{"behavior":"degenerate_repetition","label_filter":null,"limit":10,"next_offset":16217,"offset":16207,"previous_offset":null,"rows":[{"assistant_initiates":false,"asst_text":"If you want to go back one more directory from the base directory in your path, you can use `fileparts` iteratively. Here\u2019s how you can move two directories up from the original `start_path`:\n\n```matlab\n% Get the parent directory once\n[firstParentDir, ~, ~] = fileparts(start_path);\n\n% Get the parent of the first parent directory (go one more level up)\n[secondParentDir, ~, ~] = fileparts(firstParentDir);\n\n% Use this modified path as the starting directory for uigetdir\nexport_path = uigetdir(secondParentDir, \"Choose Directory to Export\");\n```\n\nThis will effectively take you two levels up from the initial path stored in `start_path`. Make sure that the directory structure allows going back that many levels, otherwise you might end up at the root or an invalid directory path.","behavior":"degenerate_repetition","conversation_hash":"647628db7d249b85a4e40552396d31f0","conversation_redacted":false,"conversation_toxic":false,"country":"Romania","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":807,"n_turns_in_conversation":4,"n_words":129,"source_row_offset":16207,"source_shard":"turns-00030.parquet","timestamp":"Thu, 26 Sep 2024 08:22:26 GMT","turn_id":"a67752deccc3fda24e537472","turn_index":2,"user_text":"how to do 1 more behind?"},{"assistant_initiates":false,"asst_text":"Ah, I see. If you want to navigate one level deeper into the directory tree after obtaining the parent directory, you can append a specific folder name to the `parent_dir`. \n\nHere's how you can achieve this:\n\n1. Get the parent directory as you've done.\n2. Append the desired directory name to that parent directory.\n\n```matlab\n% Suppose you have the start_path\n[parent_dir, ~, ~] = fileparts(start_path);\n\n% Now, specify the additional directory you want to navigate into\nadditional_folder = 'desired_subfolder_name'; % Replace this with the actual folder name\n\n% Construct the new path\nnew_start_path = fullfile(parent_dir, additional_folder);\n\n% Use this modified path as the starting directory for uigetdir\nexport_path = uigetdir(new_start_path, \"Choose Directory to Export\");\n```\n\nReplace `'desired_subfolder_name'` with the actual name of the folder you want to move into one level down from the `parent_dir`. If you are looking for a specific subdirectory dynamically, you may need to use commands like `dir` to list directories and choose one.","behavior":"degenerate_repetition","conversation_hash":"647628db7d249b85a4e40552396d31f0","conversation_redacted":false,"conversation_toxic":false,"country":"Romania","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":1087,"n_turns_in_conversation":4,"n_words":167,"source_row_offset":16208,"source_shard":"turns-00030.parquet","timestamp":"Thu, 26 Sep 2024 08:22:26 GMT","turn_id":"b460f63a0fbb15cff84cfc43","turn_index":3,"user_text":"No I want 1 more down from parentDir"},{"assistant_initiates":false,"asst_text":"(Verse 1)\n\ud3ec\ub3c4\uc54c\uc774 \uae5c\ube61\uc774\uba70 \ucda4\uc744 \ucd9c \ub54c,  \n\ub808\ubaac\uc740 \ub178\ub780\ube5b\uc73c\ub85c \ubc18\uc9dd\uac70\ub824\uc694.  \n\uac00\uc9c0\ub294 \uae38\ucb49\uae38\ucb49 \uba4b\uc9c4 \uc2a4\ud15d\uc744 \ubc1f\uace0,  \n\uc624\uc774\ub294 \uc2e0\ub098\uac8c \ub9de\uc7a5\uad6c\uccd0\uc694!\n\n(Chorus)  \n\uc6b0\ub9ac \ubaa8\ub450 \ubaa8\uc5ec\uc11c \uc190\uc744 \uc7a1\uace0,  \n\uc2f1\uadf8\ub7ec\uc6b4 \ub9ac\ub4ec \uc18d\uc5d0 \ube60\uc838\ubd10\uc694.  \n\uc591\ud30c\uc758 \ud765\uaca8\uc6b4 \uba5c\ub85c\ub514 \ub530\ub77c,  \n\ucc44\uc18c\ub4e4\uc758 \ubb34\ub3c4\ud68c\uac00 \uc2dc\uc791\ub3fc\uc694!\n\n(Verse 2)  \n\uc591\ud30c\uac00 \ub208\ubb3c \ud758\ub9ac\ub4ef \uc6c3\uc5b4\ub118\uae30\uace0,  \n\ucc44\uc18c \uce5c\uad6c\ub4e4\uc740 \ub2e4 \uac19\uc774 \uc6c3\uc9c0\uc694.  \n\uc790\uc5f0\uc758 \uba5c\ub85c\ub514\uac00 \uc6b0\ub9ac\ub97c \uac10\uc2f8,  \n\uc624\ub298 \ubc24\uc740 \uc815\ub9d0\ub85c \uc78a\uc9c0 \ubabb\ud560 \uac70\uc608\uc694.\n\n(Chorus)  \n\uc6b0\ub9ac \ubaa8\ub450 \ubaa8\uc5ec\uc11c \uc190\uc744 \uc7a1\uace0,  \n\uc2f1\uadf8\ub7ec\uc6b4 \ub9ac\ub4ec \uc18d\uc5d0 \ube60\uc838\ubd10\uc694.  \n\uc591\ud30c\uc758 \ud765\uaca8\uc6b4 \uba5c\ub85c\ub514 \ub530\ub77c,  \n\ucc44\uc18c\ub4e4\uc758 \ubb34\ub3c4\ud68c\uac00 \uc2dc\uc791\ub3fc\uc694!\n\n(Bridge)  \n\ucc44\uc18c\ub4e4\uc758 \uafc8 \uac19\uc740 \uc774\ubc24,  \n\ubaa8\ub450 \ud568\uaed8 \uc18c\ub9ac\uccd0\ubd10\uc694.  \n\ud558\ub098\ub418\uc5b4 \ucda4\ucd94\ub294 \uc21c\uac04,  \n\uc774 \uc990\uac70\uc6c0\uc740 \ub05d\uc774 \uc5c6\uc5b4\uc694!\n\n(Outro)  \n\uc624\ub298\ubc24\uc758 \ubb34\ub3c4\ud68c\ub294 \ub05d\ub098\uc9c0 \uc54a\uc544,  \n\ucc44\uc18c\ub4e4\uc758 \ud30c\ud2f0\ub294 \uc774\uc5b4\uc9c8 \uac70\uc608\uc694.  \n\uacbd\ucf8c\ud55c \ub9ac\ub4ec\uc5d0 \ubab8\uc744 \ub9e1\uae30\uace0,  \n\ud3ec\ub3c4, \ub808\ubaac, \ubaa8\ub450 \ud568\uaed8 \ucda4\ucdb0\uc694!","behavior":"degenerate_repetition","conversation_hash":"80bd8689fbab3456632a06db2c3deee3","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":"Korean","model":"gpt-4o-2024-08-06","n_chars":557,"n_turns_in_conversation":3,"n_words":112,"source_row_offset":16209,"source_shard":"turns-00030.parquet","timestamp":"Thu, 26 Sep 2024 08:22:34 GMT","turn_id":"9d8ebefb9faf6e5bffbfe00f","turn_index":0,"user_text":"\ud3ec\ub3c4,\ub808\ubaac,\uac00\uc9c0,\uc624\uc774,\uc591\ud30c,\ucc44\uc18c\ub4e4\uc774 \ubaa8\uc5ec\uc11c \ucda4\uc744 \ucd94\uba74\uc11c,\uacbd\ucf8c\ud558\uace0,\uc7ac\ubbf8\uc788\ub294 \ub178\ub798\ub97c \ub9cc\ub4e4\uc5b4\uc918"},{"assistant_initiates":false,"asst_text":"(Verse)  \n\ud3ec\ub3c4\uc54c\uc774 \ud0f1\uae00\ud0f1\uae00, \ub9ac\ub4ec\uc5d0 \ub9de\ucdb0 \ube59\uae00\ube59\uae00,  \n\ub808\ubaac\uc740 \uc0c1\ud07c\ud558\uac8c, \ud587\uc0b4 \uc18d\uc5d0 \ubc18\uc9dd\ubc18\uc9dd!  \n\uac00\uc9c0\uc640 \uc624\uc774 \uc190\uc7a1\uace0, \ube59\uae00\ube59\uae00 \ub3cc\uace0 \ub3cc\uace0,  \n\uc591\ud30c\ub294 \ub208\ubb3c \ub300\uc2e0 \uc6c3\uc74c\uc744 \ub098\ub220\uc694!\n\n(Chorus)  \n\uc774 \ubc24\uc740 \uc6b0\ub9ac\uc758 \ucc44\uc18c \ud30c\ud2f0,  \n\ubaa8\ub450 \ud568\uaed8 \ucda4\ucd94\ub294 \ud658\uc0c1\uc801\uc778 \uc21c\uac04!  \n\uacbd\ucf8c\ud55c \ub9ac\ub4ec\uc5d0 \ub9c8\uc74c\uc744 \ub9e1\uaca8,  \n\ucc44\uc18c\ub4e4\uc758 \uc990\uac70\uc6c0\uc740 \uba48\ucd94\uc9c0 \uc54a\uc544!\n\n(Verse)  \n\uac00\uc9c0\uac00 \ud55c \ubc1c \uc55e\uc73c\ub85c, \uc624\uc774\ub294 \ub4a4\ub85c,  \n\uc11c\ub85c \ub9de\ucdb0\uac00\ub294 \ucda4, \uc815\ub9d0 \uc7ac\ubbf8\uc788\uc5b4\uc694!  \n\uc591\ud30c\ub294 \uc6c3\uc74c\uc744 \ud130\ub728\ub9ac\uba70,  \n\ubaa8\ub450\uc758 \ub9c8\uc74c\uc744 \ud658\ud558\uac8c \ubc1d\ud600\uc694!\n\n(Chorus)  \n\uc774 \ubc24\uc740 \uc6b0\ub9ac\uc758 \ucc44\uc18c \ud30c\ud2f0,  \n\ubaa8\ub450 \ud568\uaed8 \ucda4\ucd94\ub294 \ud658\uc0c1\uc801\uc778 \uc21c\uac04!  \n\uacbd\ucf8c\ud55c \ub9ac\ub4ec\uc5d0 \ub9c8\uc74c\uc744 \ub9e1\uaca8,  \n\ucc44\uc18c\ub4e4\uc758 \uc990\uac70\uc6c0\uc740 \uba48\ucd94\uc9c0 \uc54a\uc544!","behavior":"degenerate_repetition","conversation_hash":"80bd8689fbab3456632a06db2c3deee3","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":"Korean","model":"gpt-4o-2024-08-06","n_chars":423,"n_turns_in_conversation":3,"n_words":86,"source_row_offset":16210,"source_shard":"turns-00030.parquet","timestamp":"Thu, 26 Sep 2024 08:22:34 GMT","turn_id":"dbd147a2e11bf3580c14774c","turn_index":1,"user_text":"\ud3ec\ub3c4,\ub808\ubaac,\uac00\uc9c0,\uc624\uc774,\uc591\ud30c,\ucc44\uc18c\ub4e4\uc774 \ubaa8\uc5ec\uc11c \ucda4\uc744 \ucd94\uba74\uc11c,\uacbd\ucf8c\ud558\uace0,\uc7ac\ubbf8\uc788\ub294 \ub178\ub798\ub97c 1\ubd84\ub0b4\ub85c \ub9cc\ub4e4\uc5b4\uc918"},{"assistant_initiates":false,"asst_text":"(Verse)  \n\ud3ec\ub3c4\uc54c \ud1a1\ud1a1, \ub9ac\ub4ec\uc5d0 \ub9de\ucdb0 \ube59\uae00\ube59\uae00,  \n\ub808\ubaac\uc740 \uc0c1\ud07c\ud558\uac8c, \ucda4\ucd94\uba70 \ubc18\uc9dd\ubc18\uc9dd!  \n\uac00\uc9c0\ub294 \uba4b\uc9c4 \uc2a4\ud15d, \uc624\uc774\uc640 \uc190\uc744 \uc7a1\uace0,  \n\uc591\ud30c\ub294 \uc990\uac70\uc6cc\uc11c \ubbf8\uc18c\ub97c \ud65c\uc9dd!\n\n(Chorus)  \n\ucc44\uc18c\ub4e4\uc758 \ud30c\ud2f0, \uc2e0\ub098\ub294 \ubc24,  \n\ubaa8\ub450 \ud568\uaed8 \ucda4\ucdb0, \uac71\uc815\uc740 \ub194!  \n\ub9ac\ub4ec \ub530\ub77c \ubc1c\uc744 \ub9de\ucdb0, \uc990\uac70\uc6b4 \uc774 \uc21c\uac04,  \n\uacbd\ucf8c\ud55c \uba5c\ub85c\ub514\uc5d0 \ub9c8\uc74c\uc744 \ub9e1\uaca8!\n\n(Bridge)  \n\ud558\ub098 \ub458 \uc14b \ub137, \uc2a4\ud15d\uc744 \ubc1f\uc73c\uba74,  \n\uc6c3\uc74c\uc18c\ub9ac \uac00\ub4dd \ucc28\ub294 \uc774\uacf3!  \n\uc790\uc5f0\uc758 \uce5c\uad6c\ub4e4, \ud568\uaed8\ud558\ub294 \uae30\uc068,  \n\uc774 \ubc24\uc774 \ub05d\ub098\uc9c0 \uc54a\uae30\ub97c \ubc14\ub77c\uc694!\n\n(Outro)  \n\ucc44\uc18c\ub4e4\uc758 \ubb34\ub3c4\ud68c\ub294 \uba48\ucd94\uc9c0 \uc54a\uc544,  \n\ud3ec\ub3c4\uc640 \ub808\ubaac\uc774 \uc774\ub044\ub294 \ud658\uc0c1\uc758 \ubc24!  \n\ubaa8\ub450 \ud568\uaed8 \ucda4\ucd94\uba70 \uc990\uaca8\ubd10\uc694,  \n\uc774 \uc21c\uac04\uc744 \ub193\uce58\uc9c0 \ub9c8\uc694!","behavior":"degenerate_repetition","conversation_hash":"80bd8689fbab3456632a06db2c3deee3","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":"Korean","model":"gpt-4o-2024-08-06","n_chars":413,"n_turns_in_conversation":3,"n_words":86,"source_row_offset":16211,"source_shard":"turns-00030.parquet","timestamp":"Thu, 26 Sep 2024 08:22:34 GMT","turn_id":"8e3d9b863340160c9cd44671","turn_index":2,"user_text":"\ud3ec\ub3c4,\ub808\ubaac,\uac00\uc9c0,\uc624\uc774,\uc591\ud30c,\ucc44\uc18c\ub4e4\uc774 \ubaa8\uc5ec\uc11c \ucda4\uc744 \ucd94\uba74\uc11c,\uacbd\ucf8c\ud558\uace0,\uc7ac\ubbf8\uc788\ub294 \ub178\ub798\ub97c 1\ubd84\ub0b4\ub85c \ub9cc\ub4e4\uc5b4\uc918"},{"assistant_initiates":false,"asst_text":"To extend your `ImagePredictorApp` to support other model types beyond vision models, you'll need to:\n\n1. **Introduce Model Type Selection:** Allow users to select the type of model they wish to work with (e.g., Vision, Text, Tabular, etc.).\n\n2. **Handle Different Model Loading Mechanisms:** Depending on the selected model type, use appropriate libraries and methods to load models (e.g., FastAI for vision and text, Scikit-learn for tabular data, PyTorch for custom models).\n\n3. **Adapt the Training Pipeline:** Ensure that the training process accommodates different data types and model architectures based on the selected model type.\n\n4. **Update the Prediction Logic:** Modify the prediction functionality to handle various input types (images, text, tabular data) accordingly.\n\nBelow is a revised version of your application that incorporates support for both **Vision** and **Text** models using FastAI. This example can be further extended to include more model types like Tabular or custom PyTorch models.\n\n### Key Changes:\n\n1. **Model Type Selection:** Added a `Gtk.ComboBoxText` to allow users to select the model type (`Vision` or `Text`).\n\n2. **Dynamic UI Adjustments:** Based on the selected model type, the application adjusts available options and input fields.\n\n3. **Separate Training Pipelines:** Implemented distinct training workflows for Vision and Text models.\n\n4. **Unified Model Loading and Prediction:** Updated model loading and prediction functions to handle different model types seamlessly.\n\n### Updated Code:\n\n```python\nimport os\nimport sys\nimport threading\nimport logging\nfrom io import BytesIO\nfrom typing import Optional, Union\n\nimport gi\nimport requests\nfrom PIL import Image\nfrom fastai.vision.all import (\n    load_learner as load_vision_learner,\n    PILImage,\n    Learner,\n    vision_learner,\n    untar_data,\n    URLs,\n    ImageDataLoaders,\n    models as vision_models,\n    accuracy,\n    Callback,\n    Resize,\n    aug_transforms,\n)\nfrom fastai.text.all import (\n    load_learner as load_text_learner,\n    TextDataLoaders,\n    text_classifier_learner,\n    AWD_LSTM,\n)\nfrom sklearn.externals import joblib  # For scikit-learn models\n\ngi.require_version('Gtk', '3.0')\nfrom gi.repository import Gtk, GdkPixbuf, Gdk, GLib\n\n# Configure logging\nlogging.basicConfig(\n    level=logging.INFO,\n    format='%(asctime)s %(levelname)s:%(name)s: %(message)s',\n    handlers=[\n        logging.FileHandler(\"model_predictor_app.log\"),\n        logging.StreamHandler(sys.stdout)\n    ]\n)\nlogger = logging.getLogger(__name__)\n\n\nclass ModelPredictorApp(Gtk.Window):\n    def __init__(self):\n        super().__init__(title=\"Model Predictor with Training Support\")\n        self.set_border_width(10)\n        self.set_default_size(1200, 800)\n\n        # Initialize model attributes\n        self.vision_model: Optional[Learner] = None\n        self.text_model: Optional[Learner] = None\n        self.sk_model: Optional[object] = None  # For sklearn models\n\n        # Initialize UI components\n        self.init_ui()\n\n    def init_ui(self):\n        \"\"\"Initialize and arrange all UI components.\"\"\"\n        # Main container using Paned for resizable sections\n        paned = Gtk.Paned.new(Gtk.Orientation.HORIZONTAL)\n        self.add(paned)\n\n        # --- Left Pane: Controls and Training Settings ---\n        left_box = Gtk.Box(orientation=Gtk.Orientation.VERTICAL, spacing=10)\n        paned.pack1(left_box, resize=True, shrink=False)\n\n        # --- Model Type Selection ---\n        model_type_box = Gtk.Box(orientation=Gtk.Orientation.HORIZONTAL, spacing=5)\n        model_type_label = Gtk.Label(label=\"Model Type:\")\n        model_type_label.set_xalign(0)\n        model_type_box.pack_start(model_type_label, False, False, 0)\n\n        self.model_type_combo = Gtk.ComboBoxText()\n        for model_type in [\"Vision\", \"Text\", \"Tabular\", \"Custom PyTorch\"]:\n            self.model_type_combo.append_text(model_type)\n        self.model_type_combo.set_active(0)  # Default to Vision\n        self.model_type_combo.connect(\"changed\", self.on_model_type_changed)\n        model_type_box.pack_start(self.model_type_combo, False, False, 0)\n        left_box.pack_start(model_type_box, False, False, 0)\n\n        # --- Controls Section ---\n        controls_frame = Gtk.Frame(label=\"Prediction Controls\")\n        controls_box = Gtk.Box(orientation=Gtk.Orientation.VERTICAL, spacing=10)\n        controls_box.set_margin_bottom(10)\n        controls_box.set_margin_top(10)\n        controls_box.set_margin_start(10)\n        controls_box.set_margin_end(10)\n        controls_frame.add(controls_box)\n        left_box.pack_start(controls_frame, False, False, 0)\n\n        # Load Model Button\n        self.load_model_button = Gtk.Button(label=\"Load Model\")\n        self.load_model_button.connect(\"clicked\", self.on_load_model)\n        controls_box.pack_start(self.load_model_button, False, False, 0)\n\n        # Dynamic Input Selection based on Model Type\n        self.input_box = Gtk.Box(orientation=Gtk.Orientation.HORIZONTAL, spacing=5)\n        self.input_entry = Gtk.Entry()\n        self.input_entry.set_placeholder_text(\"Enter input (path/URL/text)\")\n        self.input_entry.connect(\"activate\", self.on_enter_input)\n        self.input_button = Gtk.Button(label=\"Browse/Input\")\n        self.input_button.connect(\"clicked\", self.on_select_input)\n        self.input_box.pack_start(self.input_entry, True, True, 0)\n        self.input_box.pack_start(self.input_button, False, False, 0)\n        controls_box.pack_start(self.input_box, False, False, 0)\n\n        # Threshold Slider (Applicable for Classification)\n        self.threshold_box = Gtk.Box(orientation=Gtk.Orientation.VERTICAL, spacing=5)\n        self.threshold_label = Gtk.Label(label=\"Prediction Threshold: 50%\")\n        self.threshold_label.set_xalign(0)\n        self.threshold_box.pack_start(self.threshold_label, False, False, 0)\n\n        self.threshold_adjustment = Gtk.Adjustment(value=50, lower=0, upper=100, step_increment=1, page_increment=10)\n        self.threshold_slider = Gtk.Scale(orientation=Gtk.Orientation.HORIZONTAL, adjustment=self.threshold_adjustment)\n        self.threshold_slider.set_digits(0)\n        self.threshold_slider.connect(\"value-changed\", self.on_threshold_changed)\n        self.threshold_box.pack_start(self.threshold_slider, False, False, 0)\n        controls_box.pack_start(self.threshold_box, False, False, 0)\n\n        # Predict Button\n        self.predict_button = Gtk.Button(label=\"Predict\")\n        self.predict_button.connect(\"clicked\", self.on_predict)\n        self.predict_button.set_sensitive(False)\n        controls_box.pack_start(self.predict_button, False, False, 0)\n\n        # Output Display\n        self.output_label = Gtk.Label(label=\"Output will be displayed here.\")\n        self.output_label.set_line_wrap(True)\n        self.output_label.set_xalign(0)\n        self.output_label.set_justify(Gtk.Justification.LEFT)\n        controls_box.pack_start(self.output_label, False, False, 0)\n\n        # Progress Bar\n        self.progress_bar = Gtk.ProgressBar()\n        self.progress_bar.set_show_text(True)\n        self.progress_bar.set_visible(False)\n        controls_box.pack_start(self.progress_bar, False, False, 0)\n\n        # --- Training Settings Section ---\n        training_frame = Gtk.Frame(label=\"Model Training Settings\")\n        training_box = Gtk.Box(orientation=Gtk.Orientation.VERTICAL, spacing=10)\n        training_box.set_margin_bottom(10)\n        training_box.set_margin_top(10)\n        training_box.set_margin_start(10)\n        training_box.set_margin_end(10)\n        training_frame.add(training_box)\n        left_box.pack_start(training_frame, False, False, 0)\n\n        # Dataset Selection\n        dataset_select_box = Gtk.Box(orientation=Gtk.Orientation.HORIZONTAL, spacing=5)\n        self.dataset_entry = Gtk.Entry()\n        self.dataset_entry.set_placeholder_text(\"Enter dataset path or URL\")\n        self.dataset_button = Gtk.Button(label=\"Browse Dataset\")\n        self.dataset_button.connect(\"clicked\", self.on_select_dataset)\n        dataset_select_box.pack_start(self.dataset_entry, True, True, 0)\n        dataset_select_box.pack_start(self.dataset_button, False, False, 0)\n        training_box.pack_start(dataset_select_box, False, False, 0)\n\n        # Model Architecture Selection (Dynamic based on Model Type)\n        self.architecture_box = Gtk.Box(orientation=Gtk.Orientation.HORIZONTAL, spacing=5)\n        architecture_label = Gtk.Label(label=\"Model Architecture:\")\n        architecture_label.set_xalign(0)\n        self.architecture_box.pack_start(architecture_label, False, False, 0)\n\n        self.architecture_combo = Gtk.ComboBoxText()\n        # Initially populate with Vision models\n        self.populate_architecture_options(\"Vision\")\n        self.architecture_combo.set_active(0)\n        self.architecture_box.pack_start(self.architecture_combo, False, False, 0)\n        training_box.pack_start(self.architecture_box, False, False, 0)\n\n        # Hyperparameters\n        self.hyperparams_grid = Gtk.Grid(column_spacing=10, row_spacing=10)\n\n        # Learning Rate\n        lr_label = Gtk.Label(label=\"Learning Rate:\")\n        lr_label.set_halign(Gtk.Align.END)\n        self.hyperparams_grid.attach(lr_label, 0, 0, 1, 1)\n        self.lr_entry = Gtk.Entry()\n        self.lr_entry.set_text(\"1e-3\")\n        self.hyperparams_grid.attach(self.lr_entry, 1, 0, 1, 1)\n\n        # Batch Size\n        bs_label = Gtk.Label(label=\"Batch Size:\")\n        bs_label.set_halign(Gtk.Align.END)\n        self.hyperparams_grid.attach(bs_label, 0, 1, 1, 1)\n        self.bs_entry = Gtk.Entry()\n        self.bs_entry.set_text(\"32\")\n        self.hyperparams_grid.attach(self.bs_entry, 1, 1, 1, 1)\n\n        # Number of Epochs\n        epochs_label = Gtk.Label(label=\"Number of Epochs:\")\n        epochs_label.set_halign(Gtk.Align.END)\n        self.hyperparams_grid.attach(epochs_label, 0, 2, 1, 1)\n        self.epochs_entry = Gtk.Entry()\n        self.epochs_entry.set_text(\"10\")\n        self.hyperparams_grid.attach(self.epochs_entry, 1, 2, 1, 1)\n\n        # Save Model Name\n        save_label = Gtk.Label(label=\"Save Model As:\")\n        save_label.set_halign(Gtk.Align.END)\n        self.hyperparams_grid.attach(save_label, 0, 3, 1, 1)\n        self.save_entry = Gtk.Entry()\n        self.save_entry.set_text(\"trained_model.pkl\")\n        self.hyperparams_grid.attach(self.save_entry, 1, 3, 1, 1)\n\n        training_box.pack_start(self.hyperparams_grid, False, False, 0)\n\n        # Train Model Button\n        self.train_button = Gtk.Button(label=\"Train Model\")\n        self.train_button.connect(\"clicked\", self.on_train_model)\n        training_box.pack_start(self.train_button, False, False, 0)\n\n        # Training Output Display\n        self.training_output = Gtk.TextView()\n        self.training_output.set_editable(False)\n        self.training_output.set_wrap_mode(Gtk.WrapMode.WORD)\n        training_output_scrolled = Gtk.ScrolledWindow()\n        training_output_scrolled.set_vexpand(True)\n        training_output_scrolled.add(self.training_output)\n        training_box.pack_start(training_output_scrolled, True, True, 0)\n\n        # --- Right Pane: Input Preview ---\n        preview_frame = Gtk.Frame(label=\"Input Preview\")\n        preview_box = Gtk.Box(orientation=Gtk.Orientation.VERTICAL, spacing=5)\n        preview_box.set_margin_bottom(10)\n        preview_box.set_margin_top(10)\n        preview_box.set_margin_start(10)\n        preview_box.set_margin_end(10)\n        preview_frame.add(preview_box)\n\n        self.input_preview = Gtk.Image()\n        self.input_preview.set_valign(Gtk.Align.START)\n        preview_box.pack_start(self.input_preview, True, True, 0)\n\n        # Drag and Drop Support for Input Preview (Image and Text)\n        self.input_preview.drag_dest_set(Gtk.DestDefaults.ALL, [], Gdk.DragAction.COPY)\n        # Add more targets based on model type\n        self.input_preview.drag_dest_add_uri_targets()\n        self.input_preview.connect(\"drag-data-received\", self.on_drag_data_received)\n\n        paned.pack2(preview_frame, resize=True, shrink=False)\n\n        # Initial UI state\n        self.on_model_type_changed(None)\n\n    # ------------------ UI Update Based on Model Type ------------------\n\n    def on_model_type_changed(self, combo):\n        \"\"\"Adjust UI components based on the selected model type.\"\"\"\n        model_type = self.model_type_combo.get_active_text()\n        logger.info(f\"Model type selected: {model_type}\")\n\n        # Update Architecture Options\n        self.populate_architecture_options(model_type)\n\n        # Adjust Input Placeholder and Browse Button\n        self.input_entry.set_placeholder_text(self.get_input_placeholder(model_type))\n        self.input_button.set_label(self.get_input_button_label(model_type))\n\n        # Clear Input Preview\n        self.clear_input_preview()\n\n    def populate_architecture_options(self, model_type: str):\n        \"\"\"Populate architecture_combo based on model type.\"\"\"\n        self.architecture_combo.remove_all()\n        if model_type == \"Vision\":\n            for arch in [\"resnet18\", \"resnet34\", \"resnet50\", \"resnet101\", \"resnet152\"]:\n                self.architecture_combo.append_text(arch)\n        elif model_type == \"Text\":\n            for arch in [\"awd_lstm\"]:\n                self.architecture_combo.append_text(arch)\n        elif model_type == \"Tabular\":\n            for arch in [\"tabular_resnet\"]:\n                self.architecture_combo.append_text(arch)\n        elif model_type == \"Custom PyTorch\":\n            self.architecture_combo.append_text(\"Custom\")\n        else:\n            self.architecture_combo.append_text(\"Unknown\")\n        self.architecture_combo.set_active(0)\n\n    def get_input_placeholder(self, model_type: str) -> str:\n        \"\"\"Get placeholder text based on model type.\"\"\"\n        if model_type == \"Vision\":\n            return \"Enter image path or URL\"\n        elif model_type == \"Text\":\n            return \"Enter text input\"\n        elif model_type == \"Tabular\":\n            return \"Enter data file path or parameters\"\n        elif model_type == \"Custom PyTorch\":\n            return \"Enter input data path or text\"\n        else:\n            return \"Enter input\"\n\n    def get_input_button_label(self, model_type: str) -> str:\n        \"\"\"Get browse/input button label based on model type.\"\"\"\n        if model_type == \"Vision\":\n            return \"Browse Image\"\n        elif model_type == \"Text\":\n            return \"Input Text\"\n        elif model_type == \"Tabular\":\n            return \"Browse Dataset\"\n        elif model_type == \"Custom PyTorch\":\n            return \"Browse/Input\"\n        else:\n            return \"Browse/Input\"\n\n    def clear_input_preview(self):\n        \"\"\"Clear the input preview area.\"\"\"\n        self.input_preview.set_from_icon_name(\"image-missing\", Gtk.IconSize.DIALOG)\n\n    # ------------------ Prediction Controls ------------------\n\n    def on_drag_data_received(self, widget, drag_context, x, y, data, info, time):\n        \"\"\"Handle drag-and-drop of files or URLs based on model type.\"\"\"\n        uris = data.get_uris()\n        if uris:\n            uri = uris[0]\n            model_type = self.model_type_combo.get_active_text()\n            path_or_url = self.parse_uri(uri)\n            if path_or_url:\n                if model_type in [\"Vision\", \"Custom PyTorch\"]:\n                    self.input_entry.set_text(path_or_url)\n                    self.display_input(path_or_url)\n                elif model_type == \"Text\":\n                    # For text, drag-and-drop might not be straightforward; handle accordingly\n                    self.input_entry.set_text(path_or_url)\n                    self.display_input_text(path_or_url)\n                elif model_type == \"Tabular\":\n                    self.input_entry.set_text(path_or_url)\n                    # Potentially load a sample from the dataset\n            else:\n                self.output_label.set_markup(\"<span foreground='red'>Unsupported input type.</span>\")\n        Gtk.drag_finish(drag_context, True, False, time)\n\n    @staticmethod\n    def parse_uri(uri: str) -> Optional[str]:\n        \"\"\"Parse URI to get local path or URL.\"\"\"\n        if uri.startswith(\"file://\"):\n            return uri[7:]\n        elif uri.startswith((\"http://\", \"https://\", \"ftp://\")):\n            return uri\n        else:\n            return None\n\n    def on_enter_input(self, widget):\n        \"\"\"Handle pressing Enter in the input entry.\"\"\"\n        model_type = self.model_type_combo.get_active_text()\n        user_input = self.input_entry.get_text().strip()\n        if user_input:\n            if model_type in [\"Vision\", \"Custom PyTorch\"]:\n                self.display_input(user_input)\n            elif model_type == \"Text\":\n                self.display_input_text(user_input)\n            elif model_type == \"Tabular\":\n                # Handle tabular data preview if necessary\n                self.display_input_tabular(user_input)\n\n    def on_select_input(self, button):\n        \"\"\"Handle the Select/Input button click based on model type.\"\"\"\n        model_type = self.model_type_combo.get_active_text()\n        if model_type in [\"Vision\", \"Custom PyTorch\", \"Tabular\"]:\n            dialog = Gtk.FileChooserDialog(\n                title=\"Select Input\",\n                parent=self,\n                action=Gtk.FileChooserAction.OPEN\n            )\n            dialog.add_buttons(\n                Gtk.STOCK_CANCEL, Gtk.ResponseType.CANCEL,\n                Gtk.STOCK_OPEN, Gtk.ResponseType.OK\n            )\n            if model_type == \"Vision\" or model_type == \"Custom PyTorch\":\n                dialog.set_filter(self.create_image_filter())\n            elif model_type == \"Tabular\":\n                dialog.set_filter(self.create_dataset_filter())\n\n            response = dialog.run()\n            if response == Gtk.ResponseType.OK:\n                input_path = dialog.get_filename()\n                self.input_entry.set_text(input_path)\n                if model_type in [\"Vision\", \"Custom PyTorch\"]:\n                    self.display_input(input_path)\n                elif model_type == \"Tabular\":\n                    self.display_input_tabular(input_path)\n            dialog.destroy()\n        elif model_type == \"Text\":\n            # For text input, open a dialog to enter text\n            self.open_text_input_dialog()\n\n    def open_text_input_dialog(self):\n        \"\"\"Open a dialog to input text for Text models.\"\"\"\n        dialog = Gtk.Dialog(title=\"Enter Text\", parent=self, flags=0)\n        dialog.add_buttons(\n            Gtk.STOCK_CANCEL, Gtk.ResponseType.CANCEL,\n            Gtk.STOCK_OK, Gtk.ResponseType.OK\n        )\n\n        box = dialog.get_content_area()\n        text_view = Gtk.TextView()\n        text_view.set_wrap_mode(Gtk.WrapMode.WORD)\n        box.add(text_view)\n        dialog.show_all()\n\n        response = dialog.run()\n        if response == Gtk.ResponseType.OK:\n            buffer = text_view.get_buffer()\n            start_iter = buffer.get_start_iter()\n            end_iter = buffer.get_end_iter()\n            user_text = buffer.get_text(start_iter, end_iter, True)\n            self.input_entry.set_text(user_text)\n            self.display_input_text(user_text)\n        dialog.destroy()\n\n    @staticmethod\n    def create_image_filter() -> Gtk.FileFilter:\n        \"\"\"Create a file filter for image files.\"\"\"\n        img_filter = Gtk.FileFilter()\n        img_filter.set_name(\"Image Files (*.png, *.jpg, *.jpeg, *.webp)\")\n        img_filter.add_pattern(\"*.png\")\n        img_filter.add_pattern(\"*.jpg\")\n        img_filter.add_pattern(\"*.jpeg\")\n        img_filter.add_pattern(\"*.webp\")\n        img_filter.add_mime_type(\"image/png\")\n        img_filter.add_mime_type(\"image/jpeg\")\n        img_filter.add_mime_type(\"image/webp\")\n        return img_filter\n\n    @staticmethod\n    def create_dataset_filter() -> Gtk.FileFilter:\n        \"\"\"Create a file filter for dataset files.\"\"\"\n        data_filter = Gtk.FileFilter()\n        data_filter.set_name(\"CSV Files (*.csv)\")\n        data_filter.add_pattern(\"*.csv\")\n        return data_filter\n\n    def on_threshold_changed(self, scale: Gtk.Scale):\n        \"\"\"Update the threshold label when the slider value changes.\"\"\"\n        value = self.threshold_slider.get_value()\n        self.threshold_label.set_text(f\"Prediction Threshold: {int(value)}%\")\n\n    def on_predict(self, button: Gtk.Button):\n        \"\"\"Handle the Predict button click.\"\"\"\n        model_type = self.model_type_combo.get_active_text()\n        threshold = self.threshold_slider.get_value()\n\n        if model_type == \"Vision\" and not self.vision_model:\n            self.output_label.set_markup(\"<span foreground='red'>Please load a Vision model first.</span>\")\n            return\n        elif model_type == \"Text\" and not self.text_model:\n            self.output_label.set_markup(\"<span foreground='red'>Please load a Text model first.</span>\")\n            return\n        elif model_type == \"Tabular\" and not self.sk_model:\n            self.output_label.set_markup(\"<span foreground='red'>Please load a Scikit-learn model first.</span>\")\n            return\n        elif model_type == \"Custom PyTorch\" and not self.vision_model:\n            self.output_label.set_markup(\"<span foreground='red'>Please load a PyTorch model first.</span>\")\n            return\n\n        user_input = self.input_entry.get_text().strip()\n        if not user_input:\n            self.output_label.set_markup(\"<span foreground='red'>Please provide the necessary input.</span>\")\n            return\n\n        self.set_controls_sensitive(False)\n        self.show_progress(\"Performing prediction...\")\n        thread = threading.Thread(target=self.predict_input, args=(model_type, user_input, threshold), daemon=True)\n        thread.start()\n\n    def predict_input(self, model_type: str, user_input: str, threshold: float):\n        \"\"\"Perform prediction based on the model type and input.\"\"\"\n        try:\n            if model_type == \"Vision\":\n                image = self.load_image(user_input)\n                GLib.idle_add(self.display_input, user_input)\n                pred, pred_idx, outputs = self.vision_model.predict(image)\n                probabilities = outputs.numpy() * 100  # Convert to percentage\n                classes = self.vision_model.dls.vocab  # Get class names\n\n                # Filter predictions based on threshold\n                output_lines = [\"<b>Predictions:</b>\"]\n                for class_name, probability in zip(classes, probabilities):\n                    if probability >= threshold:\n                        line = f\"\u2022 {class_name}: {probability:.2f}%\"\n                        output_lines.append(line)\n                output_lines.append(f\"\\n<b>Top Prediction:</b> {pred}\")\n\n                markup_text = \"\\n\".join(output_lines)\n                GLib.idle_add(self.update_output, markup_text)\n\n            elif model_type == \"Text\":\n                GLib.idle_add(self.display_input_text, user_input)\n                pred, pred_idx, outputs = self.text_model.predict(user_input)\n                probabilities = outputs.numpy() * 100\n                classes = self.text_model.dls.vocab\n\n                # Filter predictions based on threshold\n                output_lines = [\"<b>Predictions:</b>\"]\n                for class_name, probability in zip(classes, probabilities):\n                    if probability >= threshold:\n                        line = f\"\u2022 {class_name}: {probability:.2f}%\"\n                        output_lines.append(line)\n                output_lines.append(f\"\\n<b>Top Prediction:</b> {pred}\")\n\n                markup_text = \"\\n\".join(output_lines)\n                GLib.idle_add(self.update_output, markup_text)\n\n            elif model_type == \"Tabular\":\n                # For simplicity, assume the input is a CSV file path for batch prediction\n                if not os.path.isfile(user_input):\n                    raise ValueError(\"Invalid dataset path for tabular prediction.\")\n                import pandas as pd\n\n                data = pd.read_csv(user_input)\n                predictions = self.sk_model.predict_proba(data)\n                classes = self.sk_model.classes_\n\n                # Process predictions\n                output_lines = [\"<b>Batch Predictions:</b>\"]\n                for idx, row in data.iterrows():\n                    output_lines.append(f\"Record {idx + 1}:\")\n                    for class_name, prob in zip(classes, row):\n                        if prob * 100 >= threshold:\n                            output_lines.append(f\"  \u2022 {class_name}: {prob * 100:.2f}%\")\n                    # Assuming binary classification, add top prediction\n                    top_pred = self.sk_model.predict(data.iloc[[idx]])[0]\n                    output_lines.append(f\"  <b>Top Prediction:</b> {top_pred}\\n\")\n\n                markup_text = \"\\n\".join(output_lines)\n                GLib.idle_add(self.update_output, markup_text)\n\n            elif model_type == \"Custom PyTorch\":\n                # Assuming similar to Vision models; customize as needed\n                image = self.load_image(user_input)\n                GLib.idle_add(self.display_input, user_input)\n                pred, pred_idx, outputs = self.vision_model.predict(image)\n                probabilities = outputs.detach().numpy() * 100\n                classes = [f\"Class {i}\" for i in range(len(predictions))]  # Placeholder\n\n                # Filter predictions based on threshold\n                output_lines = [\"<b>Predictions:</b>\"]\n                for class_name, probability in zip(classes, probabilities):\n                    if probability >= threshold:\n                        line = f\"\u2022 {class_name}: {probability:.2f}%\"\n                        output_lines.append(line)\n                output_lines.append(f\"\\n<b>Top Prediction:</b> {pred}\")\n\n                markup_text = \"\\n\".join(output_lines)\n                GLib.idle_add(self.update_output, markup_text)\n\n            logger.info(f\"Prediction successful for input: {user_input}\")\n        except Exception as e:\n            logger.error(f\"Error during prediction: {e}\", exc_info=True)\n            GLib.idle_add(self.update_output, f\"<span foreground='red'>Error during prediction:</span>\\n{e}\")\n        finally:\n            GLib.idle_add(self.hide_progress)\n            GLib.idle_add(self.set_controls_sensitive, True)\n\n    def load_image(self, image_input: str) -> PILImage:\n        \"\"\"\n        Load an image from a local path or a URL.\n\n        Args:\n            image_input (str): The path or URL of the image.\n\n        Returns:\n            PILImage: The loaded image.\n        \"\"\"\n        logger.debug(f\"Loading image from: {image_input}\")\n        if image_input.startswith(('http://', 'https://', 'ftp://')):\n            response = requests.get(image_input, timeout=10)\n            response.raise_for_status()\n            image = PILImage.create(BytesIO(response.content))\n            logger.debug(\"Image loaded from URL.\")\n            return image\n        elif os.path.isfile(image_input):\n            image = PILImage.create(image_input)\n            logger.debug(\"Image loaded from local file.\")\n            return image\n        else:\n            logger.error(\"Invalid image path or URL.\")\n            raise ValueError(\"Invalid image path or URL.\")\n\n    def display_input(self, image_input: str):\n        \"\"\"\n        Display the selected image in the input preview widget.\n\n        Args:\n            image_input (str): The path or URL of the image.\n        \"\"\"\n        try:\n            if image_input.startswith(('http://', 'https://', 'ftp://')):\n                response = requests.get(image_input, timeout=10)\n                response.raise_for_status()\n                loader = GdkPixbuf.PixbufLoader.new()\n                loader.write(response.content)\n                loader.close()\n                pixbuf = loader.get_pixbuf()\n            else:\n                pixbuf = GdkPixbuf.Pixbuf.new_from_file(image_input)\n\n            # Calculate scaling while preserving aspect ratio\n            max_width, max_height = 600, 600\n            width = pixbuf.get_width()\n            height = pixbuf.get_height()\n            scaling_factor = min(max_width / width, max_height / height, 1)\n            new_width = int(width * scaling_factor)\n            new_height = int(height * scaling_factor)\n\n            scaled_pixbuf = pixbuf.scale_simple(new_width, new_height, GdkPixbuf.InterpType.BILINEAR)\n            self.input_preview.set_from_pixbuf(scaled_pixbuf)\n            logger.debug(\"Image displayed successfully.\")\n        except Exception as e:\n            logger.error(f\"Failed to display input image: {e}\", exc_info=True)\n            self.input_preview.set_from_icon_name(\"image-missing\", Gtk.IconSize.DIALOG)\n            self.update_output(f\"<span foreground='red'>Failed to display input preview:</span>\\n{e}\")\n\n    def display_input_text(self, text: str):\n        \"\"\"Display text input in the preview area.\"\"\"\n        self.input_preview.set_from_icon_name(\"text-x-generic\", Gtk.IconSize.DIALOG)\n        # Optionally, display the text in a separate label or text view\n\n    def display_input_tabular(self, path: str):\n        \"\"\"Display a preview of the tabular data.\"\"\"\n        try:\n            import pandas as pd\n\n            data = pd.read_csv(path)\n            preview = data.head().to_string()\n            # Display preview in a separate text view or label\n            self.input_preview.set_from_icon_name(\"text-x-generic\", Gtk.IconSize.DIALOG)\n            self.update_output(f\"<b>Data Preview:</b>\\n{preview}\")\n        except Exception as e:\n            logger.error(f\"Failed to display tabular data: {e}\", exc_info=True)\n            self.update_output(f\"<span foreground='red'>Failed to display data preview:</span>\\n{e}\")\n\n    def update_output(self, text: str):\n        \"\"\"Update the output label with the given text.\"\"\"\n        self.output_label.set_markup(text)\n\n    def set_controls_sensitive(self, sensitive: bool):\n        \"\"\"Enable or disable controls based on the sensitive flag.\"\"\"\n        # Prediction Controls\n        self.load_model_button.set_sensitive(sensitive)\n        self.input_button.set_sensitive(sensitive)\n        self.input_entry.set_sensitive(sensitive)\n        self.threshold_slider.set_sensitive(sensitive)\n        self.predict_button.set_sensitive(sensitive and (\n            (self.model_type_combo.get_active_text() == \"Vision\" and self.vision_model) or\n            (self.model_type_combo.get_active_text() == \"Text\" and self.text_model) or\n            (self.model_type_combo.get_active_text() == \"Tabular\" and self.sk_model) or\n            (self.model_type_combo.get_active_text() == \"Custom PyTorch\" and self.vision_model)\n        ))\n\n        # Training Controls\n        self.train_button.set_sensitive(sensitive)\n        self.dataset_button.set_sensitive(sensitive)\n        self.dataset_entry.set_sensitive(sensitive)\n        self.architecture_combo.set_sensitive(sensitive)\n        self.lr_entry.set_sensitive(sensitive)\n        self.bs_entry.set_sensitive(sensitive)\n        self.epochs_entry.set_sensitive(sensitive)\n        self.save_entry.set_sensitive(sensitive)\n\n    def show_progress(self, message: str):\n        \"\"\"Display the progress bar with a message.\"\"\"\n        self.progress_bar.set_visible(True)\n        self.progress_bar.set_fraction(0.0)\n        self.progress_bar.set_text(message)\n        self.progress_bar.pulse()\n\n        # Start a timeout to animate the progress bar\n        GLib.timeout_add(100, self.animate_progress)\n\n    def animate_progress(self) -> bool:\n        \"\"\"Animate the progress bar.\"\"\"\n        self.progress_bar.pulse()\n        return True  # Continue calling\n\n    def hide_progress(self):\n        \"\"\"Hide the progress bar.\"\"\"\n        self.progress_bar.set_visible(False)\n        self.progress_bar.set_text(\"\")\n\n    # ------------------ Model Loading ------------------\n\n    def on_load_model(self, button: Gtk.Button):\n        \"\"\"Handle the Load Model button click.\"\"\"\n        model_type = self.model_type_combo.get_active_text()\n\n        dialog = Gtk.FileChooserDialog(\n            title=\"Select a Model File\",\n            parent=self,\n            action=Gtk.FileChooserAction.OPEN\n        )\n        dialog.add_buttons(\n            Gtk.STOCK_CANCEL, Gtk.ResponseType.CANCEL,\n            Gtk.STOCK_OPEN, Gtk.ResponseType.OK\n        )\n        dialog.set_filter(self.create_model_filter(model_type))\n\n        response = dialog.run()\n        if response == Gtk.ResponseType.OK:\n            model_path = dialog.get_filename()\n            logger.info(f\"Selected model file: {model_path}\")\n            self.load_model_async(model_type, model_path)\n        dialog.destroy()\n\n    def create_model_filter(self, model_type: str) -> Gtk.FileFilter:\n        \"\"\"Create a file filter for model files based on model type.\"\"\"\n        file_filter = Gtk.FileFilter()\n        file_filter.set_name(\"Model Files\")\n\n        if model_type in [\"Vision\", \"Text\", \"Custom PyTorch\"]:\n            file_filter.add_pattern(\"*.pkl\")\n            file_filter.add_pattern(\"*.pth\")\n            file_filter.add_mime_type(\"application/octet-stream\")\n        elif model_type == \"Tabular\":\n            file_filter.add_pattern(\"*.pkl\")\n            file_filter.add_pattern(\"*.joblib\")\n            file_filter.add_mime_type(\"application/octet-stream\")\n        else:\n            file_filter.add_pattern(\"*\")\n\n        return file_filter\n\n    def load_model_async(self, model_type: str, model_path: str):\n        \"\"\"Load the model in a separate thread to keep UI responsive.\"\"\"\n        self.set_controls_sensitive(False)\n        self.show_progress(\"Loading model...\")\n        thread = threading.Thread(target=self.load_model, args=(model_type, model_path), daemon=True)\n        thread.start()\n\n    def load_model(self, model_type: str, model_path: str):\n        \"\"\"Load the machine learning model from the specified path.\"\"\"\n        try:\n            if model_type == \"Vision\":\n                learner = load_vision_learner(model_path)\n                GLib.idle_add(self.on_model_loaded, model_type, learner, model_path)\n                logger.info(f\"Vision model loaded successfully from: {model_path}\")\n            elif model_type == \"Text\":\n                learner = load_text_learner(model_path)\n                GLib.idle_add(self.on_model_loaded, model_type, learner, model_path)\n                logger.info(f\"Text model loaded successfully from: {model_path}\")\n            elif model_type == \"Tabular\":\n                sk_model = joblib.load(model_path)\n                GLib.idle_add(self.on_model_loaded, model_type, sk_model, model_path)\n                logger.info(f\"Scikit-learn model loaded successfully from: {model_path}\")\n            elif model_type == \"Custom PyTorch\":\n                # Load custom PyTorch model (assuming it's a FastAI Learner)\n                learner = load_vision_learner(model_path)  # Adjust as per your custom model\n                GLib.idle_add(self.on_model_loaded, model_type, learner, model_path)\n                logger.info(f\"Custom PyTorch model loaded successfully from: {model_path}\")\n            else:\n                raise ValueError(\"Unsupported model type.\")\n        except Exception as e:\n            logger.error(f\"Failed to load model: {e}\", exc_info=True)\n            GLib.idle_add(self.on_model_load_failed, model_type, str(e))\n\n    def on_model_loaded(self, model_type: str, model, model_path: str):\n        \"\"\"Callback when the model is successfully loaded.\"\"\"\n        if model_type == \"Vision\":\n            self.vision_model = model\n        elif model_type == \"Text\":\n            self.text_model = model\n        elif model_type == \"Tabular\":\n            self.sk_model = model\n        elif model_type == \"Custom PyTorch\":\n            self.vision_model = model  # Adjust based on implementation\n\n        self.update_output(f\"<span foreground='green'>Model loaded successfully from:</span>\\n{model_path}\")\n        self.hide_progress()\n        self.set_controls_sensitive(True)\n\n    def on_model_load_failed(self, model_type: str, error_message: str):\n        \"\"\"Callback when the model fails to load.\"\"\"\n        if model_type == \"Vision\":\n            self.vision_model = None\n        elif model_type == \"Text\":\n            self.text_model = None\n        elif model_type == \"Tabular\":\n            self.sk_model = None\n        elif model_type == \"Custom PyTorch\":\n            self.vision_model = None  # Adjust based on implementation\n\n        self.update_output(f\"<span foreground='red'>Failed to load model:</span>\\n{error_message}\")\n        self.hide_progress()\n        self.set_controls_sensitive(True)\n\n    # ------------------ Model Training Controls ------------------\n\n    def on_train_model(self, button: Gtk.Button):\n        \"\"\"Handle the Train Model button click.\"\"\"\n        model_type = self.model_type_combo.get_active_text()\n        dataset_input = self.dataset_entry.get_text().strip()\n        architecture = self.architecture_combo.get_active_text()\n        lr = self.lr_entry.get_text().strip()\n        bs = self.bs_entry.get_text().strip()\n        epochs = self.epochs_entry.get_text().strip()\n        save_model_name = self.save_entry.get_text().strip()\n\n        # Validate inputs\n        if not dataset_input or not os.path.isdir(dataset_input):\n            self.append_training_output(\"Invalid dataset path.\", error=True)\n            return\n\n        if not architecture:\n            self.append_training_output(\"Please select a model architecture.\", error=True)\n            return\n\n        try:\n            lr = float(lr)\n            bs = int(bs)\n            epochs = int(epochs)\n        except ValueError:\n            self.append_training_output(\"Learning rate must be a float, Batch size and Epochs must be integers.\", error=True)\n            return\n\n        if not save_model_name:\n            self.append_training_output(\"Please specify a name to save the trained model.\", error=True)\n            return\n\n        # Start training in a separate thread\n        self.set_controls_sensitive(False)\n        self.show_training_progress(\"Starting model training...\")\n        thread = threading.Thread(\n            target=self.train_model,\n            args=(model_type, dataset_input, architecture, lr, bs, epochs, save_model_name),\n            daemon=True\n        )\n        thread.start()\n\n    def train_model(self, model_type: str, dataset_path: str, architecture: str, lr: float, bs: int, epochs: int, save_model_name: str):\n        \"\"\"Train the model with the specified settings.\"\"\"\n        try:\n            logger.info(f\"Starting training with model type: {model_type}, architecture: {architecture}, LR: {lr}, BS: {bs}, Epochs: {epochs}\")\n            GLib.idle_add(self.append_training_output, f\"Loading dataset from: {dataset_path}\")\n\n            if model_type == \"Vision\":\n                # Create ImageDataLoaders\n                data = ImageDataLoaders.from_folder(\n                    dataset_path,\n                    valid_pct=0.2,\n                    item_tfms=Resize(224),\n                    batch_tfms=aug_transforms(),\n                    bs=bs\n                )\n                logger.info(\"ImageDataLoaders created successfully.\")\n                GLib.idle_add(self.append_training_output, \"Dataset loaded successfully.\")\n\n                # Initialize the learner\n                learner = vision_learner(data, arch=getattr(vision_models, architecture)(), metrics=accuracy)\n                logger.info(\"Learner initialized successfully.\")\n                GLib.idle_add(self.append_training_output, f\"Initialized learner with architecture: {architecture}\")\n\n                # Start training with fine-tuning\n                GLib.idle_add(self.append_training_output, \"Starting training...\")\n                learner.fine_tune(epochs, base_lr=lr, callbacks=[self.TrainingCallback(self)])\n\n                # Save the trained model\n                learner.export(save_model_name)\n                logger.info(f\"Vision model trained and saved as: {save_model_name}\")\n                GLib.idle_add(self.append_training_output, f\"Model trained and saved as: {save_model_name}\", success=True)\n\n                # Automatically load the newly trained model\n                GLib.idle_add(self.load_model_async, model_type, save_model_name)\n\n            elif model_type == \"Text\":\n                from fastai.text.all import TextDataLoaders, text_classifier_learner, AWD_LSTM\n\n                # Create TextDataLoaders\n                data = TextDataLoaders.from_folder(\n                    dataset_path,\n                    valid_pct=0.2,\n                    text_vocab=None,\n                    bs=bs\n                )\n                logger.info(\"TextDataLoaders created successfully.\")\n                GLib.idle_add(self.append_training_output, \"Dataset loaded successfully.\")\n\n                # Initialize the learner\n                learner = text_classifier_learner(data, AWD_LSTM, metrics=accuracy)\n                logger.info(\"Text learner initialized successfully.\")\n                GLib.idle_add(self.append_training_output, f\"Initialized text learner with architecture: {architecture}\")\n\n                # Start training with fine-tuning\n                GLib.idle_add(self.append_training_output, \"Starting training...\")\n                learner.fine_tune(epochs, base_lr=lr, callbacks=[self.TrainingCallback(self)])\n\n                # Save the trained model\n                learner.export(save_model_name)\n                logger.info(f\"Text model trained and saved as: {save_model_name}\")\n                GLib.idle_add(self.append_training_output, f\"Model trained and saved as: {save_model_name}\", success=True)\n\n                # Automatically load the newly trained model\n                GLib.idle_add(self.load_model_async, model_type, save_model_name)\n\n            elif model_type == \"Tabular\":\n                # Example using Scikit-learn (e.g., RandomForestClassifier)\n                import pandas as pd\n                from sklearn.ensemble import RandomForestClassifier\n                from sklearn.model_selection import train_test_split\n                from sklearn.metrics import accuracy_score\n\n                data = pd.read_csv(dataset_path)\n                if 'target' not in data.columns:\n                    raise ValueError(\"Dataset must contain a 'target' column.\")\n\n                X = data.drop('target', axis=1)\n                y = data['target']\n                X_train, X_valid, y_train, y_valid = train_test_split(X, y, test_size=0.2)\n\n                model = RandomForestClassifier(n_estimators=100, random_state=42)\n                model.fit(X_train, y_train)\n                preds = model.predict(X_valid)\n                acc = accuracy_score(y_valid, preds)\n                GLib.idle_add(self.append_training_output, f\"Validation Accuracy: {acc * 100:.2f}%\")\n\n                # Save the model\n                joblib.dump(model, save_model_name)\n                logger.info(f\"Scikit-learn model trained and saved as: {save_model_name}\")\n                GLib.idle_add(self.append_training_output, f\"Model trained and saved as: {save_model_name}\", success=True)\n\n                # Automatically load the newly trained model\n                GLib.idle_add(self.load_model_async, model_type, save_model_name)\n\n            elif model_type == \"Custom PyTorch\":\n                # Placeholder for custom PyTorch model training\n                # Implement as per your custom requirements\n                GLib.idle_add(self.append_training_output, \"Custom PyTorch training not implemented yet.\", error=True)\n\n            else:\n                raise ValueError(\"Unsupported model type for training.\")\n        except Exception as e:\n            logger.error(f\"Training failed: {e}\", exc_info=True)\n            GLib.idle_add(self.append_training_output, f\"Training failed: {e}\", error=True)\n        finally:\n            GLib.idle_add(self.hide_training_progress)\n            GLib.idle_add(self.set_controls_sensitive, True)\n\n    def show_training_progress(self, message: str):\n        \"\"\"Display the training progress bar with a message.\"\"\"\n        self.progress_bar.set_visible(True)\n        self.progress_bar.set_fraction(0.0)\n        self.progress_bar.set_text(message)\n        self.progress_bar.pulse()\n\n        # Start a timeout to animate the progress bar\n        GLib.timeout_add(100, self.animate_progress)\n\n    def hide_training_progress(self):\n        \"\"\"Hide the training progress bar.\"\"\"\n        self.progress_bar.set_visible(False)\n        self.progress_bar.set_text(\"\")\n\n    def append_training_output(self, message: str, error: bool = False, success: bool = False):\n        \"\"\"Append messages to the training output TextView.\"\"\"\n        buffer = self.training_output.get_buffer()\n        end_iter = buffer.get_end_iter()\n        if error:\n            formatted_message = f\"<span foreground='red'>{message}</span>\\n\"\n        elif success:\n            formatted_message = f\"<span foreground='green'>{message}</span>\\n\"\n        else:\n            formatted_message = f\"{message}\\n\"\n        buffer.insert_markup(end_iter, formatted_message)\n\n    # ------------------ Model Training Callback Class ------------------\n\n    class TrainingCallback(Callback):\n        \"\"\"A callback to monitor training progress and output.\"\"\"\n\n        def __init__(self, app):\n            self.app = app\n\n        def after_epoch(self):\n            epoch = self.learn.epoch\n            loss = self.learn.recorder.losses[-1]\n            metrics = self.learn.recorder.metrics[-1]\n            metric_name = 'Accuracy' if 'accuracy' in self.learn.metrics else 'Metric'\n            message = f\"Epoch {epoch + 1}: Loss={loss:.4f}, {metric_name}={metrics * 100:.2f}%\"\n            GLib.idle_add(self.app.append_training_output, message)\n\n        def after_fit(self):\n            GLib.idle_add(self.app.append_training_output, \"Training completed successfully.\", success=True)\n\n    # ------------------ Prediction Functions ------------------\n\n    # load_image and display_input methods are already defined above\n\n    # ------------------ Utility Functions ------------------\n\n    # set_controls_sensitive is already defined above\n\n    # ------------------ Main Function ------------------\n\n    def main():\n        \"\"\"Initialize and run the GTK application.\"\"\"\n        app = ModelPredictorApp()\n        app.connect(\"destroy\", Gtk.main_quit)\n        app.show_all()\n        Gtk.main()\n\n\nif __name__ == \"__main__\":\n    ModelPredictorApp.main()\n```\n\n### Detailed Explanation of Changes:\n\n1. **Model Type Selection:**\n    - Added a `Gtk.ComboBoxText` named `model_type_combo` to allow users to select the model type (`Vision`, `Text`, `Tabular`, `Custom PyTorch`).\n    - Connected the `changed` signal to `on_model_type_changed` to dynamically adjust the UI based on the selected model type.\n\n2. **Dynamic Architecture Options:**\n    - The `architecture_combo` now populates different architectures based on the selected model type. For example, Vision models have `resnet` variants, Text models use architectures like `awd_lstm`, etc.\n\n3. **Input Handling:**\n    - Adjusted the input fields to vary based on the model type. For Vision and Custom PyTorch models, users can select images. For Text models, users can input text directly. For Tabular models, users can select dataset files (e.g., CSV).\n\n4. **Model Loading:**\n    - Updated the `load_model_async` and `load_model` methods to handle different model types. For Vision and Text models, FastAI's `load_learner` is used. For Tabular models, Scikit-learn's `joblib.load` is utilized.\n    - Ensured that models are stored in separate attributes (`vision_model`, `text_model`, `sk_model`) based on their type.\n\n5. **Prediction Logic:**\n    - Enhanced the `predict_input` method to handle predictions differently based on model type:\n        - **Vision:** Processes images and displays top predictions.\n        - **Text:** Processes text inputs and displays classification results.\n        - **Tabular:** Performs batch predictions on datasets and displays results.\n        - **Custom PyTorch:** Placeholder for custom implementations; adjust as needed.\n\n6. **Training Pipeline:**\n    - Expanded the `train_model` method to support training for Vision, Text, and Tabular models:\n        - **Vision:** Utilizes FastAI's `ImageDataLoaders` and trains using `vision_learner`.\n        - **Text:** Utilizes FastAI's `TextDataLoaders` and trains using `text_classifier_learner`.\n        - **Tabular:** Demonstrates a simple Scikit-learn training pipeline with `RandomForestClassifier`.\n        - **Custom PyTorch:** Placeholder for custom training logic; implement as per requirements.\n\n7. **UI Enhancements:**\n    - Adjusted various UI components to be dynamic and respond to the selected model type.\n    - Enhanced error handling and output messaging to inform users of the application's state.\n\n8. **Logging:**\n    - Enhanced logging to provide detailed insights into the application's operations, making debugging easier.\n\n### Extending to More Model Types:\n\nTo further extend the application to support additional model types (e.g., **Tabular** using FastAI or **Custom PyTorch** models), follow these steps:\n\n1. **Add the Model Type to the ComboBox:**\n    - Update the `model_type_combo` with new model types.\n\n2. **Implement Model Loading Logic:**\n    - Define how each new model type should be loaded. For example, use `torch.load` for custom PyTorch models or extend FastAI's capabilities for other data types.\n\n3. **Design Training Pipelines:**\n    - Customize the `train_model` method to handle the specific requirements of training the new model types, including data preprocessing, model architecture selection, and evaluation metrics.\n\n4. **Handle Different Input Types:**\n    - Ensure that the input handling and prediction logic cater to the data formats required by the new model types.\n\n5. **Update the UI Accordingly:**\n    - Modify the UI to provide necessary options and feedback based on the new model types.\n\n### Dependencies:\n\nEnsure that all required libraries are installed. You can install missing dependencies using `pip`:\n\n```bash\npip install fastai scikit-learn pillow requests pygobject\n```\n\n**Note:** The above code assumes familiarity with FastAI and Scikit-learn. For integrating other frameworks like TensorFlow or custom models, additional adjustments will be necessary.\n\n### Final Remarks:\n\nThis enhanced application now supports multiple model types, providing a more versatile tool for users working with different kinds of machine learning models. By following the structured approach outlined above, you can continue to expand the application's capabilities to encompass a broader range of model types and functionalities.","behavior":"degenerate_repetition","conversation_hash":"223046847302d9f5f5bab0459f89ec96","conversation_redacted":false,"conversation_toxic":false,"country":"South Africa","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":87044,"n_turns_in_conversation":1,"n_words":6346,"source_row_offset":16212,"source_shard":"turns-00030.parquet","timestamp":"Thu, 26 Sep 2024 08:23:05 GMT","turn_id":"47a96b8c2e20b00126daf78c","turn_index":0,"user_text":"```python\nimport os\nimport sys\nimport threading\nimport logging\nfrom io import BytesIO\nfrom typing import Optional, Union\n\nimport gi\nimport requests\nfrom PIL import Image\nfrom fastai.vision.all import load_learner, PILImage, Learner, vision_learner, untar_data, URLs, ImageDataLoaders\n\ngi.require_version('Gtk', '3.0')\nfrom gi.repository import Gtk, GdkPixbuf, Gdk, GLib\n\n# Configure logging\nlogging.basicConfig(\n    level=logging.INFO,\n    format='%(asctime)s %(levelname)s:%(name)s: %(message)s',\n    handlers=[\n        logging.FileHandler(\"image_predictor_app.log\"),\n        logging.StreamHandler(sys.stdout)\n    ]\n)\nlogger = logging.getLogger(__name__)\n\n\nclass ImagePredictorApp(Gtk.Window):\n    def __init__(self):\n        super().__init__(title=\"Image Predictor with Model Training\")\n        self.set_border_width(10)\n        self.set_default_size(1200, 800)  # Increased size to accommodate training settings\n\n        # Initialize model attribute\n        self.model: Optional[Learner] = None\n\n        # Initialize UI components\n        self.init_ui()\n\n    def init_ui(self):\n        \"\"\"Initialize and arrange all UI components.\"\"\"\n        # Main container using Paned for resizable sections\n        paned = Gtk.Paned.new(Gtk.Orientation.HORIZONTAL)\n        self.add(paned)\n\n        # --- Left Pane: Controls and Training Settings ---\n        left_box = Gtk.Box(orientation=Gtk.Orientation.VERTICAL, spacing=10)\n        paned.pack1(left_box, resize=True, shrink=False)\n\n        # --- Controls Section ---\n        controls_frame = Gtk.Frame(label=\"Prediction Controls\")\n        controls_box = Gtk.Box(orientation=Gtk.Orientation.VERTICAL, spacing=10)\n        controls_box.set_margin_bottom(10)\n        controls_box.set_margin_top(10)\n        controls_box.set_margin_start(10)\n        controls_box.set_margin_end(10)\n        controls_frame.add(controls_box)\n        left_box.pack_start(controls_frame, False, False, 0)\n\n        # Load Model Button\n        self.load_model_button = Gtk.Button(label=\"Load Model (.pkl / .pth)\")\n        self.load_model_button.connect(\"clicked\", self.on_load_model)\n        controls_box.pack_start(self.load_model_button, False, False, 0)\n\n        # Image Selection\n        image_select_box = Gtk.Box(orientation=Gtk.Orientation.HORIZONTAL, spacing=5)\n        self.image_entry = Gtk.Entry()\n        self.image_entry.set_placeholder_text(\"Enter image path or URL\")\n        self.image_entry.connect(\"activate\", self.on_enter_image_path)\n        self.image_button = Gtk.Button(label=\"Browse Image\")\n        self.image_button.connect(\"clicked\", self.on_select_image)\n        image_select_box.pack_start(self.image_entry, True, True, 0)\n        image_select_box.pack_start(self.image_button, False, False, 0)\n        controls_box.pack_start(image_select_box, False, False, 0)\n\n        # Threshold Slider\n        threshold_box = Gtk.Box(orientation=Gtk.Orientation.VERTICAL, spacing=5)\n        self.threshold_label = Gtk.Label(label=\"Prediction Threshold: 50%\")\n        self.threshold_label.set_xalign(0)\n        threshold_box.pack_start(self.threshold_label, False, False, 0)\n\n        self.threshold_adjustment = Gtk.Adjustment(value=50, lower=0, upper=100, step_increment=1, page_increment=10)\n        self.threshold_slider = Gtk.Scale(orientation=Gtk.Orientation.HORIZONTAL, adjustment=self.threshold_adjustment)\n        self.threshold_slider.set_digits(0)\n        self.threshold_slider.connect(\"value-changed\", self.on_threshold_changed)\n        threshold_box.pack_start(self.threshold_slider, False, False, 0)\n        controls_box.pack_start(threshold_box, False, False, 0)\n\n        # Predict Button\n        self.predict_button = Gtk.Button(label=\"Predict\")\n        self.predict_button.connect(\"clicked\", self.on_predict)\n        self.predict_button.set_sensitive(False)\n        controls_box.pack_start(self.predict_button, False, False, 0)\n\n        # Output Display\n        self.output_label = Gtk.Label(label=\"Output will be displayed here.\")\n        self.output_label.set_line_wrap(True)\n        self.output_label.set_xalign(0)\n        self.output_label.set_justify(Gtk.Justification.LEFT)\n        controls_box.pack_start(self.output_label, False, False, 0)\n\n        # Progress Bar\n        self.progress_bar = Gtk.ProgressBar()\n        self.progress_bar.set_show_text(True)\n        self.progress_bar.set_visible(False)\n        controls_box.pack_start(self.progress_bar, False, False, 0)\n\n        # --- Training Settings Section ---\n        training_frame = Gtk.Frame(label=\"Model Training Settings\")\n        training_box = Gtk.Box(orientation=Gtk.Orientation.VERTICAL, spacing=10)\n        training_box.set_margin_bottom(10)\n        training_box.set_margin_top(10)\n        training_box.set_margin_start(10)\n        training_box.set_margin_end(10)\n        training_frame.add(training_box)\n        left_box.pack_start(training_frame, False, False, 0)\n\n        # Dataset Selection\n        dataset_select_box = Gtk.Box(orientation=Gtk.Orientation.HORIZONTAL, spacing=5)\n        self.dataset_entry = Gtk.Entry()\n        self.dataset_entry.set_placeholder_text(\"Enter dataset path or URL\")\n        self.dataset_button = Gtk.Button(label=\"Browse Dataset\")\n        self.dataset_button.connect(\"clicked\", self.on_select_dataset)\n        dataset_select_box.pack_start(self.dataset_entry, True, True, 0)\n        dataset_select_box.pack_start(self.dataset_button, False, False, 0)\n        training_box.pack_start(dataset_select_box, False, False, 0)\n\n        # Model Architecture Selection\n        architecture_box = Gtk.Box(orientation=Gtk.Orientation.HORIZONTAL, spacing=5)\n        architecture_label = Gtk.Label(label=\"Model Architecture:\")\n        architecture_label.set_xalign(0)\n        architecture_box.pack_start(architecture_label, False, False, 0)\n\n        self.architecture_combo = Gtk.ComboBoxText()\n        for arch in [\"resnet18\", \"resnet34\", \"resnet50\", \"resnet101\", \"resnet152\"]:\n            self.architecture_combo.append_text(arch)\n        self.architecture_combo.set_active(1)  # Default to resnet34\n        architecture_box.pack_start(self.architecture_combo, False, False, 0)\n        training_box.pack_start(architecture_box, False, False, 0)\n\n        # Hyperparameters\n        hyperparams_grid = Gtk.Grid(column_spacing=10, row_spacing=10)\n\n        # Learning Rate\n        lr_label = Gtk.Label(label=\"Learning Rate:\")\n        lr_label.set_halign(Gtk.Align.END)\n        hyperparams_grid.attach(lr_label, 0, 0, 1, 1)\n        self.lr_entry = Gtk.Entry()\n        self.lr_entry.set_text(\"1e-3\")\n        hyperparams_grid.attach(self.lr_entry, 1, 0, 1, 1)\n\n        # Batch Size\n        bs_label = Gtk.Label(label=\"Batch Size:\")\n        bs_label.set_halign(Gtk.Align.END)\n        hyperparams_grid.attach(bs_label, 0, 1, 1, 1)\n        self.bs_entry = Gtk.Entry()\n        self.bs_entry.set_text(\"32\")\n        hyperparams_grid.attach(self.bs_entry, 1, 1, 1, 1)\n\n        # Number of Epochs\n        epochs_label = Gtk.Label(label=\"Number of Epochs:\")\n        epochs_label.set_halign(Gtk.Align.END)\n        hyperparams_grid.attach(epochs_label, 0, 2, 1, 1)\n        self.epochs_entry = Gtk.Entry()\n        self.epochs_entry.set_text(\"10\")\n        hyperparams_grid.attach(self.epochs_entry, 1, 2, 1, 1)\n\n        # Save Model Name\n        save_label = Gtk.Label(label=\"Save Model As:\")\n        save_label.set_halign(Gtk.Align.END)\n        hyperparams_grid.attach(save_label, 0, 3, 1, 1)\n        self.save_entry = Gtk.Entry()\n        self.save_entry.set_text(\"trained_model.pkl\")\n        hyperparams_grid.attach(self.save_entry, 1, 3, 1, 1)\n\n        training_box.pack_start(hyperparams_grid, False, False, 0)\n\n        # Train Model Button\n        self.train_button = Gtk.Button(label=\"Train Model\")\n        self.train_button.connect(\"clicked\", self.on_train_model)\n        training_box.pack_start(self.train_button, False, False, 0)\n\n        # Training Output Display\n        self.training_output = Gtk.TextView()\n        self.training_output.set_editable(False)\n        self.training_output.set_wrap_mode(Gtk.WrapMode.WORD)\n        training_output_scrolled = Gtk.ScrolledWindow()\n        training_output_scrolled.set_vexpand(True)\n        training_output_scrolled.add(self.training_output)\n        training_box.pack_start(training_output_scrolled, True, True, 0)\n\n        # --- Right Pane: Image Preview ---\n        preview_frame = Gtk.Frame(label=\"Image Preview\")\n        preview_box = Gtk.Box(orientation=Gtk.Orientation.VERTICAL, spacing=5)\n        preview_box.set_margin_bottom(10)\n        preview_box.set_margin_top(10)\n        preview_box.set_margin_start(10)\n        preview_box.set_margin_end(10)\n        preview_frame.add(preview_box)\n\n        self.image_preview = Gtk.Image()\n        self.image_preview.set_valign(Gtk.Align.START)\n        preview_box.pack_start(self.image_preview, True, True, 0)\n\n        # Drag and Drop Support for Image Preview\n        self.image_preview.drag_dest_set(Gtk.DestDefaults.ALL, [], Gdk.DragAction.COPY)  # Corrected this line\n        self.image_preview.drag_dest_add_uri_targets()\n        self.image_preview.connect(\"drag-data-received\", self.on_drag_data_received)\n\n        paned.pack2(preview_frame, resize=True, shrink=False)\n\n    # ------------------ Prediction Controls ------------------\n\n    def on_drag_data_received(self, widget, drag_context, x, y, data, info, time):\n        \"\"\"Handle drag-and-drop of image files or URLs.\"\"\"\n        uris = data.get_uris()\n        if uris:\n            uri = uris[0]\n            path_or_url = self.parse_uri(uri)\n            if path_or_url:\n                self.image_entry.set_text(path_or_url)\n                self.display_image(path_or_url)\n        Gtk.drag_finish(drag_context, True, False, time)\n\n    @staticmethod\n    def parse_uri(uri: str) -> Optional[str]:\n        \"\"\"Parse URI to get local path or URL.\"\"\"\n        if uri.startswith(\"file://\"):\n            return uri[7:]\n        elif uri.startswith((\"http://\", \"https://\", \"ftp://\")):\n            return uri\n        else:\n            return None\n\n    def on_enter_image_path(self, widget):\n        \"\"\"Handle pressing Enter in the image entry.\"\"\"\n        image_input = self.image_entry.get_text().strip()\n        if image_input:\n            self.display_image(image_input)\n\n    def on_load_model(self, button: Gtk.Button):\n        \"\"\"Handle the Load Model button click.\"\"\"\n        dialog = Gtk.FileChooserDialog(\n            title=\"Select a Model File\",\n            parent=self,\n            action=Gtk.FileChooserAction.OPEN\n        )\n        dialog.add_buttons(\n            Gtk.STOCK_CANCEL, Gtk.ResponseType.CANCEL,\n            Gtk.STOCK_OPEN, Gtk.ResponseType.OK\n        )\n        dialog.set_filter(self.create_model_filter())\n\n        response = dialog.run()\n        if response == Gtk.ResponseType.OK:\n            model_path = dialog.get_filename()\n            logger.info(f\"Selected model file: {model_path}\")\n            self.load_model_async(model_path)\n        dialog.destroy()\n\n    @staticmethod\n    def create_model_filter() -> Gtk.FileFilter:\n        \"\"\"Create a file filter for model files.\"\"\"\n        file_filter = Gtk.FileFilter()\n        file_filter.set_name(\"Model Files (*.pkl, *.pth)\")\n        file_filter.add_pattern(\"*.pkl\")\n        file_filter.add_pattern(\"*.pth\")\n        return file_filter\n\n    def load_model_async(self, model_path: str):\n        \"\"\"Load the model in a separate thread to keep UI responsive.\"\"\"\n        self.set_controls_sensitive(False)\n        self.show_progress(\"Loading model...\")\n        thread = threading.Thread(target=self.load_model, args=(model_path,), daemon=True)\n        thread.start()\n\n    def load_model(self, model_path: str):\n        \"\"\"Load the machine learning model from the specified path.\"\"\"\n        try:\n            learner = load_learner(model_path)\n            GLib.idle_add(self.on_model_loaded, learner, model_path)\n            logger.info(f\"Model loaded successfully from: {model_path}\")\n        except Exception as e:\n            logger.error(f\"Failed to load model: {e}\", exc_info=True)\n            GLib.idle_add(self.on_model_load_failed, str(e))\n\n    def on_model_loaded(self, learner: Learner, model_path: str):\n        \"\"\"Callback when the model is successfully loaded.\"\"\"\n        self.model = learner\n        self.predict_button.set_sensitive(True)\n        self.output_label.set_markup(f\"<span foreground='green'>Model loaded successfully from:</span>\\n{model_path}\")\n        self.hide_progress()\n        self.set_controls_sensitive(True)\n\n    def on_model_load_failed(self, error_message: str):\n        \"\"\"Callback when the model fails to load.\"\"\"\n        self.model = None\n        self.predict_button.set_sensitive(False)\n        self.output_label.set_markup(f\"<span foreground='red'>Failed to load model:</span>\\n{error_message}\")\n        self.hide_progress()\n        self.set_controls_sensitive(True)\n\n    def on_select_image(self, button: Gtk.Button):\n        \"\"\"Handle the Select Image button click.\"\"\"\n        dialog = Gtk.FileChooserDialog(\n            title=\"Select an Image\",\n            parent=self,\n            action=Gtk.FileChooserAction.OPEN\n        )\n        dialog.add_buttons(\n            Gtk.STOCK_CANCEL, Gtk.ResponseType.CANCEL,\n            Gtk.STOCK_OPEN, Gtk.ResponseType.OK\n        )\n        dialog.set_filter(self.create_image_filter())\n\n        response = dialog.run()\n        if response == Gtk.ResponseType.OK:\n            image_path = dialog.get_filename()\n            self.image_entry.set_text(image_path)\n            self.display_image(image_path)\n            logger.info(f\"Selected image file: {image_path}\")\n        dialog.destroy()\n\n    @staticmethod\n    def create_image_filter() -> Gtk.FileFilter:\n        \"\"\"Create a file filter for image files.\"\"\"\n        img_filter = Gtk.FileFilter()\n        img_filter.set_name(\"Image Files (*.png, *.jpg, *.jpeg, *.webp)\")\n        img_filter.add_pattern(\"*.png\")\n        img_filter.add_pattern(\"*.jpg\")\n        img_filter.add_pattern(\"*.jpeg\")\n        img_filter.add_pattern(\"*.webp\")\n        img_filter.add_mime_type(\"image/png\")\n        img_filter.add_mime_type(\"image/jpeg\")\n        img_filter.add_mime_type(\"image/webp\")\n        return img_filter\n\n    def on_threshold_changed(self, scale: Gtk.Scale):\n        \"\"\"Update the threshold label when the slider value changes.\"\"\"\n        value = self.threshold_slider.get_value()\n        self.threshold_label.set_text(f\"Prediction Threshold: {int(value)}%\")\n\n    def on_predict(self, button: Gtk.Button):\n        \"\"\"Handle the Predict button click.\"\"\"\n        if not self.model:\n            self.output_label.set_markup(\"<span foreground='red'>Please load a model first.</span>\")\n            return\n\n        image_input = self.image_entry.get_text().strip()\n        if not image_input:\n            self.output_label.set_markup(\"<span foreground='red'>Please provide an image path or URL.</span>\")\n            return\n\n        self.set_controls_sensitive(False)\n        self.show_progress(\"Performing prediction...\")\n        thread = threading.Thread(target=self.predict_image, args=(image_input,), daemon=True)\n        thread.start()\n\n    def predict_image(self, image_input: str):\n        \"\"\"Load the image and perform prediction in a separate thread.\"\"\"\n        try:\n            image = self.load_image(image_input)\n            GLib.idle_add(self.display_image, image_input)\n            pred, pred_idx, outputs = self.model.predict(image)\n            probabilities = outputs.numpy() * 100  # Convert to percentage\n            classes = self.model.dls.vocab  # Get class names\n            threshold = self.threshold_slider.get_value()  # Get the threshold value\n\n            # Prepare formatted prediction results\n            output_lines = [\"<b>Predictions:</b>\"]\n            for class_name, probability in zip(classes, probabilities):\n                if probability >= threshold:\n                    line = f\"\u2022 {class_name}: {probability:.2f}%\"\n                    output_lines.append(line)\n            output_lines.append(f\"\\n<b>Overall Prediction:</b> {pred}\")\n\n            # Update the output label using Pango Markup for better formatting\n            markup_text = \"\\n\".join(output_lines)\n            GLib.idle_add(self.update_output, markup_text)\n            logger.info(f\"Prediction successful for image: {image_input}\")\n        except Exception as e:\n            logger.error(f\"Error during prediction: {e}\", exc_info=True)\n            GLib.idle_add(self.update_output, f\"<span foreground='red'>Error during prediction:</span>\\n{e}\")\n        finally:\n            GLib.idle_add(self.hide_progress)\n            GLib.idle_add(self.set_controls_sensitive, True)\n\n    def load_image(self, image_input: str) -> PILImage:\n        \"\"\"\n        Load an image from a local path or a URL.\n\n        Args:\n            image_input (str): The path or URL of the image.\n\n        Returns:\n            PILImage: The loaded image.\n        \"\"\"\n        logger.debug(f\"Loading image from: {image_input}\")\n        if image_input.startswith(('http://', 'https://', 'ftp://')):\n            response = requests.get(image_input, timeout=10)\n            response.raise_for_status()\n            image = PILImage.create(BytesIO(response.content))\n            logger.debug(\"Image loaded from URL.\")\n            return image\n        elif os.path.isfile(image_input):\n            image = PILImage.create(image_input)\n            logger.debug(\"Image loaded from local file.\")\n            return image\n        else:\n            logger.error(\"Invalid image path or URL.\")\n            raise ValueError(\"Invalid image path or URL.\")\n\n    def display_image(self, image_input: str):\n        \"\"\"\n        Display the selected image in the image preview widget.\n\n        Args:\n            image_input (str): The path or URL of the image.\n        \"\"\"\n        try:\n            if image_input.startswith(('http://', 'https://', 'ftp://')):\n                response = requests.get(image_input, timeout=10)\n                response.raise_for_status()\n                loader = GdkPixbuf.PixbufLoader.new()\n                loader.write(response.content)\n                loader.close()\n                pixbuf = loader.get_pixbuf()\n            else:\n                pixbuf = GdkPixbuf.Pixbuf.new_from_file(image_input)\n\n            # Calculate scaling while preserving aspect ratio\n            max_width, max_height = 600, 600\n            width = pixbuf.get_width()\n            height = pixbuf.get_height()\n            scaling_factor = min(max_width / width, max_height / height, 1)\n            new_width = int(width * scaling_factor)\n            new_height = int(height * scaling_factor)\n\n            scaled_pixbuf = pixbuf.scale_simple(new_width, new_height, GdkPixbuf.InterpType.BILINEAR)\n            self.image_preview.set_from_pixbuf(scaled_pixbuf)\n            logger.debug(\"Image displayed successfully.\")\n        except Exception as e:\n            logger.error(f\"Failed to display image: {e}\", exc_info=True)\n            self.image_preview.set_from_icon_name(\"image-missing\", Gtk.IconSize.DIALOG)\n            self.update_output(f\"<span foreground='red'>Failed to display image preview:</span>\\n{e}\")\n\n    def update_output(self, text: str):\n        \"\"\"Update the output label with the given text.\"\"\"\n        self.output_label.set_markup(text)\n\n    def set_controls_sensitive(self, sensitive: bool):\n        \"\"\"Enable or disable controls based on the sensitive flag.\"\"\"\n        self.load_model_button.set_sensitive(sensitive)\n        self.image_button.set_sensitive(sensitive)\n        self.image_entry.set_sensitive(sensitive)\n        self.threshold_slider.set_sensitive(sensitive)\n        self.predict_button.set_sensitive(sensitive and self.model is not None)\n        self.train_button.set_sensitive(sensitive)\n        self.dataset_button.set_sensitive(sensitive)\n        self.dataset_entry.set_sensitive(sensitive)\n        self.architecture_combo.set_sensitive(sensitive)\n        self.lr_entry.set_sensitive(sensitive)\n        self.bs_entry.set_sensitive(sensitive)\n        self.epochs_entry.set_sensitive(sensitive)\n        self.save_entry.set_sensitive(sensitive)\n\n    def show_progress(self, message: str):\n        \"\"\"Display the progress bar with a message.\"\"\"\n        self.progress_bar.set_visible(True)\n        self.progress_bar.set_fraction(0.0)\n        self.progress_bar.set_text(message)\n        self.progress_bar.pulse()\n\n        # Start a timeout to animate the progress bar\n        GLib.timeout_add(100, self.animate_progress)\n\n    def animate_progress(self) -> bool:\n        \"\"\"Animate the progress bar.\"\"\"\n        self.progress_bar.pulse()\n        return True  # Continue calling\n\n    def hide_progress(self):\n        \"\"\"Hide the progress bar.\"\"\"\n        self.progress_bar.set_visible(False)\n        self.progress_bar.set_text(\"\")\n\n    # ------------------ Model Training Controls ------------------\n\n    def on_select_dataset(self, button: Gtk.Button):\n        \"\"\"Handle the Browse Dataset button click.\"\"\"\n        dialog = Gtk.FileChooserDialog(\n            title=\"Select a Dataset Directory\",\n            parent=self,\n            action=Gtk.FileChooserAction.SELECT_FOLDER\n        )\n        dialog.add_buttons(\n            Gtk.STOCK_CANCEL, Gtk.ResponseType.CANCEL,\n            Gtk.STOCK_OPEN, Gtk.ResponseType.OK\n        )\n\n        response = dialog.run()\n        if response == Gtk.ResponseType.OK:\n            dataset_path = dialog.get_filename()\n            self.dataset_entry.set_text(dataset_path)\n            logger.info(f\"Selected dataset directory: {dataset_path}\")\n        dialog.destroy()\n\n    def on_train_model(self, button: Gtk.Button):\n        \"\"\"Handle the Train Model button click.\"\"\"\n        dataset_input = self.dataset_entry.get_text().strip()\n        architecture = self.architecture_combo.get_active_text()\n        lr = self.lr_entry.get_text().strip()\n        bs = self.bs_entry.get_text().strip()\n        epochs = self.epochs_entry.get_text().strip()\n        save_model_name = self.save_entry.get_text().strip()\n\n        # Validate inputs\n        if not dataset_input or not os.path.isdir(dataset_input):\n            self.append_training_output(\"Invalid dataset path.\", error=True)\n            return\n\n        if not architecture:\n            self.append_training_output(\"Please select a model architecture.\", error=True)\n            return\n\n        try:\n            lr = float(lr)\n            bs = int(bs)\n            epochs = int(epochs)\n        except ValueError:\n            self.append_training_output(\"Learning rate must be a float, Batch size and Epochs must be integers.\", error=True)\n            return\n\n        if not save_model_name:\n            self.append_training_output(\"Please specify a name to save the trained model.\", error=True)\n            return\n\n        # Start training in a separate thread\n        self.set_controls_sensitive(False)\n        self.show_training_progress(\"Starting model training...\")\n        thread = threading.Thread(\n            target=self.train_model,\n            args=(dataset_input, architecture, lr, bs, epochs, save_model_name),\n            daemon=True\n        )\n        thread.start()\n\n    def train_model(self, dataset_path: str, architecture: str, lr: float, bs: int, epochs: int, save_model_name: str):\n        \"\"\"Train the model with the specified settings.\"\"\"\n        try:\n            logger.info(f\"Starting training with architecture: {architecture}, LR: {lr}, BS: {bs}, Epochs: {epochs}\")\n            GLib.idle_add(self.append_training_output, f\"Loading dataset from: {dataset_path}\")\n\n            # Create ImageDataLoaders\n            data = ImageDataLoaders.from_folder(\n                dataset_path,\n                valid_pct=0.2,\n                item_tfms=Resize(224),\n                batch_tfms=aug_transforms(),\n                bs=bs\n            )\n\n            # Initialize the learner\n            learner = vision_learner(data, arch=getattr(models, architecture)(), metrics=accuracy)\n\n            # Start training\n            GLib.idle_add(self.append_training_output, \"Starting training...\")\n            learner.fine_tune(epochs, base_lr=lr, callbacks=[TrainingCallback(self)])\n\n            # Save the trained model\n            learner.export(save_model_name)\n            logger.info(f\"Model trained and saved as: {save_model_name}\")\n            GLib.idle_add(self.append_training_output, f\"Model trained and saved as: {save_model_name}\", success=True)\n\n            # Optionally, load the newly trained model\n            GLib.idle_add(self.load_model_async, save_model_name)\n\n        except Exception as e:\n            logger.error(f\"Training failed: {e}\", exc_info=True)\n            GLib.idle_add(self.append_training_output, f\"Training failed: {e}\", error=True)\n        finally:\n            GLib.idle_add(self.hide_training_progress)\n            GLib.idle_add(self.set_controls_sensitive, True)\n\n    def show_training_progress(self, message: str):\n        \"\"\"Display the training progress bar with a message.\"\"\"\n        self.progress_bar.set_visible(True)\n        self.progress_bar.set_fraction(0.0)\n        self.progress_bar.set_text(message)\n        self.progress_bar.pulse()\n\n        # Start a timeout to animate the progress bar\n        GLib.timeout_add(100, self.animate_progress)\n\n    def hide_training_progress(self):\n        \"\"\"Hide the training progress bar.\"\"\"\n        self.progress_bar.set_visible(False)\n        self.progress_bar.set_text(\"\")\n\n    def append_training_output(self, message: str, error: bool = False, success: bool = False):\n        \"\"\"Append messages to the training output TextView.\"\"\"\n        buffer = self.training_output.get_buffer()\n        end_iter = buffer.get_end_iter()\n        if error:\n            formatted_message = f\"<span foreground='red'>{message}</span>\\n\"\n        elif success:\n            formatted_message = f\"<span foreground='green'>{message}</span>\\n\"\n        else:\n            formatted_message = f\"{message}\\n\"\n        buffer.insert_markup(end_iter, formatted_message)\n\n    # ------------------ Model Training Callbacks ------------------\n\n    class TrainingCallback(Callback):\n        \"\"\"A callback to monitor training progress and output.\"\"\"\n\n        def __init__(self, app):\n            self.app = app\n\n        def after_epoch(self):\n            epoch = self.learn.epoch\n            loss = self.learn.recorder.losses[-1]\n            acc = self.learn.recorder.metrics[-1]\n            message = f\"Epoch {epoch + 1}: Loss={loss:.4f}, Accuracy={acc * 100:.2f}%\"\n            GLib.idle_add(self.app.append_training_output, message)\n\n        def after_fit(self):\n            GLib.idle_add(self.app.append_training_output, \"Training completed successfully.\", success=True)\n\n    # ------------------ Prediction Functions ------------------\n\n    def load_image(self, image_input: str) -> PILImage:\n        \"\"\"\n        Load an image from a local path or a URL.\n\n        Args:\n            image_input (str): The path or URL of the image.\n\n        Returns:\n            PILImage: The loaded image.\n        \"\"\"\n        logger.debug(f\"Loading image from: {image_input}\")\n        if image_input.startswith(('http://', 'https://', 'ftp://')):\n            response = requests.get(image_input, timeout=10)\n            response.raise_for_status()\n            image = PILImage.create(BytesIO(response.content))\n            logger.debug(\"Image loaded from URL.\")\n            return image\n        elif os.path.isfile(image_input):\n            image = PILImage.create(image_input)\n            logger.debug(\"Image loaded from local file.\")\n            return image\n        else:\n            logger.error(\"Invalid image path or URL.\")\n            raise ValueError(\"Invalid image path or URL.\")\n\n    def display_image(self, image_input: str):\n        \"\"\"\n        Display the selected image in the image preview widget.\n\n        Args:\n            image_input (str): The path or URL of the image.\n        \"\"\"\n        try:\n            if image_input.startswith(('http://', 'https://', 'ftp://')):\n                response = requests.get(image_input, timeout=10)\n                response.raise_for_status()\n                loader = GdkPixbuf.PixbufLoader.new()\n                loader.write(response.content)\n                loader.close()\n                pixbuf = loader.get_pixbuf()\n            else:\n                pixbuf = GdkPixbuf.Pixbuf.new_from_file(image_input)\n\n            # Calculate scaling while preserving aspect ratio\n            max_width, max_height = 600, 600\n            width = pixbuf.get_width()\n            height = pixbuf.get_height()\n            scaling_factor = min(max_width / width, max_height / height, 1)\n            new_width = int(width * scaling_factor)\n            new_height = int(height * scaling_factor)\n\n            scaled_pixbuf = pixbuf.scale_simple(new_width, new_height, GdkPixbuf.InterpType.BILINEAR)\n            self.image_preview.set_from_pixbuf(scaled_pixbuf)\n            logger.debug(\"Image displayed successfully.\")\n        except Exception as e:\n            logger.error(f\"Failed to display image: {e}\", exc_info=True)\n            self.image_preview.set_from_icon_name(\"image-missing\", Gtk.IconSize.DIALOG)\n            self.update_output(f\"<span foreground='red'>Failed to display image preview:</span>\\n{e}\")\n\n    def update_output(self, text: str):\n        \"\"\"Update the output label with the given text.\"\"\"\n        self.output_label.set_markup(text)\n\n    # ------------------ Utility Functions ------------------\n\n    def set_controls_sensitive(self, sensitive: bool):\n        \"\"\"Enable or disable controls based on the sensitive flag.\"\"\"\n        # Prediction Controls\n        self.load_model_button.set_sensitive(sensitive)\n        self.image_button.set_sensitive(sensitive)\n        self.image_entry.set_sensitive(sensitive)\n        self.threshold_slider.set_sensitive(sensitive)\n        self.predict_button.set_sensitive(sensitive and self.model is not None)\n\n        # Training Controls\n        self.train_button.set_sensitive(sensitive)\n        self.dataset_button.set_sensitive(sensitive)\n        self.dataset_entry.set_sensitive(sensitive)\n        self.architecture_combo.set_sensitive(sensitive)\n        self.lr_entry.set_sensitive(sensitive)\n        self.bs_entry.set_sensitive(sensitive)\n        self.epochs_entry.set_sensitive(sensitive)\n        self.save_entry.set_sensitive(sensitive)\n\n    # ------------------ Model Training Functions ------------------\n\n    def on_train_model(self, button: Gtk.Button):\n        \"\"\"Handle the Train Model button click.\"\"\"\n        dataset_input = self.dataset_entry.get_text().strip()\n        architecture = self.architecture_combo.get_active_text()\n        lr = self.lr_entry.get_text().strip()\n        bs = self.bs_entry.get_text().strip()\n        epochs = self.epochs_entry.get_text().strip()\n        save_model_name = self.save_entry.get_text().strip()\n\n        # Validate inputs\n        if not dataset_input or not os.path.isdir(dataset_input):\n            self.append_training_output(\"Invalid dataset path.\", error=True)\n            return\n\n        if not architecture:\n            self.append_training_output(\"Please select a model architecture.\", error=True)\n            return\n\n        try:\n            lr = float(lr)\n            bs = int(bs)\n            epochs = int(epochs)\n        except ValueError:\n            self.append_training_output(\"Learning rate must be a float, Batch size and Epochs must be integers.\", error=True)\n            return\n\n        if not save_model_name:\n            self.append_training_output(\"Please specify a name to save the trained model.\", error=True)\n            return\n\n        # Start training in a separate thread\n        self.set_controls_sensitive(False)\n        self.show_training_progress(\"Starting model training...\")\n        thread = threading.Thread(\n            target=self.train_model,\n            args=(dataset_input, architecture, lr, bs, epochs, save_model_name),\n            daemon=True\n        )\n        thread.start()\n\n    def train_model(self, dataset_path: str, architecture: str, lr: float, bs: int, epochs: int, save_model_name: str):\n        \"\"\"Train the model with the specified settings.\"\"\"\n        try:\n            logger.info(f\"Starting training with architecture: {architecture}, LR: {lr}, BS: {bs}, Epochs: {epochs}\")\n            GLib.idle_add(self.append_training_output, f\"Loading dataset from: {dataset_path}\")\n\n            from fastai.vision.all import models, accuracy, Callback, Resize, aug_transforms\n\n            # Create ImageDataLoaders\n            data = ImageDataLoaders.from_folder(\n                dataset_path,\n                valid_pct=0.2,\n                item_tfms=Resize(224),\n                batch_tfms=aug_transforms(),\n                bs=bs\n            )\n            logger.info(\"ImageDataLoaders created successfully.\")\n            GLib.idle_add(self.append_training_output, \"Dataset loaded successfully.\")\n\n            # Initialize the learner\n            learner = vision_learner(data, arch=getattr(models, architecture)(), metrics=accuracy)\n            logger.info(\"Learner initialized successfully.\")\n            GLib.idle_add(self.append_training_output, f\"Initialized learner with architecture: {architecture}\")\n\n            # Start training with fine-tuning\n            GLib.idle_add(self.append_training_output, \"Starting training...\")\n            learner.fine_tune(epochs, base_lr=lr, callbacks=[self.TrainingCallback(self)])\n\n            # Save the trained model\n            learner.export(save_model_name)\n            logger.info(f\"Model trained and saved as: {save_model_name}\")\n            GLib.idle_add(self.append_training_output, f\"Model trained and saved as: {save_model_name}\", success=True)\n\n            # Automatically load the newly trained model\n            GLib.idle_add(self.load_model_async, save_model_name)\n\n        except Exception as e:\n            logger.error(f\"Training failed: {e}\", exc_info=True)\n            GLib.idle_add(self.append_training_output, f\"Training failed: {e}\", error=True)\n        finally:\n            GLib.idle_add(self.hide_training_progress)\n            GLib.idle_add(self.set_controls_sensitive, True)\n\n    def show_training_progress(self, message: str):\n        \"\"\"Display the training progress bar with a message.\"\"\"\n        self.progress_bar.set_visible(True)\n        self.progress_bar.set_fraction(0.0)\n        self.progress_bar.set_text(message)\n        self.progress_bar.pulse()\n\n        # Start a timeout to animate the progress bar\n        GLib.timeout_add(100, self.animate_progress)\n\n    def hide_training_progress(self):\n        \"\"\"Hide the training progress bar.\"\"\"\n        self.progress_bar.set_visible(False)\n        self.progress_bar.set_text(\"\")\n\n    def append_training_output(self, message: str, error: bool = False, success: bool = False):\n        \"\"\"Append messages to the training output TextView.\"\"\"\n        buffer = self.training_output.get_buffer()\n        end_iter = buffer.get_end_iter()\n        if error:\n            formatted_message = f\"<span foreground='red'>{message}</span>\\n\"\n        elif success:\n            formatted_message = f\"<span foreground='green'>{message}</span>\\n\"\n        else:\n            formatted_message = f\"{message}\\n\"\n        buffer.insert_markup(end_iter, formatted_message)\n\n    # ------------------ Model Training Callback Class ------------------\n\n    from fastai.callback.core import Callback\n\n    class TrainingCallback(Callback):\n        \"\"\"A callback to monitor training progress and output.\"\"\"\n\n        def __init__(self, app):\n            self.app = app\n\n        def after_epoch(self):\n            epoch = self.learn.epoch\n            loss = self.learn.recorder.losses[-1]\n            acc = self.learn.recorder.metrics[-1]\n            message = f\"Epoch {epoch + 1}: Loss={loss:.4f}, Accuracy={acc * 100:.2f}%\"\n            GLib.idle_add(self.app.append_training_output, message)\n\n        def after_fit(self):\n            GLib.idle_add(self.app.append_training_output, \"Training completed successfully.\", success=True)\n\n    # ------------------ Main Function ------------------\n\n    def main():\n        \"\"\"Initialize and run the GTK application.\"\"\"\n        app = ImagePredictorApp()\n        app.connect(\"destroy\", Gtk.main_quit)\n        app.show_all()\n        Gtk.main()\n\n\n    if __name__ == \"__main__\":\n        main()\n```\nAdd support for other model types, rather than just Vision models."},{"assistant_initiates":false,"asst_text":"\u5728\u65b0\u7684\u4e13\u4e1a\u9ad8\u7b49\u6559\u80b2\u4f53\u7cfb\u4e2d\u3002\u6559\u80b2\u90e8\u5f3a\u8c03\uff0c\u4f1a\u5bf9\u73b0\u884c\u7684\u4e13\u4e1a\u548c\u66f4\u65b0\u540e\u7684\u6559\u80b2\u7cfb\u7edf\u65b9\u5411\u8fdb\u884c\u5339\u914d\uff0c\u4ee5\u786e\u4fdd\u5e73\u7a33\u8fc7\u6e21\u3002\u5f53\u524d\u53c2\u4e0e\u5b66\u58eb\u8bfe\u7a0b\u7684\u5b66\u751f\u4f9d\u65e7\u6709\u6743\u5b8c\u6210\u4ed6\u4eec\u7684\u73b0\u6709\u5b66\u4e60\u8def\u5f84\uff0c\u5e76\u5728\u6bd5\u4e1a\u540e\u9009\u62e9\u7ee7\u7eed\u8ffd\u6c42\u4e13\u4e1a\u9ad8\u7b49\u6559\u80b2\u6216\u7855\u58eb\u8bfe\u7a0b\u3002\n\n\u8fd9\u79cd\u6539\u9769\u53cd\u6620\u51fa\u4fc4\u7f57\u65af\u6559\u80b2\u653f\u7b56\u6b63\u5728\u5411\u66f4\u52a0\u7075\u6d3b\u548c\u4ee5\u804c\u4e1a\u4e3a\u5bfc\u5411\u7684\u65b9\u5f0f\u503e\u659c\u3002\u8fd9\u4e3b\u8981\u662f\u4e3a\u4e86\u9002\u5e94\u5feb\u901f\u53d8\u5316\u7684\u52b3\u52a8\u529b\u5e02\u573a\u9700\u6c42\uff0c\u786e\u4fdd\u5b66\u751f\u5728\u8fdb\u5165\u804c\u573a\u65f6\u5177\u5907\u8db3\u591f\u7684\u4e13\u4e1a\u6280\u80fd\u548c\u7ade\u4e89\u529b\u3002\n\n\u603b\u4f53\u6765\u8bf4\uff0c\u65b0\u7684\u6559\u80b2\u653f\u7b56\u8bd5\u56fe\u7f29\u77ed\u5b66\u751f\u7684\u5b66\u4e60\u65f6\u95f4\uff0c\u4f7f\u5f97\u4ed6\u4eec\u80fd\u591f\u66f4\u65e9\u8e0f\u5165\u804c\u4e1a\u751f\u6daf\uff0c\u4ece\u800c\u5728\u5feb\u901f\u53d8\u5316\u7684\u5168\u7403\u73af\u5883\u4e2d\u4fdd\u6301\u7ade\u4e89\u529b\u3002\u4e0d\u8fc7\uff0c\u8fd9\u9879\u53d8\u9769\u4e5f\u9700\u8981\u8003\u8651\u5230\u8fc7\u6e21\u8fc7\u7a0b\u4e2d\u7684\u6311\u6218\uff0c\u4f8b\u5982\u5982\u4f55\u5e73\u8861\u57fa\u7840\u4e0e\u4e13\u4e1a\u6559\u80b2\uff0c\u786e\u4fdd\u8d28\u91cf\u548c\u6709\u6548\u6027\uff0c\u4ee5\u53ca\u5b66\u751f\u548c\u793e\u4f1a\u5bf9\u4e8e\u8fd9\u79cd\u53d8\u5316\u7684\u9002\u5e94\u80fd\u529b\u3002","behavior":"degenerate_repetition","conversation_hash":"c561d136c7edb36c412430a181542693","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":"Chinese","model":"gpt-4o-2024-08-06","n_chars":3337,"n_turns_in_conversation":11,"n_words":366,"source_row_offset":16213,"source_shard":"turns-00030.parquet","timestamp":"Thu, 26 Sep 2024 08:23:29 GMT","turn_id":"0fdd1104c8c0b4f4165af75b","turn_index":0,"user_text":"\u00ab\u0420\u0430\u043d\u044c\u0448\u0435 \u0438 \u0431\u044b\u0441\u0442\u0440\u0435\u0435\u00bb: \u041c\u0435\u0434\u0438\u043d\u0441\u043a\u0438\u0439 \u0434\u043e\u043f\u0443\u0441\u0442\u0438\u043b \u0443\u043c\u0435\u043d\u044c\u0448\u0435\u043d\u0438\u0435 \u0441\u0440\u043e\u043a\u043e\u0432 \u0443\u0447\u0435\u0431\u044b \u0432 \u0448\u043a\u043e\u043b\u0430\u0445 \u0438 \u0432\u0443\u0437\u0430\u0445\n\u00ab\u0420\u0430\u043d\u044c\u0448\u0435 \u0438 \u0431\u044b\u0441\u0442\u0440\u0435\u0435\u00bb: \u041c\u0435\u0434\u0438\u043d\u0441\u043a\u0438\u0439 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\u7b80\u4ecb\n2. \u80cc\u666f\u4e0e\u5206\u6790\n   - \u5f53\u524d\u4fc4\u7f57\u65af\u6559\u80b2\u4f53\u7cfb\u6982\u51b5\n   - \u653f\u7b56\u53d8\u5316\u7684\u52a8\u56e0\n3. \u653f\u7b56\u53d8\u5316\u7684\u7ec6\u8282\n   - \u4e2d\u5c0f\u5b66\u6559\u80b2\u671f\u9650\u7684\u8c03\u6574\n   - \u9ad8\u7b49\u6559\u80b2\u7ed3\u6784\u7684\u53d8\u9769\n4. \u5f71\u54cd\u5206\u6790\n   - \u5bf9\u5b66\u751f\u7684\u5f71\u54cd\n   - \u5bf9\u6559\u80b2\u673a\u6784\u7684\u5f71\u54cd\n   - \u5bf9\u52b3\u52a8\u529b\u5e02\u573a\u7684\u6f5c\u5728\u5f71\u54cd\n5. \u56fd\u9645\u5bf9\u6bd4\n   - \u4e0e\u5176\u4ed6\u56fd\u5bb6\u7684\u6559\u80b2\u4f53\u7cfb\u5bf9\u6bd4\n   - \u5176\u4ed6\u56fd\u5bb6\u7684\u76f8\u5173\u6539\u9769\u7ecf\u9a8c\n6. \u6311\u6218\u4e0e\u673a\u9047\n   - \u6539\u9769\u8fc7\u7a0b\u4e2d\u53ef\u80fd\u9047\u5230\u7684\u6311\u6218\n   - \u6539\u9769\u5e26\u6765\u7684\u65b0\u673a\u9047\n7. \u7ed3\u8bba\u4e0e\u5efa\u8bae\n   - \u4e3a\u653f\u7b56\u5236\u5b9a\u8005\u7684\u5efa\u8bae\n   - 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**\u5bf9\u52b3\u52a8\u529b\u5e02\u573a\u7684\u6f5c\u5728\u5f71\u54cd**\uff1a\u8fd9\u53ef\u80fd\u5bfc\u81f4\u6bd5\u4e1a\u751f\u6570\u91cf\u589e\u52a0\uff0c\u5e02\u573a\u7ade\u4e89\u52a0\u5267\u3002\u540c\u65f6\uff0c\u62e5\u6709\u66f4\u4e13\u4e1a\u6280\u80fd\u7684\u65b0\u578b\u6bd5\u4e1a\u751f\u53ef\u80fd\u63a8\u52a8\u884c\u4e1a\u521b\u65b0\u4e0e\u53d1\u5c55\u3002\n\n**5. \u56fd\u9645\u5bf9\u6bd4**\n\n- **\u4e0e\u5176\u4ed6\u56fd\u5bb6\u7684\u6559\u80b2\u4f53\u7cfb\u5bf9\u6bd4**\uff1a\u8bb8\u591a\u56fd\u5bb6\u4e5f\u6b63\u5728\u8fdb\u884c\u7c7b\u4f3c\u7684\u6559\u80b2\u6539\u9769\uff0c\u4ee5\u5e94\u5bf9\u5168\u7403\u52b3\u52a8\u529b\u5e02\u573a\u7684\u53d8\u5316\u9700\u6c42\u3002\n\n- **\u5176\u4ed6\u56fd\u5bb6\u7684\u76f8\u5173\u6539\u9769\u7ecf\u9a8c**\uff1a\u4f8b\u5982\uff0c\u5fb7\u56fd\u7684\u53cc\u5143\u5236\u804c\u4e1a\u6559\u80b2\u548c\u82ac\u5170\u7684\u6559\u80b2\u521b\u65b0\u4e3a\u5168\u7403\u6559\u80b2\u6539\u9769\u63d0\u4f9b\u4e86\u91cd\u8981\u53c2\u8003\u3002\n\n**6. \u6311\u6218\u4e0e\u673a\u9047**\n\n- **\u6539\u9769\u8fc7\u7a0b\u4e2d\u53ef\u80fd\u9047\u5230\u7684\u6311\u6218**\uff1a\u653f\u7b56\u6267\u884c\u7684\u7ec6\u8282\u95ee\u9898\uff0c\u5982\u8bfe\u7a0b\u8bbe\u7f6e\u3001\u6559\u5e08\u57f9\u8bad\u3001\u6559\u80b2\u8d44\u6e90\u7684\u91cd\u65b0\u914d\u7f6e\u7b49\u3002\n\n- **\u6539\u9769\u5e26\u6765\u7684\u65b0\u673a\u9047**\uff1a\u901a\u8fc7\u6539\u9769\uff0c\u53ef\u80fd\u4fc3\u8fdb\u6559\u80b2\u8d28\u91cf\u63d0\u5347\uff0c\u589e\u5f3a\u5b66\u751f\u5b9e\u8df5\u80fd\u529b\uff0c\u4e3a\u56fd\u5bb6\u5728\u5168\u7403\u8303\u56f4\u5185\u7684\u7ade\u4e89\u529b\u52a0\u5206\u3002\n\n**7. \u7ed3\u8bba\u4e0e\u5efa\u8bae**\n\n- **\u4e3a\u653f\u7b56\u5236\u5b9a\u8005\u7684\u5efa\u8bae**\uff1a\u5728\u6539\u9769\u8fc7\u7a0b\u4e2d\u9700\u6ce8\u610f\u5e73\u7a33\u8fc7\u6e21\uff0c\u901a\u8fc7\u8bd5\u70b9\u548c\u6559\u5b66\u53cd\u9988\u8c03\u6574\u653f\u7b56\u5b9e\u65bd\u7b56\u7565\u3002\n\n- 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