.simulated_returns\n                        st.header(\"\ud83d\udcca Simulation Results\")\n        \n                        # Descriptive Statistics\n                        st.subheader(\"\ud83d\udcc8 Descriptive Statistics\")\n                        st.write(pd.Series(simulated_returns).describe())\n        \n                        # Histogram\n                        st.subheader(\"\ud83d\udcca Returns Distribution\")\n                        fig_hist = px.histogram(\n                            simulated_returns,\n                            nbins=50,\n                            title='\ud83d\udcca Simulated Returns Distribution',\n                            labels={'value': 'Simulated Returns', 'count': 'Frequency'},\n                            template=THEME\n                        )\n                        st.plotly_chart(fig_hist, use_container_width=True)\n        \n                        # Cumulative Distribution Function (CDF)\n                        st.subheader(\"\ud83d\udcc9 Cumulative Distribution Function (CDF)\")\n                        fig_cdf = px.ecdf(\n                            simulated_returns,\n                            title='\ud83d\udcc9 Cumulative Distribution of Simulated Returns',\n                            labels={'value': 'Simulated Returns', 'cumcount': 'CDF'},\n                            template=THEME\n                        )\n                        st.plotly_chart(fig_cdf, use_container_width=True)\n        \n                        # Box Plot\n                        st.subheader(\"\ud83d\udce6 Box Plot of Simulated Returns\")\n                        fig_box = px.box(\n                            pd.DataFrame(simulated_returns, columns=['Returns']),\n                            y='Returns',\n                            title='\ud83d\udce6 Box Plot of Simulated Returns',\n                            template=THEME\n                        )\n                        st.plotly_chart(fig_box, use_container_width=True)\n        \n                        # Summary Statistics Table\n                        st.subheader(\"\ud83d\udcdd Summary Statistics\")\n                        summary_stats = pd.DataFrame({\n                            'Metric': ['Mean', 'Median', 'Standard Deviation', 'Minimum', 'Maximum'],\n                            'Value': [\n                                f\"{np.mean(simulated_returns):.4f}\",\n                                f\"{np.median(simulated_returns):.4f}\",\n                                f\"{np.std(simulated_returns):.4f}\",\n                                f\"{np.min(simulated_returns):.4f}\",\n                                f\"{np.max(simulated_returns):.4f}\"\n                            ]\n                        })\n                        st.table(summary_stats)\n        \n                        # Interpretation\n                        st.markdown(\"\"\"\n                        **\ud83d\udcd6 Interpretation:**\n                        - **Descriptive Statistics:** Provides an overview of the distribution of simulated portfolio returns.\n                        - **Histogram:** Visualizes the frequency distribution of returns.\n                        - **CDF:** Shows the probability that a return is less than or equal to a particular value.\n                        - **Box Plot:** Highlights the median, quartiles, and potential outliers in the return distribution.\n                        - **Summary Statistics:** Summarizes key metrics from the simulation runs.\n                        \"\"\")\n                    else:\n                        st.info(\"\ud83d\udd0d Run simulations to view results here.\")\n    #else:\n        #st.warning(\"Please add at least one portfolio to run Monte Carlo simulations.\")\n\n# Main Risk Analysis Section\nif current_step == \"Risk Analysis\":\n    st.title(\"\u26a0\ufe0f Risk Analysis\")\n    \n    if st.session_state.portfolios:\n        portfolio_names = [p['name'] for p in st.session_state.portfolios]\n        selected_portfolio = st.selectbox(\"\ud83d\udcc1 Select Portfolio for Risk Analysis\", portfolio_names)\n        portfolio = next(p for p in st.session_state.portfolios if p['name'] == selected_portfolio)\n        \n        # Download and prepare data\n        price_data = download_data(\n            portfolio['selected'],\n            portfolio['start_date'],\n            portfolio['end_date']\n        )\n        \n        if price_data.empty:\n            st.error(\"No price data available for the selected portfolio.\")\n        else:\n            available_selected = [ticker for ticker in portfolio['selected'] if ticker in price_data.columns]\n            missing_selected = list(set(portfolio['selected']) - set(available_selected))\n            if missing_selected:\n                st.warning(f\"Excluded tickers with no data: {', '.join(missing_selected)}\")\n            \n            if not available_selected:\n                st.error(\"No selected tickers have available data for the chosen date range.\")\n            else:\n                returns = price_data[available_selected].pct_change().dropna()\n                \n                # Organize risk analysis into tabs\n                risk_analysis_tabs = st.tabs([\"\ud83d\udcc9 Risk Metrics\", \"\ud83d\udcc8 Visualizations\", \"\ud83d\udee0\ufe0f Sensitivity Analysis\"])\n                \n                # Risk Metrics Tab\n                with risk_analysis_tabs[0]:\n                    st.header(\"\ud83d\udcc9 Risk Metrics\")\n                    \n                    # Risk Parameters\n                    risk_params_col1, risk_params_col2 = st.columns(2)\n                    \n                    with risk_params_col1:\n                        confidence_level = st.number_input(\n                            \"\ud83d\udd0d Confidence Level for VaR/CVaR (%)\",\n                            min_value=90.0,\n                            max_value=99.0,\n                            value=95.0,\n                            step=1.0,\n                            help=\"Set the confidence level for risk metrics like VaR and CVaR.\"\n                        ) / 100  # Convert to decimal\n                    \n                    with risk_params_col2:\n                        enable_stress_testing = st.checkbox(\"\u26a0\ufe0f Enable Stress Testing\", help=\"Apply specific shocks to assets to assess portfolio resilience.\")\n                    \n                    if enable_stress_testing:\n                        st.markdown(\"#### \u26a0\ufe0f Stress Testing Shocks\")\n                        stress_shocks = {}\n                        for ticker in available_selected:\n                            shock = st.number_input(\n                                f\"\ud83d\udca5 Stress Shock to {ticker} (%)\",\n                                min_value=-100.0,\n                                max_value=100.0,\n                                value=0.0,\n                                step=0.1,\n                                help=f\"Define the percentage shock for {ticker}.\"\n                            ) / 100  # Convert to decimal\n                            stress_shocks[ticker] = shock\n                        if not any(shock != 0 for shock in stress_shocks.values()):\n                            st.warning(\"\u26a0\ufe0f No stress shocks applied. Consider adding at least one to perform stress testing.\")\n                    \n                    # Sensitivity Analysis Inputs\n                    st.subheader(\"\ud83d\udd0e Sensitivity Analysis Parameters\")\n                    enable_sensitivity_analysis = st.checkbox(\"\ud83d\udd04 Enable Sensitivity Analysis\", help=\"Assess how changes in market factors affect portfolio risk.\")\n                    sensitivity_adjustments = {}\n                    if enable_sensitivity_analysis:\n                        sensitivity_col1, sensitivity_col2 = st.columns(2)\n                        with sensitivity_col1:\n                            interest_rate_change = st.number_input(\n                                \"\ud83d\udcb9 Change in Interest Rates (bps)\",\n                                value=0.0,\n                                step=0.1,\n                                help=\"Specify the change in interest rates in basis points.\"\n                            ) / 10000  # Convert to decimal\n                        with sensitivity_col2:\n                            inflation_rate_change = st.number_input(\n                                \"\ud83d\udcc8 Change in Inflation Rates (bps)\",\n                                value=0.0,\n                                step=0.1,\n                                help=\"Specify the change in inflation rates in basis points.\"\n                            ) / 10000  # Convert to decimal\n                        \n                        for ticker in available_selected:\n                            col_a, col_b = st.columns(2)\n                            with col_a:\n                                interest_sens = st.number_input(\n                                    f\"\ud83d\udcca Interest Rate Sensitivity for {ticker}\",\n                                    value=1.0,\n                                    step=0.1,\n                                    help=f\"Set sensitivity of {ticker} to interest rate changes.\"\n                                )\n                            with col_b:\n                                inflation_sens = st.number_input(\n                                    f\"\ud83d\udcca Inflation Rate Sensitivity for {ticker}\",\n                                    value=1.0,\n                                    step=0.1,\n                                    help=f\"Set sensitivity of {ticker} to inflation rate changes.\"\n                                )\n                            adjustment = interest_sens * interest_rate_change + inflation_sens * inflation_rate_change\n                            sensitivity_adjustments[ticker] = adjustment\n                \n                    # Calculate Risk Metrics Button\n                    if st.button(\"\ud83d\udcca Calculate Risk Metrics\"):\n                        with st.spinner(\"\ud83e\uddee Calculating risk metrics...\"):\n                            # Apply stress shocks if enabled\n                            portfolio_returns = returns.copy()\n                            if enable_stress_testing:\n                                for ticker, shock in stress_shocks.items():\n                                    portfolio_returns[ticker] += shock\n                            \n                            # Apply sensitivity adjustments if enabled\n                            if enable_sensitivity_analysis:\n                                for ticker, adjustment in sensitivity_adjustments.items():\n                                    portfolio_returns[ticker] += adjustment\n                            \n                            # Ensure no extreme negative returns\n                            portfolio_returns = portfolio_returns.clip(lower=-1.0)\n                            \n                            # Calculate weighted portfolio returns\n                            weights = np.array(portfolio['allocations']) / 100\n                            portfolio_returns = portfolio_returns.dot(weights)\n                            \n                            # Store in session state\n                            st.session_state.portfolio_returns = portfolio_returns\n                            \n                            # Calculate metrics\n                            cum_returns = (1 + portfolio_returns).cumprod()\n                            var = calculate_var(portfolio_returns, confidence_level)\n                            cvar = calculate_cvar(portfolio_returns, confidence_level)\n                            skewness = skew(portfolio_returns)\n                            kurt = kurtosis(portfolio_returns)\n                            annual_vol = portfolio_returns.std() * np.sqrt(252)\n                            max_drawdown_val = calculate_drawdown(cum_returns) * 100\n                            \n                            # Store metrics\n                            risk_metrics = {\n                                'Value at Risk (VaR)': f\"{var * 100:.2f}%\",\n                                'Conditional Value at Risk (CVaR)': f\"{cvar * 100:.2f}%\",\n                                'Skewness': f\"{skewness:.2f}\",\n                                'Kurtosis': f\"{kurt:.2f}\",\n                                'Annualized Volatility (%)': f\"{annual_vol * 100:.2f}%\",\n                                'Maximum Drawdown (%)': f\"{max_drawdown_val:.2f}%\"\n                            }\n                            \n                            st.session_state.risk_metrics = risk_metrics\n                            st.success(\"\u2705 Risk metrics calculated successfully!\")\n        \n                # Visualizations Tab\n                with risk_analysis_tabs[1]:\n                    st.header(\"\ud83d\udcc8 Risk Visualizations\")\n                    \n                    if 'risk_metrics' in st.session_state:\n                        var = st.session_state.risk_metrics.get('Value at Risk (VaR)', None)\n                        cvar = st.session_state.risk_metrics.get('Conditional Value at Risk (CVaR)', None)\n                        portfolio_returns_local = st.session_state.portfolio_returns\n                        cum_returns = (1 + portfolio_returns_local).cumprod()\n                        \n                        # VaR and CVaR Plot\n                        st.subheader(\"\ud83d\udcc9 Cumulative Returns with VaR and CVaR\")\n                        fig_cum = px.line(\n                            cum_returns,\n                            x=cum_returns.index,\n                            y=cum_returns,\n                            title='\ud83d\udcc9 Cumulative Returns Over Time',\n                            labels={'y': 'Cumulative Returns', 'x': 'Date'},\n                            template=THEME\n                        )\n                        if var is not None and cvar is not None:\n                            fig_cum.add_hline(\n                                y=1 + var,\n                                line_dash=\"dash\",\n                                line_color=\"red\",\n                                annotation_text=f\"VaR ({confidence_level*100:.0f}%): {var*100:.2f}%\",\n                                annotation_position=\"bottom right\"\n                            )\n                            fig_cum.add_hline(\n                                y=1 + cvar,\n                                line_dash=\"dash\",\n                                line_color=\"blue\",\n                                annotation_text=f\"CVaR ({confidence_level*100:.0f}%): {cvar*100:.2f}%\",\n                                annotation_position=\"bottom right\"\n                            )\n                        st.plotly_chart(fig_cum, use_container_width=True)\n                        \n                        # Returns Distribution with VaR and CVaR\n                        st.subheader(\"\ud83d\udcca Returns Distribution with VaR and CVaR\")\n                        fig_dist = px.histogram(\n                            portfolio_returns_local,\n                            nbins=50,\n                            title='\ud83d\udcca Simulated Returns Distribution',\n                            labels={'value': 'Returns', 'count': 'Frequency'},\n                            template=THEME\n                        )\n                        if var is not None and cvar is not None:\n                            fig_dist.add_vline(\n                                x=var,\n                                line_dash=\"dash\",\n                                line_color=\"red\",\n                                annotation_text=f\"VaR: {var*100:.2f}%\",\n                                annotation_position=\"top left\"\n                            )\n                            fig_dist.add_vline(\n                                x=cvar,\n                                line_dash=\"dash\",\n                                line_color=\"blue\",\n                                annotation_text=f\"CVaR: {cvar*100:.2f}%\",\n                                annotation_position=\"top left\"\n                            )\n                        st.plotly_chart(fig_dist, use_container_width=True)\n                        \n                        # Box Plot\n                        st.subheader(\"\ud83d\udce6 Box Plot of Portfolio Returns\")\n                        fig_box = px.box(\n                            pd.DataFrame(portfolio_returns_local, columns=['Returns']),\n                            y='Returns',\n                            title='\ud83d\udce6 Box Plot of Portfolio Returns',\n                            template=THEME\n                        )\n                        st.plotly_chart(fig_box, use_container_width=True)\n                        \n                        # Interpretation\n                        st.markdown(\"\"\"\n                        **\ud83d\udcd6 Interpretation:**\n                        - **VaR (Value at Risk):** Represents the maximum expected loss at the specified confidence level.\n                        - **CVaR (Conditional Value at Risk):** Indicates the average loss exceeding the VaR.\n                        - **Skewness:** Measures the asymmetry of the return distribution.\n                        - **Kurtosis:** Indicates the \"tailedness\" of the return distribution.\n                        - **Annualized Volatility:** Measures the dispersion of returns, indicating risk.\n                        - **Maximum Drawdown:** Shows the largest peak-to-trough decline, reflecting potential risk exposure.\n                        \"\"\")\n                    else:\n                        st.info(\"\ud83d\udd0d Calculate risk metrics to view visualizations.\")\n                \n                # Sensitivity Analysis Tab\n                with risk_analysis_tabs[2]:\n                    st.header(\"\ud83d\udee0\ufe0f Sensitivity Analysis\")\n                    \n                    if 'portfolio_returns' in st.session_state:\n                        portfolio_returns_local = st.session_state.portfolio_returns\n                        st.subheader(\"\ud83d\udcc8 Adjusted Returns Distribution\")\n                        fig_adj_returns = px.histogram(\n                            portfolio_returns_local,\n                            nbins=50,\n                            title='\ud83d\udcc8 Adjusted Portfolio Returns Distribution',\n                            labels={'value': 'Returns', 'count': 'Frequency'},\n                            template=THEME\n                        )\n                        st.plotly_chart(fig_adj_returns, use_container_width=True)\n                        \n                        st.subheader(\"\ud83d\udd04 Risk-Return Scatter Plot\")\n                        fig_risk_return = px.scatter(\n                            x=portfolio_returns_local.std() * np.sqrt(252),\n                            y=portfolio_returns_local.mean() * 252,\n                            size=[w * 100 for w in weights],\n                            color=[w * 100 for w in weights],\n                            hover_name=available_selected,\n                            title='\ud83d\udd04 Risk vs Return Scatter Plot',\n                            labels={'x': 'Annualized Volatility (Std Dev)', 'y': 'Annualized Return'},\n                            template=THEME\n                        )\n                        st.plotly_chart(fig_risk_return, use_container_width=True)\n                        \n                        # Interpretation\n                        st.markdown(\"\"\"\n                        **\ud83d\udcd6 Interpretation:**\n                        - **Adjusted Returns Distribution:** Reflects how stress shocks and sensitivity adjustments affect portfolio returns.\n                        - **Risk-Return Scatter Plot:** Visualizes the relationship between risk (volatility) and return for each asset in the portfolio.\n                        - **Size & Color Indicators:** Represent the allocation percentage, highlighting each asset's influence on the overall portfolio.\n                        \"\"\")\n                    else:\n                        st.info(\"\ud83d\udd0d Calculate risk metrics to perform sensitivity analysis.\")\n    #else:\n        #st.warning(\"Please add at least one portfolio to run risk analysis.\")"},{"assistant_initiates":false,"asst_text":"```json\n{\n    \"result\": 0.3\n}\n```","behavior":"degenerate_repetition","conversation_hash":"8adc722b05c4f1e462d1406669f558e0","conversation_redacted":false,"conversation_toxic":false,"country":"Egypt","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"Spanish","model":"gpt-4o-2024-08-06","n_chars":2461,"n_turns_in_conversation":1,"n_words":364,"source_row_offset":30160,"source_shard":"turns-00044.parquet","timestamp":"Tue, 05 Nov 2024 14:26:31 GMT","turn_id":"db948815ee84ff94b9fa3223","turn_index":0,"user_text":"You are a JSON assistant. You only reply in valid JSON and never in normal text. Asigne un nivel de dificultad a la informaci\ufffdn del video proporcionada a continuaci\ufffdn. La salida debe ser una \ufffdnica propiedad llamada \"result\" con un valor de punto flotante entre 0 y 1. Los niveles de dificultad se categorizan de la siguiente manera: \n0.0 - Completo Principiante: Este nivel incluye vocabulario muy b\ufffdsico y frases simples, acompa\ufffdadas de visuales y contexto claros, lo que lo hace adecuado para individuos sin conocimientos previos del idioma objetivo. \n0.3 - Principiante: Este nivel presenta oraciones simples y vocabulario de uso com\ufffdn. El video puede proporcionar algunos recursos visuales y contexto para ayudar con la comprensi\ufffdn. \n0.5 - Intermedio: Este nivel presenta oraciones m\ufffds complejas y un rango de vocabulario m\ufffds amplio. Puede incluir algunas expresiones idiom\ufffdticas que requieren conocimientos adicionales de fondo. \n0.6 - Intermedio Superior: Este nivel incluye vocabulario especializado y conceptos relevantes para campos espec\ufffdficos. Los espectadores deben tener una competencia razonable en el idioma objetivo para una comprensi\ufffdn completa. \n0.8 - Avanzado: Este nivel utiliza vocabulario avanzado y estructuras de oraciones intrincadas, potencialmente involucrando discusiones matizadas que requieren un fuerte dominio del idioma. \n1 - Muy Avanzado: Este nivel est\ufffd dirigido a hablantes fluidos, incorporando terminolog\ufffda y conceptos especializados que pueden no ser familiares para todos los hablantes nativos.\n T\u0092tulo: Todas las se\u00f1ales te llevan al mejor d\u00eda de tu vida \ud83e\udd8b | Bluey Espa\u00f1ol Canal Oficial\nIdioma: es\nLeyenda: Bluey es una adorable e incansable perrita pastor granadero australiano que vive con su mam\u00e1, su pap\u00e1 y su hermanita Bingo. \u00a1Bluey se vale de su energ\u00eda y su imaginaci\u00f3n sin l\u00edmites para descubrir, re\u00edr y jugar con todos sus amigos y su familia! \u00a1Acompa\u00f1a a Bluey, Bingo, Bandit y Chilli en todas sus aventuras! \n\nCreado por Ludo Studio \u00a9 Ludo Studio \ud83d\udc3e \nSuscr\u00edbete para saber lo \u00faltimo de Bluey \ud83d\udc99: http://bit.ly/SubscribeToBluey \n\ud83d\udda5 P\u00e1gina web: https://www.bluey.tv \n\ud83d\udc14 Twitter: https://twitter.com/officialblueytv \n\ud83d\udc36 Instagram: http://instagram.com/officialblueytv \n\ud83d\udc0a Facebook: https://www.facebook.com/OfficialBlueyTV \n\nEste es un canal comercial de BBC Studios.\nDescripci\u0097n: Hola Flappy Qu\u00e9 bueno verte otra [M\u00fasica] vez tu mam\u00e1 es muy [M\u00fasica] agradable polic\u00eda no puede ser Oh"},{"assistant_initiates":false,"asst_text":"The task involves understanding predictive factors for responses to peginterferon plus ribavirin therapy in Japanese patients infected with hepatitis C virus genotype 1b, focusing on amino acid substitutions and cholesterol levels as significant indicators. \n\n[sys1]\nAmino acid substitutions in the core region and low-density lipoprotein cholesterol levels are key predictors of response to therapy.\nThese factors help determine early and sustained virological responses in patients undergoing treatment.\n[end sys1]\n\n[sys2]\n**2.1 Understand the Problem:**\nThe objective is to comprehend how amino acid substitutions in the HCV core region and LDL cholesterol levels influence the effectiveness of PEG-IFN-RBV therapy in Japanese patients.\n\n*Reflection:* \n\"What do these findings suggest about the biology of HCV and its interaction with therapy? Have I considered all metabolic and genetic factors influencing treatment response?\"\n\n*Creative Perspective:* \n\"Are there hidden biochemical or genetic mechanisms that might explain why these specific factors are predictive? Could there be other biomarkers that we haven't yet explored?\"\n\n**2.2 Analyze the Information:**\nThe study outlines that cholesterol levels and amino acid substitutions play a predictive role in therapy response, with additional factors like GGT levels and ribavirin dosage contributing to predictions.\n\n*Reflection:* \n\"Am I considering the complete range of physiological and genetic variables? Are other related biomarkers outside of cholesterol and amino acid substitutions involved?\"\n\n*Creative Perspective:* \n\"Is there a potential pattern linking these markers that suggests a broader underlying mechanism affecting treatment success?\"\n\n**2.3 Generate Hypotheses:**\n1. Amino acid substitutions alter viral protein structure, affecting susceptibility to therapy. (Confidence: 0.8, Creative: 0.5)\n2. High LDL cholesterol levels may be associated with enhanced drug delivery or retention in cells. (Confidence: 0.7, Creative: 0.6)\n3. GGT is an indicator of liver function, affecting drug metabolism. (Confidence: 0.6, Creative: 0.4)\n4. Gender differences influence the immune response to HCV. (Confidence: 0.6, Creative: 0.5)\n5. Certain genetic profiles minimize adverse drug reactions, leading to better treatment adherence. (Confidence: 0.5, Creative: 0.7)\n6. The dose-dependent effect of ribavirin leads to higher efficacy at certain thresholds. (Confidence: 0.7, Creative: 0.5)\n7. Core region mutations affect viral replication rates, influencing therapeutic outcomes. (Confidence: 0.7, Creative: 0.6)\n8. Cholesterol metabolism intersects with pathways crucial to HCV lifecycle or therapy mechanism. (Confidence: 0.6, Creative: 0.7)\n9. Leukocyte count indicative of immune system robustness against HCV. (Confidence: 0.6, Creative: 0.5)\n10. ICG R15 reflects liver perfusion efficiency, influencing drug efficacy. (Confidence: 0.5, Creative: 0.6)\n\n*Reflection:*\n\"Are these hypotheses considering the total interplay of virological and host factors thoroughly? Are there any landmark studies that contradict or reinforce these ideas?\"\n\n*Creative Perspective:* \n\"Could identifying other less obvious markers expand our understanding of treatment success?\"\n\n**2.4 Anticipate Future Steps and Obstacles:**\n*Objective:* Plan further investigations, addressing potential obstacles like genetic variability among patients.\n\n*Reflection:* \n\"What might hinder the generalization of these results to broader patient populations?\"\n\n*Creative Perspective:* \n\"How can we leverage emerging technologies or data to refine and enhance these predictions?\"\n\n**2.5 Evaluate Hypotheses:**\nI've assessed each hypothesis based on current knowledge and potential. Some require more data to increase confidence.\n\n*Reflection:* \n\"Have I been consistent and fair in evaluating each idea? Which aspects hold the most promise for new therapeutic approaches?\"\n\n*Creative Perspective:* \n\"Is there overlooked potential in cross-disciplinary insights that could reinforce these findings?\"\n\n**2.6 Select the Best Hypothesis:**\nSelected hypothesis: \"Core region mutations affecting viral replication rates is promising due to linkage to treatment outcomes\" (High Confidence and Creative scores).\n\n*Reflection:* \n\"This hypothesis uniquely ties observed genetic changes to treatment dynamics. What makes this approach different?\"\n\n*Creative Perspective:* \n\"Are there other corollaries in antiviral therapy that apply here?\"\n\n**2.7 Implement the Hypothesis:**\n*Objective:* Conduct a targeted study to specifically address the biochemical pathways by which core region mutations influence therapy effectiveness.\n\n*Reflection:*\n\"Is my study design robust enough to yield clear, actionable insights? What resources are crucial?\"\n\n*Creative Perspective:* \n\"Can we revolutionize treatment protocols based on these insights for personalized medicine approaches?\"\n\n**2.8 Monitor and Review Progress:**\nRegularly evaluate experimental data, refining methods as needed.\n\n*Reflection:* \n\"What aspects of the approach are most effective? Where are improvements required?\"\n\n*Creative Perspective:*\n\"What innovative methodologies or technologies could enhance our experimental design?\"\n\n**2.9 Reflect and Capture Insights:**\nSummarize learning about amino acid substitutions and treatment effectiveness, feeding insights back into research frameworks.\n\n*Reflection:* \n\"What new light does this shine on the treatment landscape?\"\n\n*Creative Perspective:*\n\"How can emerging patterns from this research inform future virological therapies?\"\n\n[end sys2]\n\n[sys1]\nThe research identifies key predictors of therapy response, offering insights for personalized treatment approaches in HCV genotype 1b patients.\nFurther investigations could improve treatment strategies, potentially leading to more targeted and effective therapies.\n[end sys1]","behavior":"degenerate_repetition","conversation_hash":"f42c6ca8b65476f1cefa641fb426977d","conversation_redacted":false,"conversation_toxic":false,"country":"United Kingdom","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"English","model":"gpt-4o-2024-08-06","n_chars":15747,"n_turns_in_conversation":1,"n_words":2148,"source_row_offset":30161,"source_shard":"turns-00044.parquet","timestamp":"Tue, 05 Nov 2024 14:26:34 GMT","turn_id":"0013e4c3cf94ceaa8285b28f","turn_index":0,"user_text":"You are a helpful assistant generating synthetic data that captures *System 1* and *System 2* thinking, *creativity*, and *metacognitive reflection*. Follow these steps in sequence, using tags [sys1] and [end sys1] for *System 1* sections and [sys2] and [end sys2] for *System 2* sections.\n\n1. *Identify System 1 and System 2 Thinking Requirements:*\n   - Carefully read the text.\n   - Identify parts of the text that require quick, straightforward responses (*System 1*). Mark these sections with [sys1] and [end sys1].\n   - Identify parts that require in-depth, reflective thinking (*System 2*), marked with [sys2] and [end sys2].\n\n2. *Apply Step-by-Step Problem Solving with Creativity and Metacognitive Reflection for System 2 Sections:*\n\n   *2.1 Understand the Problem:*\n   - Objective: Fully comprehend the issue, constraints, and relevant context.\n   - Reflection: \"What do I understand about this issue? What might I be overlooking?\"\n   - Creative Perspective: Seek hidden patterns or possibilities that could reveal deeper insights or innovative connections.\n\n   *2.2 Analyze the Information:*\n   - Objective: Break down the problem logically.\n   - Reflection: \"Am I considering all factors? Are there any assumptions that need challenging?\"\n   - Creative Perspective: Explore unique patterns or overlooked relationships in the data that could add depth to the analysis.\n\n   *2.3 Generate Hypotheses:*\n   - Objective: Propose at least 10 hypotheses, each with a Confidence Score (0.0 to 1.0) and Creative Score (0.0 to 1.0), reflecting originality, surprise, and utility.\n   - Reflection: \"Have I explored all possible explanations or approaches, both conventional and unconventional?\"\n   - Creative Perspective: Consider novel angles that might provide unexpected insights.\n\n   *2.4 Anticipate Future Steps and Obstacles:*\n   - Objective: Make predictions, accounting for potential outcomes and obstacles.\n   - Reflection: \"What challenges might I face? Is my plan flexible for different scenarios?\"\n   - Creative Perspective: Visualize unforeseen outcomes and adapt plans to make use of them effectively.\n\n   *2.5 Evaluate Hypotheses:*\n   - Objective: Assess hypotheses based on feasibility, risk, and potential impact.\n   - Evaluation: Refine Confidence and Creative Scores as needed.\n   - Reflection: \"Am I unbiased in my assessment? Which options fit best with the overall objectives?\"\n   - Creative Perspective: Identify hidden opportunities or overlooked details in each hypothesis.\n\n   *2.6 Select the Best Hypothesis:*\n   - Objective: Choose the most promising, strategic hypothesis.\n   - Reflection: \"Why does this hypothesis stand out? How does it uniquely address the issue?\"\n   - Creative Perspective: Consider any underutilized potential in the selected approach.\n\n   *2.7 Implement the Hypothesis:*\n   - Objective: Outline actionable steps for testing the hypothesis.\n   - Reflection: \"Is this plan practical? What resources or preparation are required?\"\n   - Creative Perspective: Refine steps to maximize effectiveness and yield unexpected benefits.\n\n   *2.8 Monitor and Review Progress:*\n   - Objective: Review progress, noting areas for improvement.\n   - Reflection: \"What\u2019s working well? What could be improved?\"\n   - Creative Perspective: Look for emerging patterns that could refine future approaches.\n\n   *2.9 Reflect and Capture Insights:*\n   - Objective: Summarize lessons learned and insights gained for future reference.\n   - Reflection: \"What new understanding has emerged from this process?\"\n   - Creative Perspective: Identify innovative insights or patterns that could be applied to similar challenges.\n\n3. *Generate Text Output with Interleaved System 1 and System 2 Responses:*\n   - Use the tags [sys1] and [sys2] throughout.\n   - Aim for a lengthier, detailed response. Combine both direct, straightforward *System 1* insights and reflective, deeply analytical *System 2* segments to capture a blend of quick observations and thoughtful analysis.\n\n---\n\n### *Example Input Text:*\n\nYou are tasked with analyzing a sudden shift in customer preferences in a tech market. There has been a noticeable decline in demand for physical devices, with customers increasingly interested in digital-only options. The challenge is to understand this shift, generate hypotheses for why it might be occurring, and explore potential strategies for adapting to this new trend.\n\n---\n\n### *Processed Output with System 1 and System 2 Thinking:*\n\nThe tech market is experiencing a shift in customer demand, moving away from physical devices toward digital-only options.\n\n[sys1]\nCustomer demand has shifted from physical devices to digital-only options.\nWe need to understand why this change is happening and find strategies to address it.\nKey factors may include cost, convenience, and technological trends.\n[end sys1]\n\n[sys2]\n**2.1 Understand the Problem:**\nThe goal is to understand the underlying reasons for the shift in demand from physical devices to digital-only options, then develop a strategy to adapt.\n\n*Reflection:* \n\"I understand that preferences are changing, but what might be driving this? Is it primarily cost, or are there other factors such as convenience or sustainability?\"\n\n*Creative Perspective:* \n\"Could there be a larger trend in digital minimalism or a preference for eco-friendly solutions that we\u2019re missing? What unseen motivations might explain this shift?\"\n\n**2.2 Analyze the Information:**\nThere are multiple possible factors driving this shift, from economic influences to cultural shifts. It\u2019s essential to isolate each factor and understand its impact.\n\n*Reflection:* \n\"Am I fully considering the various economic and social influences? Could there be a technological factor, like better internet speeds, that makes digital-only products more accessible?\"\n\n*Creative Perspective:* \n\"Are there patterns or trends in other markets that could shed light on this shift? Could this be part of a larger trend toward virtual experiences?\"\n\n**2.3 Generate Hypotheses:**\n1. Customers prefer digital options due to lower costs. (Confidence: 0.8, Creative: 0.4)\n2. There\u2019s a growing trend toward minimalism and reduced physical clutter. (Confidence: 0.7, Creative: 0.7)\n3. Digital products offer greater flexibility and ease of use. (Confidence: 0.6, Creative: 0.6)\n4. Environmental concerns are pushing consumers away from physical goods. (Confidence: 0.6, Creative: 0.8)\n5. Advances in tech make digital-only options more functional. (Confidence: 0.8, Creative: 0.5)\n6. Pandemic-era remote work increased demand for digital solutions. (Confidence: 0.7, Creative: 0.6)\n7. Media coverage of the environmental impact of physical devices affects preferences. (Confidence: 0.5, Creative: 0.7)\n8. There\u2019s an increase in global digital literacy, expanding market access. (Confidence: 0.6, Creative: 0.6)\n9. Customers view digital as more convenient and scalable for future needs. (Confidence: 0.7, Creative: 0.5)\n10. Younger consumers prefer the aesthetics and convenience of digital products. (Confidence: 0.6, Creative: 0.6)\n\n*Reflection:* \n\"Have I considered all possible influences? Are there any surprising factors that could explain this shift?\"\n\n*Creative Perspective:* \n\"Could specific social trends, like the rise of influencer culture or digital-first lifestyles, be influencing customer choices?\"\n\n**2.4 Anticipate Future Steps and Obstacles:**\n*Objective:* Anticipate possible challenges, such as resistance from segments still preferring physical products.\n\n*Reflection:* \n\"What market obstacles might we face if we shift our focus to digital-only? Are there sub-segments that still prioritize physical products?\"\n\n*Creative Perspective:* \n\"Could expanding digital options help us reach a more global audience? Are there emerging trends that we could leverage in our strategy?\"\n\n[end sys2]\n\n[sys1]\nTo address this shift, consider a strategy that incorporates both digital-only offerings and educational campaigns about the benefits of digital solutions.\nUse insights from customer feedback and current trends to guide product development.\nFocus on flexibility and adaptation to cater to different customer segments.\n[end sys1]\n\n\nPredictive factors of early and sustained responses to peginterferon plus ribavirin combination therapy in Japanese patients infected with hepatitis C virus genotype 1b: amino acid substitutions in the core region and low-density lipoprotein cholesterol levels.\nWe showed previously that amino acid (aa) substitutions in the HCV core region (HCV-CR) are predictors of non-virological response (NVR) to peginterferon (PEG-IFN) plus ribavirin (RBV) therapy. Here, we determined the predictive factors of sustained virological response (SVR) and early virologic response (EVR) to this treatment. We evaluated the response to 48-week PEG-IFN-RBV therapy in 114 Japanese adults infected with HCV genotype 1b and determined the predictors of EVR and SVR. EVR was achieved by 70% and SVR by 45% of patients. 64% of patients who achieved EVR also showed SVR, while none of non-EVR achieved SVR. Multivariate analysis identified low-density lipoprotein cholesterol (LDL-C) (>or=86 mg/dl), aa substitutions in HCV-CR (double-wild-type; arginine at aa 70/leucine at aa 91), gamma-glutamyl transpeptidase (GGT) (<109 IU/l), RBV dose (>or=11.0mg/kg), and leukocyte count (>or=4500/mm3) as significant determinants of EVR, and aa substitutions in HCV-CR (double-wild-type), LDL-C (>or=86 mg/dl), male gender, ICG R15 (<10%), GGT (<109 IU/l), and RBV dose (>or=11.0 mg/kg) as determinants of SVR. Prediction of response to therapy based on combination of these factors had high sensitivity, specificity, positive, and negative predictive values. Our study identified aa substitutions in the core region and serum LDL-C as predictors of response to PEG-IFN-RBV therapy in Japanese patients infected with HCV genotype 1b."}],"shard":{"index":44,"name":"turns-00044.parquet","rows":44850}}