Respan Dataset Explorer

ОПИСАНИЕ: Необходимо проанализировать JSON-данные с организациями и превести его в формат:
- id;
- name (название организации), взять из title;
- city (город организации, русский язык), взять из city;
- country (страна организации, русский язык), определить по city, если не получается поставить null;
- contacts (контактные лица) - массив объектов, каждый объект содержит поля ниже, сами контактные лица найти в объекте организации (contact_person+phone+email+site+comment), автоматически определить и разбить, если какая-то информация отсутствует, поставить в поле null:
  -- name (имя контактного лица);
  -- emails (почта контактного лица) - массив;
  -- phones (телефон контактного лица) - массив;
- site (сайт организации), взять из site, обработать, если нет, поставить null, сайт может содержать другие данные, кроме ссылки, их переносить не нужно;
- active (активность организации), взять из active преобразовать в boolean;
- comment (комментарий), объединить все комментарии из объекта организации, если нет, поставить null;
- prices (цены) - массив объектов, каждый объект содержит поля ниже, цены найти в объекте организации, автоматически определить и разбить:
  -- name (аименование услуги, нижний регистр, русский язык) - необходимо определить, что является названием услуги, если это не удаётся точно определить между ценой и наименованием стоит точка, восклицательный знак или это как-то иначе отделено, то это не название услуги, нужно указать null;
  -- price (цена услуги) - произвольный формат (строка), пример "от 1000 руб.", "по договорённости", "от 500 до 1000 рублей", итд;
- services (услуги, нижний регистр, русский язык, единственное число) - массив содержащий формат ключ-значение, ключ - название услуги, значение - boolean, важные правила:
  -- по умолчанию услуга "монтаж бризера" должна быть true, если в объекте не указано обратное;
  -- по умолчанию услуга "монтаж кондиционера" должна быть false, если в объекте не указано обратное;
  -- другие услуги определить из текста;
- extra_params (дополнительные параметры, нижний регистр, русский язык) - массив строк содержащих важные данные об организации, теги, ключевые и значимые параметры, эти данные нужно определить по другим полям, как пример: "дилер", "неделя ожидания", "неактивен", "оплата только налом", "без доставки", итд;
- related_organizations (связанные организации) - массив строк, содержащих названия организаций, которые связаны с текущей организацией, определить по тексту;

- work_cities (города работы) - массив строк, содержащих города, в которых работает организация, определить по тексту, основной город не должен фигурировать в списке;
- problematic (проблемные организации) - boolean, определить по тексту, если не получается, поставить false;
; | ДАННЫЕ (JSON): [{"id":53,"created_at":1536908038,"updated_at":1613364987,"title":"ИП Кондратенко ","city":"Москва","phone":"+79250226564","contact_person":"Сергей Горячев","email":"for.indent@gmail.com","site":"","comment":"Максимальная глубина 75 см, может взять удлинитель у Иванченко.","active":0},{"id":54,"created_at":1536908089,"updated_at":1703657836,"title":"Dr-Air (МСК Вент, ИП Семенчук Владислав Алексеевич)","city":"Москва","phone":"+79773077061","contact_person":"Владислав Семенчук","email":"nephewcriz@bk.ru","site":"http://Dr-Air.ru","comment":"Готов приступить к работе с 08.01.2023 Выполняет ручное бурение даймонд хит, без воды, монтаж кондиционеров / Макс глубина 120см. Есть Альпинист 9000 руб.","active":1},{"id":55,"created_at":1536908162,"updated_at":1599911289,"title":"ТИОН","city":"Москва","phone":"+7 925 275 0471, +7 909 666 6586, +7 904 597 8504, +7 929 982 4130","contact_person":"Павел Валерьевич Дмитриев, Алексей Валерьевич Индюков","email":"","site":"","comment":"Автомобиль Паши Ford C-MAX, г.н. С730ВВ 750. ","active":1},{"id":57,"created_at":1536908401,"updated_at":1673335967,"title":"ИП Крижановский","city":"Мурманск","phone":"+79062887345","contact_person":"Владимир Крижановский","email":"v76@list.ru","site":"","comment":"Северный прайс! 9200 / Ожидаем подписания соглашения / Купил энергофлекс, работает по упрощенке, есть возможность выставления счетов на монтаж без НДС. / Не работает до конца 22 года, утончить активность после НГ праздников. бурилка течет, будет ремонт","active":0},{"id":58,"created_at":1536908458,"updated_at":1615461605,"title":"Климатические решения","city":"Набережные Челны","phone":"+79375832888, +79179116000","contact_person":"Айрат Задитович Гилязов","email":"krfiz4682@mail.ru","site":"","comment":"Не готов работать на наших условиях, подвел нас с последним заказом, ведет себя очень некрасиво, вносим в черный список  // До конца 2020 года были не активны. Снова активны","active":0}]; | ФОРМАТ: вернуть только json массив, без форматирования обной строкой

```json
[{"id":53,"name":"ИП Кондратенко","city":"Москва","country":"Россия","contacts":[{"name":"Сергей Горячев","emails":["for.indent@gmail.com"],"phones":["+79250226564"]}],"site":null,"active":false,"comment":"Максимальная глубина 75 см, может взять удлинитель у Иванченко.","prices":[],"services":{"монтаж бризера":true,"монтаж кондиционера":false},"extra_params":["неактивен"],"related_organizations":[],"work_cities":[],"problematic":false},{"id":54,"name":"Dr-Air","city":"Москва","country":"Россия","contacts":[{"name":"Владислав Семенчук","emails":["nephewcriz@bk.ru"],"phones":["+79773077061"]}],"site":"http://Dr-Air.ru","active":true,"comment":"Готов приступить к работе с 08.01.2023 Выполняет ручное бурение даймонд хит, без воды, монтаж кондиционеров / Макс глубина 120см. Есть Альпинист 9000 руб.","prices":[{"name":null,"price":"9000 руб."}],"services":{"монтаж бризера":true,"монтаж кондиционера":true},"extra_params":["без воды"],"related_organizations":["МСК Вент"],"work_cities":[],"problematic":false},{"id":55,"name":"ТИОН","city":"Москва","country":"Россия","contacts":[{"name":"Павел Валерьевич Дмитриев","emails":[],"phones":["+7 925 275 0471"]},{"name":"Алексей Валерьевич Индюков","emails":[],"phones":["+7 909 666 6586"]}],"site":null,"active":true,"comment":"Автомобиль Паши Ford C-MAX, г.н. С730ВВ 750.","prices":[],"services":{"монтаж бризера":true,"монтаж кондиционера":false},"extra_params":[],"related_organizations":[],"work_cities":[],"problematic":false},{"id":57,"name":"ИП Крижановский","city":"Мурманск","country":"Россия","contacts":[{"name":"Владимир Крижановский","emails":["v76@list.ru"],"phones":["+79062887345"]}],"site":null,"active":false,"comment":"Северный прайс! 9200 / Ожидаем подписания соглашения / Купил энергофлекс, работает по упрощенке, есть возможность выставления счетов на монтаж без НДС. / Не работает до конца 22 года, утончить активность после НГ праздников. бурилка течет, будет ремонт","prices":[{"name":null,"price":"9200"}],"services":{"монтаж бризера":true,"монтаж кондиционера":false},"extra_params":["неактивен","без НДС"],"related_organizations":[],"work_cities":[],"problematic":true},{"id":58,"name":"Климатические решения","city":"Набережные Челны","country":"Россия","contacts":[{"name":"Айрат Задитович Гилязов","emails":["krfiz4682@mail.ru"],"phones":["+79375832888"," +79179116000"]}],"site":null,"active":false,"comment":"Не готов работать на наших условиях, подвел нас с последним заказом, ведет себя очень некрасиво, вносим в черный список  // До конца 2020 года были не активны. Снова активны","prices":[],"services":{"монтаж бризера":true,"монтаж кондиционера":false},"extra_params":["неактивен","в черном списке"],"related_organizations":[],"work_cities":[],"problematic":true}]
```

I wanna use it into google colab

https://huggingface.co/spaces/Shitao/OmniGen

With gradio and without ngrok

Certainly! You can run the [OmniGen](https://huggingface.co/spaces/Shitao/OmniGen) Hugging Face Space directly within Google Colab using Gradio without the need for **ngrok**. Below is a step-by-step guide to achieve this:

---

## **Step 1: Set Up Your Google Colab Environment**

1. **Open Google Colab:**
   - Navigate to [Google Colab](https://colab.research.google.com/) and create a new notebook.

2. **Enable GPU (Optional but Recommended):**
   - Go to `Runtime` > `Change runtime type`.
   - Set `Hardware accelerator` to `GPU`.
   - Click `Save`.

---

## **Step 2: Install Necessary Libraries**

You'll need to install `gradio`, `transformers`, and any other dependencies required by OmniGen. Execute the following cell:

```python
!pip install gradio transformers
```

*If OmniGen has additional dependencies, adjust the installation command accordingly (e.g., `!pip install <package-name>`). You can typically find these in the [Space's repository](https://huggingface.co/spaces/Shitao/OmniGen) or accompanying documentation.*

---

## **Step 3: Clone the OmniGen Repository**

Assuming the OmniGen Space’s code is publicly available, you can clone it directly from Hugging Face Spaces. If not, you'll need to manually set up the model and interface based on available details.

```python
!pip install huggingface_hub
from huggingface_hub import hf_hub_download

# Example: Replace 'path/to/omnigen_repo' with the actual repository path if available
# If the code isn't publicly accessible, skip to Step 4
# clone_repo("Shitao/OmniGen", repo_dir="omnigen")
```

*If the repository isn't publicly accessible, proceed to the next step to set up manually.*

---

## **Step 4: Load the OmniGen Model**

Load the specific model that OmniGen uses. You may need to refer to the [OmniGen Space](https://huggingface.co/spaces/Shitao/OmniGen) or its documentation to identify the exact model name. For demonstration, let's assume it uses a GPT-based model.

```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

# Replace 'model-name' with the actual model used by OmniGen
model_name = "gpt2"  # Example model

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# If using GPU
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)
```

*Ensure you replace `"gpt2"` with the correct model name used by OmniGen.*

---

## **Step 5: Define the Text Generation Function**

Create a function that takes user input and generates text using the loaded model.

```python
def generate_text(prompt, max_length=100, temperature=0.7):
    inputs = tokenizer.encode(prompt, return_tensors="pt").to(device)
    outputs = model.generate(
        inputs,
        max_length=max_length,
        temperature=temperature,
        do_sample=True,
        top_p=0.95,
        top_k=60,
        no_repeat_ngram_size=2
    )
    generated = tokenizer.decode(outputs[0], skip_special_tokens=True)
    return generated
```

*Adjust `max_length`, `temperature`, and other generation parameters as needed to match OmniGen's behavior.*

---

## **Step 6: Create and Launch the Gradio Interface**

Set up the Gradio interface to interact with the `generate_text` function.

```python
import gradio as gr

# Define Gradio interface
iface = gr.Interface(
    fn=generate_text,
    inputs=[
        gr.inputs.Textbox(lines=5, label="Input Prompt"),
        gr.inputs.Slider(minimum=10, maximum=500, default=100, label="Max Length"),
        gr.inputs.Slider(minimum=0.1, maximum=1.0, default=0.7, label="Temperature")
    ],
    outputs=gr.outputs.Textbox(label="Generated Text"),
    title="OmniGen Clone",
    description="Generate text based on your input prompt using the OmniGen model."
)

# Launch the interface
iface.launch(share=False)
```

*The `share=False` parameter ensures that the Gradio interface is displayed directly within the Colab notebook without creating an external link via ngrok.*

---

## **Step 7: Interact with the Interface**

Once you run the above cell, a Gradio interface will appear below it in your Colab notebook. You can input prompts, adjust parameters like `Max Length` and `Temperature`, and generate text outputs interactively.

---

## **Complete Example Code**

Here's the entire process consolidated into a single Colab notebook sequence:

```python
# Install necessary libraries
!pip install gradio transformers huggingface_hub

# Import libraries
import gradio as gr
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

# Load the model (replace 'gpt2' with the actual model used by OmniGen)
model_name = "gpt2"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Move model to GPU if available
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)

# Define the text generation function
def generate_text(prompt, max_length=100, temperature=0.7):
    inputs = tokenizer.encode(prompt, return_tensors="pt").to(device)
    outputs = model.generate(
        inputs,
        max_length=max_length,
        temperature=temperature,
        do_sample=True,
        top_p=0.95,
        top_k=60,
        no_repeat_ngram_size=2
    )
    generated = tokenizer.decode(outputs[0], skip_special_tokens=True)
    return generated

# Create Gradio interface
iface = gr.Interface(
    fn=generate_text,
    inputs=[
        gr.inputs.Textbox(lines=5, label="Input Prompt"),
        gr.inputs.Slider(minimum=10, maximum=500, default=100, label="Max Length"),
        gr.inputs.Slider(minimum=0.1, maximum=1.0, default=0.7, label="Temperature")
    ],
    outputs=gr.outputs.Textbox(label="Generated Text"),
    title="OmniGen Clone",
    description="Generate text based on your input prompt using the OmniGen model."
)

# Launch the interface
iface.launch(share=False)
```

---

## **Additional Tips**

- **Determine the Exact Model Used by OmniGen:**  
  To replicate OmniGen accurately, identify the specific model it uses. Check the [OmniGen Space](https://huggingface.co/spaces/Shitao/OmniGen) for details or documentation that specifies the model name.

- **Handle Larger Models Carefully:**  
  If OmniGen uses a large model (e.g., GPT-3 variants), be aware that:
  - Google Colab has resource limitations.
  - You might need to use model parallelism or optimization techniques.
  - Some large models may not be feasible to run on Colab's hardware.

- **Optimize for Speed and Efficiency:**  
  Depending on your use case, you might want to optimize the generation parameters or use techniques like prompt engineering to improve response times.

- **Persisting Sessions:**  
  Colab sessions can disconnect after periods of inactivity. To maintain longer-running sessions, periodically interact with the notebook or consider using other platforms if necessary.

---

## **Conclusion**

By following the above steps, you can effectively run the OmniGen Hugging Face Space within Google Colab using Gradio without relying on ngrok. This setup allows for a seamless and interactive experience directly within your browser. Adjust the model and parameters as needed to best fit the specific functionalities and behaviors of the original OmniGen Space.

If you encounter any issues or need more specific configurations, feel free to provide additional details, and I'll be happy to assist further!

CAPS ON LETTERS BIGGER

NO, YOU CAN INCLUDE IN DIV CONTAINER WITH STYLES IN A SINGLE STRING OF CODE WITHOUT NEWLINES OR INCLUDING THIS CODE IN CHAT IN CODE BLOCK TAG

If you want to ensure that text within a `div` container appears in all uppercase letters and make the text size larger, you can achieve this by using inline CSS styles. Here is an example of how you can do this in a single string of code without newlines:

`<div style="text-transform: uppercase; font-size: 24px;">YOUR TEXT HERE</div>` 

You can adjust the `font-size` value to your preference. This code will display all the text inside the `div` in uppercase and at the specified font size.

index.html:
<!DOCTYPE html>
<html lang="ar">
<head>
    <meta charset="UTF-8">
    <meta name="viewport" content="width=device-width, initial-scale=1.0">
    <title>محادثة الذكاء الاصطناعي</title>
    <link href="https://cdn.jsdelivr.net/npm/tailwindcss@2.2.19/dist/tailwind.min.css" rel="stylesheet">
    <style>
        @import url('https://fonts.googleapis.com/css2?family=Amiri:wght@400;700&display=swap');
        body { font-family: 'Amiri', serif; background-color: #1a1a1a; }
        /* أنماط المرئي الدائري */
        #visualizerContainer {
            width: 100px;
            height: 100px;
            margin: 0 auto;
            position: relative;
        }
        #visualizer {
            width: 100%;
            height: 100%;
            background: transparent;
        }
        .circle {
            width: 100%;
            height: 100%;
            border: 4px solid #38bdf8;
            border-radius: 50%;
            animation: pulse 1.5s infinite;
            position: absolute;
            top: 0;
            left: 0;
            box-shadow: 0 0 10px #38bdf8;
        }
        @keyframes pulse {
            0% {
                transform: scale(0.75);
                opacity: 1;
            }
            100% {
                transform: scale(1.25);
                opacity: 0;
            }
        }
        .hidden {
            display: none;
        }
        /* أنماط الرسائل */
        .user-message {
            background-color: #2563eb;
            color: white;
            padding: 10px 15px;
            border-radius: 20px 20px 0px 20px;
            display: inline-block;
            max-width: 80%;
            margin-bottom: 10px;
        }
        .ai-message {
            background-color: #374151;
            color: white;
            padding: 10px 15px;
            border-radius: 20px 20px 20px 0px;
            display: inline-block;
            max-width: 80%;
            margin-bottom: 10px;
        }
    </style>
</head>
<body class="text-white flex flex-col items-center justify-center min-h-screen">
    <h1 class="text-4xl mb-8">محادثة الذكاء الاصطناعي</h1>

    <!-- مرئي التحدث -->
    <div id="visualizerContainer" class="mb-8">
        <canvas id="visualizer"></canvas>
        <div id="circleVisualizer" class="circle hidden"></div>
    </div>

    <!-- التحكم في المحادثة -->
    <div class="w-2/3 flex flex-col items-center">
        <button id="talkButton" onclick="toggleChat()" 
                class="bg-green-500 px-6 py-3 mt-6 rounded hover:bg-green-700 w-full">
            🎤 ابدأ المحادثة
        </button>
        <button id="stopButton" onclick="stopRecognition()" 
                class="bg-red-500 px-6 py-3 mt-4 rounded hover:bg-red-700 w-full hidden">
            ⛔ إيقاف المحادثة
        </button>
    </div>

    <!-- سجل المحادثة -->
    <div id="historyContainer" class="mt-8 p-4 bg-gray-800 rounded w-2/3 overflow-auto max-h-96">
        <!-- ستتم إضافة الرسائل هنا -->
    </div>

    <script src="/static/script.js" defer></script>
</body>
</html>


script.js:
const historyContainer = document.getElementById('historyContainer');
const talkButton = document.getElementById('talkButton');
const stopButton = document.getElementById('stopButton');
const circleVisualizer = document.getElementById('circleVisualizer');

let recognition;
let isListening = false;

// سجل المحادثة
let conversationHistory = [];

// تحميل سجل المحادثة من Local Storage
function loadConversationHistory() {
    const history = localStorage.getItem('conversationHistory');
    if (history) {
        conversationHistory = JSON.parse(history);
        displayConversationHistory();
    }
}

// حفظ سجل المحادثة إلى Local Storage
function saveConversationHistory() {
    localStorage.setItem('conversationHistory', JSON.stringify(conversationHistory));
}

// عرض سجل المحادثة
function displayConversationHistory() {
    historyContainer.innerHTML = '';
    conversationHistory.forEach(item => {
        const messageDiv = document.createElement('div');
        messageDiv.className = 'mb-4';

        // رسالة المستخدم
        const userMessageDiv = document.createElement('div');
        userMessageDiv.className = 'text-right';
        const userMessageSpan = document.createElement('span');
        userMessageSpan.className = 'user-message';
        userMessageSpan.textContent = item.user;
        userMessageDiv.appendChild(userMessageSpan);

        // رسالة الذكاء الاصطناعي
        const aiMessageDiv = document.createElement('div');
        aiMessageDiv.className = 'text-left';
        const aiMessageSpan = document.createElement('span');
        aiMessageSpan.className = 'ai-message';
        aiMessageSpan.textContent = item.ai;
        aiMessageDiv.appendChild(aiMessageSpan);

        messageDiv.appendChild(userMessageDiv);
        messageDiv.appendChild(aiMessageDiv);
        historyContainer.appendChild(messageDiv);
    });
    // التمرير إلى الأسفل
    historyContainer.scrollTop = historyContainer.scrollHeight;
}

// إعداد Web Speech API
function initRecognition() {
    recognition = new (window.SpeechRecognition || window.webkitSpeechRecognition)();
    recognition.lang = 'ar-SA';
    recognition.interimResults = false;
    recognition.maxAlternatives = 1;

    recognition.onstart = function() {
        circleVisualizer.classList.remove('hidden');
    };

    recognition.onend = function() {
        circleVisualizer.classList.add('hidden');
    };

    recognition.onresult = async function(event) {
        const transcript = event.results[0][0].transcript;
        addMessageToHistory('user', transcript);
        await sendMessage(transcript);

        if (isListening) {
            recognition.start();
        }
    };

    recognition.onerror = function(event) {
        console.error('خطأ في التعرف على الكلام:', event.error);
        if (isListening) {
            recognition.start();
        }
    };
}

// بدء المحادثة
async function toggleChat() {
    isListening = true;
    talkButton.classList.add('hidden');
    stopButton.classList.remove('hidden');

    if (!recognition) initRecognition();
    recognition.start();
}

// إيقاف المحادثة
function stopRecognition() {
    isListening = false;
    recognition.stop();
    talkButton.classList.remove('hidden');
    stopButton.classList.add('hidden');
}

// إرسال الرسالة إلى الخادم والحصول على الرد
async function sendMessage(message) {
    try {
        const response = await fetch('/chat', {
            method: 'POST',
            headers: { 'Content-Type': 'application/json' },
            body: JSON.stringify({ message: message })
        });

        const result = await response.json();
        addMessageToHistory('ai', result.response);

        // تشغيل الرد باستخدام تحويل النص إلى كلام
        await playAudio(result.response);
    } catch (error) {
        console.error('خطأ في إرسال الرسالة:', error);
    }
}

// إضافة الرسائل إلى السجل
function addMessageToHistory(sender, message) {
    if (sender === 'user') {
        conversationHistory.push({ user: message, ai: '' });
    } else if (sender === 'ai') {
        conversationHistory[conversationHistory.length - 1].ai = message;
    }
    displayConversationHistory();
    saveConversationHistory();
}

// تحويل النص إلى كلام وتشغيله
async function playAudio(text) {
    try {
        const response = await fetch('/text-to-speech', {
            method: 'POST',
            headers: { 'Content-Type': 'application/json' },
            body: JSON.stringify({ text: text })
        });

        const audioBlob = await response.blob();
        const audioUrl = URL.createObjectURL(audioBlob);
        const audio = new Audio(audioUrl);

        audio.onplay = function() {
            circleVisualizer.classList.remove('hidden');
        };

        audio.onended = function() {
            circleVisualizer.classList.add('hidden');
            if (isListening) {
                recognition.start();
            }
        };

        await audio.play();
    } catch (error) {
        console.error('خطأ في تشغيل الصوت:', error);
    }
}

// تحميل سجل المحادثة عند تحميل الصفحة
loadConversationHistory();


improve this code make it more modern and make visualizer visualize with audio more modern

Certainly! Here's the improved version of your code. 

**Changes Made:**

- **Modern JavaScript Syntax**: Updated the JavaScript code to use modern ES6+ features like `const`, `let`, arrow functions, and template literals.
- **Improved Visualizer**: Replaced the simple circle animation with a real-time audio visualizer using the Web Audio API and Canvas. It now displays frequency bars that react to the audio.
- **Cleaned Up HTML and CSS**: Simplified and modernized the HTML structure and CSS styles.
- **Responsive Canvas**: Made the visualizer canvas responsive to window resizing.

---

**index.html**

```html
<!DOCTYPE html>
<html lang="ar">
<head>
    <meta charset="UTF-8">
    <meta name="viewport" content="width=device-width, initial-scale=1.0">
    <title>محادثة الذكاء الاصطناعي</title>
    <!-- Updated Tailwind CSS to the latest version -->
    <script src="https://cdn.tailwindcss.com"></script>
    <style>
        @import url('https://fonts.googleapis.com/css2?family=Amiri:wght@400;700&display=swap');

        body { 
            font-family: 'Amiri', serif; 
            background-color: #1a1a1a; 
            color: white;
        }
        
        /* Styles for visualizer */
        #visualizerContainer {
            width: 100%;
            height: 150px;
            margin: 0 auto;
            position: relative;
        }
        #visualizer {
            width: 100%;
            height: 100%;
            background: transparent;
        }
        
        /* Message styles */
        .user-message {
            background-color: #2563eb;
            color: white;
            padding: 10px 15px;
            border-radius: 20px 20px 0px 20px;
            display: inline-block;
            max-width: 80%;
            margin-bottom: 10px;
        }
        .ai-message {
            background-color: #374151;
            color: white;
            padding: 10px 15px;
            border-radius: 20px 20px 20px 0px;
            display: inline-block;
            max-width: 80%;
            margin-bottom: 10px;
        }
    </style>
</head>
<body class="flex flex-col items-center justify-center min-h-screen">
    <h1 class="text-4xl mb-8">محادثة الذكاء الاصطناعي</h1>

    <!-- Audio Visualizer -->
    <div id="visualizerContainer" class="mb-8">
        <canvas id="visualizer"></canvas>
    </div>

    <!-- Chat controls -->
    <div class="w-2/3 flex flex-col items-center">
        <button id="talkButton" onclick="toggleChat()" 
                class="bg-green-500 px-6 py-3 mt-6 rounded hover:bg-green-700 w-full">
            🎤 ابدأ المحادثة
        </button>
        <button id="stopButton" onclick="stopRecognition()" 
                class="bg-red-500 px-6 py-3 mt-4 rounded hover:bg-red-700 w-full hidden">
            ⛔ إيقاف المحادثة
        </button>
    </div>

    <!-- Conversation history -->
    <div id="historyContainer" class="mt-8 p-4 bg-gray-800 rounded w-2/3 overflow-auto max-h-96">
        <!-- Messages will be added here -->
    </div>

    <script src="/static/script.js" defer></script>
</body>
</html>
```

---

**script.js**

```javascript
const historyContainer = document.getElementById('historyContainer');
const talkButton = document.getElementById('talkButton');
const stopButton = document.getElementById('stopButton');
const visualizerCanvas = document.getElementById('visualizer');
const canvasContext = visualizerCanvas.getContext('2d');
const visualizerContainer = document.getElementById('visualizerContainer');

let recognition;
let isListening = false;

// Conversation history
let conversationHistory = [];

// Audio context for visualization
let audioContext;
let sourceNode;
let analyserNode;
let dataArray;

// Load conversation history from Local Storage
function loadConversationHistory() {
    const history = localStorage.getItem('conversationHistory');
    if (history) {
        conversationHistory = JSON.parse(history);
        displayConversationHistory();
    }
}

// Save conversation history to Local Storage
function saveConversationHistory() {
    localStorage.setItem('conversationHistory', JSON.stringify(conversationHistory));
}

// Display conversation history
function displayConversationHistory() {
    historyContainer.innerHTML = '';
    conversationHistory.forEach(item => {
        const messageDiv = document.createElement('div');
        messageDiv.className = 'mb-4';

        // User message
        const userMessageDiv = document.createElement('div');
        userMessageDiv.className = 'text-right';
        const userMessageSpan = document.createElement('span');
        userMessageSpan.className = 'user-message';
        userMessageSpan.textContent = item.user;
        userMessageDiv.appendChild(userMessageSpan);

        // AI message
        const aiMessageDiv = document.createElement('div');
        aiMessageDiv.className = 'text-left';
        const aiMessageSpan = document.createElement('span');
        aiMessageSpan.className = 'ai-message';
        aiMessageSpan.textContent = item.ai;
        aiMessageDiv.appendChild(aiMessageSpan);

        messageDiv.appendChild(userMessageDiv);
        messageDiv.appendChild(aiMessageDiv);
        historyContainer.appendChild(messageDiv);
    });
    // Scroll to the bottom
    historyContainer.scrollTop = historyContainer.scrollHeight;
}

// Initialize speech recognition
function initRecognition() {
    recognition = new (window.SpeechRecognition || window.webkitSpeechRecognition)();
    recognition.lang = 'ar-SA';
    recognition.interimResults = false;
    recognition.maxAlternatives = 1;

    recognition.onstart = () => {
        // Start some animation or visual indicator if needed
    };

    recognition.onend = () => {
        // Stop animation or visual indicator if needed
        if (isListening) {
            recognition.start(); // Continue listening
        }
    };

    recognition.onresult = async (event) => {
        const transcript = event.results[0][0].transcript;
        addMessageToHistory('user', transcript);
        await sendMessage(transcript);

        if (isListening) {
            recognition.start();
        }
    };

    recognition.onerror = (event) => {
        console.error('Error in speech recognition:', event.error);
        if (isListening) {
            recognition.start();
        }
    };
}

// Start the conversation
async function toggleChat() {
    isListening = true;
    talkButton.classList.add('hidden');
    stopButton.classList.remove('hidden');

    if (!recognition) initRecognition();
    recognition.start();
}

// Stop the conversation
function stopRecognition() {
    isListening = false;
    recognition.stop();
    talkButton.classList.remove('hidden');
    stopButton.classList.add('hidden');
}

// Send message to server and get response
async function sendMessage(message) {
    try {
        const response = await fetch('/chat', {
            method: 'POST',
            headers: { 'Content-Type': 'application/json' },
            body: JSON.stringify({ message })
        });

        const result = await response.json();
        addMessageToHistory('ai', result.response);

        // Play the response using text-to-speech
        await playAudio(result.response);
    } catch (error) {
        console.error('Error sending message:', error);
    }
}

// Add messages to history
function addMessageToHistory(sender, message) {
    if (sender === 'user') {
        conversationHistory.push({ user: message, ai: '' });
    } else if (sender === 'ai') {
        if (conversationHistory.length > 0) {
            conversationHistory[conversationHistory.length - 1].ai = message;
        } else {
            // If for some reason there is no user message, add a placeholder
            conversationHistory.push({ user: '', ai: message });
        }
    }
    displayConversationHistory();
    saveConversationHistory();
}

// Play audio and visualize it
async function playAudio(text) {
    try {
        const response = await fetch('/text-to-speech', {
            method: 'POST',
            headers: { 'Content-Type': 'application/json' },
            body: JSON.stringify({ text })
        });

        const arrayBuffer = await response.arrayBuffer();

        if (!audioContext) {
            audioContext = new (window.AudioContext || window.webkitAudioContext)();
        }

        const audioBuffer = await audioContext.decodeAudioData(arrayBuffer);

        // Create source node from the audio buffer
        sourceNode = audioContext.createBufferSource();
        sourceNode.buffer = audioBuffer;

        // Create an analyser node
        analyserNode = audioContext.createAnalyser();
        analyserNode.fftSize = 2048;
        const bufferLength = analyserNode.frequencyBinCount;
        dataArray = new Uint8Array(bufferLength);

        // Connect nodes
        sourceNode.connect(analyserNode);
        analyserNode.connect(audioContext.destination);

        // Start visualizing
        visualize();

        // Play the audio
        sourceNode.start(0);

        // When the audio ends
        sourceNode.onended = () => {
            // Stop visualizing
            cancelAnimationFrame(visualizerId);
            clearCanvas();

            if (isListening) {
                recognition.start();
            }
        };
    } catch (error) {
        console.error('Error playing audio:', error);
    }
}

let visualizerId;

function visualize() {
    const canvasWidth = visualizerCanvas.width;
    const canvasHeight = visualizerCanvas.height;
    const bufferLength = analyserNode.frequencyBinCount;

    const renderFrame = () => {
        visualizerId = requestAnimationFrame(renderFrame);

        analyserNode.getByteFrequencyData(dataArray);

        canvasContext.fillStyle = '#1a1a1a';
        canvasContext.fillRect(0, 0, canvasWidth, canvasHeight);

        const barWidth = (canvasWidth / bufferLength) * 2.5;
        let barHeight;
        let x = 0;

        for(let i = 0; i < bufferLength; i++) {
            barHeight = dataArray[i];

            canvasContext.fillStyle = `rgb(${barHeight+100}, 50, 50)`;
            canvasContext.fillRect(x, canvasHeight - barHeight / 2, barWidth, barHeight / 2);

            x += barWidth + 1;
        }
    }

    renderFrame();
}

// Clear the canvas after audio ends
function clearCanvas() {
    canvasContext.clearRect(0, 0, visualizerCanvas.width, visualizerCanvas.height);
}

// Resize the canvas when window resizes
function resizeCanvas() {
    visualizerCanvas.width = visualizerContainer.offsetWidth;
    visualizerCanvas.height = visualizerContainer.offsetHeight;
    clearCanvas();
}

window.addEventListener('resize', resizeCanvas);
resizeCanvas();

// Load conversation history when page loads
loadConversationHistory();
```

---

**Explanation of Changes:**

- **Modern JavaScript Syntax**: Used `const` and `let` instead of `var`, and arrow functions where appropriate.
- **Real-Time Audio Visualization**: Implemented a frequency bar visualizer using the Web Audio API and Canvas.
  - **Web Audio API**: Used `AudioContext`, `AnalyserNode`, and `Uint8Array` to analyze and visualize audio data.
  - **Canvas Visualization**: Updated the `visualize` function to draw frequency bars that react to the audio.
- **Improved Error Handling**: Added error handling for audio decoding and fetching.
- **Responsive Design**: Made the visualizer canvas responsive to window resizing.

---

Now, when the AI responds, the audio will be played and a modern visualizer will display frequency bars that react in real-time to the audio, providing a more engaging and modern user experience.

**Note**: Ensure that your server endpoints `/chat` and `/text-to-speech` are properly set up to handle the requests.

provide a code to implement simple data-type decleration construct using ply tool

To implement a simple data-type declaration construct using the PLY (Python Lex-Yacc) tool, you'll need to define both a lexer and a parser. PLY is a Python implementation of the commonly used Lex and Yacc tools, which are used to build lexical analyzers and parsers. Below is a basic example that demonstrates how to declare simple data types like integers and floats using PLY.

First, ensure you have PLY installed. If you don't have it, you can install it using pip:

```bash
pip install ply
```

Then, you can create a lexer and parser for a simple data type declaration construct as follows:

### Lexer

First, create a file named `simple_lexer.py`:

```python
import ply.lex as lex

# List of token names
tokens = (
    'TYPE',
    'IDENTIFIER',
    'SEMICOLON'
)

# Regular expressions for simple tokens
t_TYPE = r'(int|float)'
t_IDENTIFIER = r'[a-zA-Z_][a-zA-Z0-9_]*'
t_SEMICOLON = r';'

# Ignored characters (spaces and tabs)
t_ignore = ' \t'

# Error handling rule
def t_error(t):
    print(f"Illegal character '{t.value[0]}'")
    t.lexer.skip(1)

# Build the lexer
lexer = lex.lex()
```

### Parser

Next, create a file named `simple_parser.py`:

```python
import ply.yacc as yacc
from simple_lexer import tokens

# Grammar rules
def p_declaration(p):
    'declaration : TYPE IDENTIFIER SEMICOLON'
    p[0] = (p[1], p[2])
    print(f"Declared {p[1]} named '{p[2]}'")

def p_error(p):
    if p:
        print(f"Syntax error at '{p.value}'")
    else:
        print("Syntax error at EOF")

# Build the parser
parser = yacc.yacc()

def parse(data):
    return parser.parse(data)
```

### Testing the Parser

Now you can create a script to test your simple parser. Create a file named `test_parser.py`:

```python
from simple_parser import parse

data = """
int myVar;
float anotherVar;
"""

result = parse(data)

# If you want to see the result structure
print(result)
```

### Explanation

- **Lexer**: The lexer tokenizes input text into tokens like `TYPE`, `IDENTIFIER`, and `SEMICOLON`. These tokens correspond to data types, variable names, and semicolons in the input string.
- **Parser**: The parser uses the tokens to match patterns defined in the grammar rules. Here, a valid declaration consists of a `TYPE`, an `IDENTIFIER`, and a `SEMICOLON`.
- **Parse Function**: The `parse` function takes input data and parses it, processing each declaration.

This code is a minimal example and recognizes only very basic constructs. You can extend it to support more complex syntax and error checking as needed.

System: You are an expert Named Entity Recognition (NER) system. Label all identifiable entities, abstract concepts, and meaningful ideas in the provided input text, emphasizing relevance to the financial domain.

Ensure the following:
Label All Meaningful Entities: Identify every meaningful entity related to financial analysis, economic dynamics, or market contexts.
Define New Concepts as Needed: Introduce and define entity types for abstract financial concepts or industry-specific terms not typically found in standard NER tasks.
Provide an Exhaustive Entity List: Include every relevant label mentioned in the input text.

Answer in the following format:
<entity from the text> | <entity concept> | <description of entity group/concept>,
<entity from the text> | <entity concept> | <description of entity group/concept>,
...

Here is an Example : 
Input: 
Lawmakers continue to try to police social media use among teens — but Meta, parent company to Facebook, Instagram, and Threads, is pushing another group of companies to do the security work. Meta is expected to announce a proposal on Nov. 15 that will push for tech giants like Google and Apple to carry a bigger burden in keeping teenagers off of potentially harmful platforms. Meta's vision is that these companies, which manage app stores such as the Apple App Store and Google Play Store, require parental approval for teenagers aged 13 to 15 to download applications, according to a report by The Washington Post.

Output:
Lawmakers | Regulatory agents | Individuals or groups responsible for creating and enacting laws, often influencing economic and regulatory environments.  
social media | Digital Channel | Online media channels for content sharing and user interaction, particularly influential in advertising and consumer engagement.
Meta | Company | Parent company of Facebook, Instagram, and Threads, involved in social media and technology sectors.  
Facebook | Company | Social media platform owned by Meta, significant player in digital advertising and social media markets.  
Instagram | Company | Photo and video sharing social media platform owned by Meta, influential in marketing and consumer engagement.  
Threads | Company | Social media platform owned by Meta, contributing to the digital communication landscape.  
Nov. 15 | Date | Specific date relevant for financial or regulatory announcements, potentially impacting market perceptions. 
tech giants | Major Companies | Entities that hold substantial market power in the technology sector. 
Google | Company | Technology company known for its search engine and digital services, significant in advertising and app distribution.  
Apple | Company | Technology company known for its hardware and software products, influential in consumer technology and app distribution.  
bigger burden | Operational Challenge | heightened difficulties or obstacles impacting a company’s operations, often resulting in resource strain or inefficiencies.
Apple App Store | Platform | Digital distribution platform for applications on Apple devices, relevant for app market dynamics.  
Google Play Store | Platform | Digital distribution platform for applications on Android devices, important for app market dynamics.  
Parental approval | Concept | Regulatory measure proposed to manage access to applications by minors, impacting technology and social media usage.  
The Washington Post | Newspaper | News outlet providing reports and analysis, influential in shaping public opinion and regulatory discourse.
User: Sign up today for the new CNBC Sport Newsletter Olympic gold medalist Simone Biles, considered one of the world's greatest gymnasts, broke down in tears Wednesday as she shared her story as a survivor of sexual abuse from convicted USA Gymnastics doctor Larry Nassar. Biles, who has won 25 world championship medals and seven Olympic medals for Team USA, said in her opening statement that she believes the abuse happened because organizations created by Congress to protect her as an athlete — USA Gymnastics and the United States Olympic and Paralympic Committee — "failed to do their jobs." "I don't want another young gymnast, or Olympic athlete, or any individual to experience the horror that I and hundreds of others have endured before, during and continuing to this
Assistant:

CNBC | Media Company | A major financial news network providing business and market news coverage.
Sport Newsletter | Publication | A specific publication by CNBC focusing on sports-related topics and updates.
Olympic gold medalist | Title | Recognition of achievement in the Olympic Games, significant in sports and sponsorship markets.
Simone Biles | Athlete | Acclaimed gymnast known for her achievements and influence in sports.
USA Gymnastics | Organization | Governing body for gymnastics in the USA, involved in athlete management and protection.
Larry Nassar | Individual | Former USA Gymnastics doctor convicted of sexual abuse, impacting organizational trust and regulatory scrutiny.
Team USA | National Team | The United States national team for Olympic events, representative of national sports interests.
Congress | Government Body | Legislative body responsible for creating laws, influencing regulatory frameworks.
United States Olympic and Paralympic Committee | Organization | US organization tasked with overseeing Olympic and Paralympic sports, entrusted with athlete welfare.
Olympic athlete | Title | Individual competing at the Olympics, influential in sports markets and branding.
sexual abuse | Crime | Illegitimate activities impacting personal and organizational reputations and legal frameworks.
world championship medals | Achievement | Recognition of global-level sports achievement, contributing to an athlete's marketability.
Olympic medals | Achievement | Recognition in the Olympic Games, significant for athlete branding and endorsement opportunities.

System: You are an expert Named Entity Recognition (NER) system. Label all identifiable entities, abstract concepts, and meaningful ideas in the provided input text, emphasizing relevance to the financial domain.

Ensure the following:
Label All Meaningful Entities: Identify every meaningful entity related to financial analysis, economic dynamics, or market contexts.
Define New Concepts as Needed: Introduce and define entity types for abstract financial concepts or industry-specific terms not typically found in standard NER tasks.
Provide an Exhaustive Entity List: Include every relevant label mentioned in the input text.

Answer in the following format:
<entity from the text> | <entity concept> | <description of entity group/concept>,
<entity from the text> | <entity concept> | <description of entity group/concept>,
...

Here is an Example : 
Input: 
Lawmakers continue to try to police social media use among teens — but Meta, parent company to Facebook, Instagram, and Threads, is pushing another group of companies to do the security work. Meta is expected to announce a proposal on Nov. 15 that will push for tech giants like Google and Apple to carry a bigger burden in keeping teenagers off of potentially harmful platforms. Meta's vision is that these companies, which manage app stores such as the Apple App Store and Google Play Store, require parental approval for teenagers aged 13 to 15 to download applications, according to a report by The Washington Post.

Output:
Lawmakers | Regulatory agents | Individuals or groups responsible for creating and enacting laws, often influencing economic and regulatory environments.  
social media | Digital Channel | Online media channels for content sharing and user interaction, particularly influential in advertising and consumer engagement.
Meta | Company | Parent company of Facebook, Instagram, and Threads, involved in social media and technology sectors.  
Facebook | Company | Social media platform owned by Meta, significant player in digital advertising and social media markets.  
Instagram | Company | Photo and video sharing social media platform owned by Meta, influential in marketing and consumer engagement.  
Threads | Company | Social media platform owned by Meta, contributing to the digital communication landscape.  
Nov. 15 | Date | Specific date relevant for financial or regulatory announcements, potentially impacting market perceptions. 
tech giants | Major Companies | Entities that hold substantial market power in the technology sector. 
Google | Company | Technology company known for its search engine and digital services, significant in advertising and app distribution.  
Apple | Company | Technology company known for its hardware and software products, influential in consumer technology and app distribution.  
bigger burden | Operational Challenge | heightened difficulties or obstacles impacting a company’s operations, often resulting in resource strain or inefficiencies.
Apple App Store | Platform | Digital distribution platform for applications on Apple devices, relevant for app market dynamics.  
Google Play Store | Platform | Digital distribution platform for applications on Android devices, important for app market dynamics.  
Parental approval | Concept | Regulatory measure proposed to manage access to applications by minors, impacting technology and social media usage.  
The Washington Post | Newspaper | News outlet providing reports and analysis, influential in shaping public opinion and regulatory discourse.
User: KEY POINTS: Drugmakers are turning to more direct approaches to boost diversity in clinical trials, which have traditionally underrepresented people of color. Eli Lilly developed a mobile testing lab during the pandemic to continue clinical trials amid lockdowns, but it now serves as a vehicle to recruit and engage new communities on location. Firms such as Pfizer have increasingly turned to social media for recruitment, which CEO Albert Bourla says has helped speed up enrollment. Retailers CVS Health, Walgreens and Kroger have announced initiatives to provide clinical trial enrollment and follow-up services in their pharmacy clinics. Sharon Kimbrough went to the Black Women's Expo in Atlanta to sell her memoir. Getting tested for Alzheimer's was the furthest thing from her mind, but when nurses from Eli Lilly approached
Assistant:

Drugmakers | Industry Sector | Companies engaged in the research, development, and marketing of pharmaceutical drugs.
diversity in clinical trials | Concept | Efforts to include diverse populations in medical research to ensure comprehensive trial results.
people of color | Demographic Group | Ethnic and racial demographic often underrepresented in clinical research.
Eli Lilly | Company | Pharmaceutical company involved in creating a mobile testing lab to improve clinical trial inclusivity.
mobile testing lab | Facility | A mobile unit designed for conducting medical tests and engaging with diverse communities.
pandemic | Event | The global COVID-19 health crisis impacting various sectors, including pharmaceutical research.
clinical trials | Research Process | Systematic studies conducted to evaluate the efficacy and safety of medical treatments.
Pfizer | Company | Major pharmaceutical company using social media to enhance recruitment efforts for clinical studies.
social media | Digital Channel | Platforms used for communication and recruitment, significant in modern marketing strategies.
Albert Bourla | Person | CEO of Pfizer, involved in strategic decision-making in the pharmaceutical industry.
enrollment | Process | The act of recruiting participants into clinical trials, critical for study validity.
CVS Health | Company | Retail pharmacy with services related to clinical trial enrollment and patient follow-up.
Walgreens | Company | Nationwide pharmacy chain offering clinical trial services within its health clinics.
Kroger | Company | Retail company providing health services and clinical trial enrollment in its pharmacies.
Sharon Kimbrough | Person | Individual engaged in marketing a memoir, incidentally encountered clinical research recruitment efforts.
Black Women's Expo | Event | Public event aimed at a specific cultural and gender demographic, providing networking and promotional opportunities.
Atlanta | Location | City where the aforementioned expo took place, relevant to regional market demographics.
Alzheimer's | Disease | A neurodegenerative disorder, highlighting the need for diverse clinical trials in its research and treatment efforts.

System: You are an expert Named Entity Recognition (NER) system. Label all identifiable entities, abstract concepts, and meaningful ideas in the provided input text, emphasizing relevance to the financial domain.

Ensure the following:
Label All Meaningful Entities: Identify every meaningful entity related to financial analysis, economic dynamics, or market contexts.
Define New Concepts as Needed: Introduce and define entity types for abstract financial concepts or industry-specific terms not typically found in standard NER tasks.
Provide an Exhaustive Entity List: Include every relevant label mentioned in the input text.

Answer in the following format:
<entity from the text> | <entity concept> | <description of entity group/concept>,
<entity from the text> | <entity concept> | <description of entity group/concept>,
...

Here is an Example : 
Input: 
Lawmakers continue to try to police social media use among teens — but Meta, parent company to Facebook, Instagram, and Threads, is pushing another group of companies to do the security work. Meta is expected to announce a proposal on Nov. 15 that will push for tech giants like Google and Apple to carry a bigger burden in keeping teenagers off of potentially harmful platforms. Meta's vision is that these companies, which manage app stores such as the Apple App Store and Google Play Store, require parental approval for teenagers aged 13 to 15 to download applications, according to a report by The Washington Post.

Output:
Lawmakers | Regulatory agents | Individuals or groups responsible for creating and enacting laws, often influencing economic and regulatory environments.  
social media | Digital Channel | Online media channels for content sharing and user interaction, particularly influential in advertising and consumer engagement.
Meta | Company | Parent company of Facebook, Instagram, and Threads, involved in social media and technology sectors.  
Facebook | Company | Social media platform owned by Meta, significant player in digital advertising and social media markets.  
Instagram | Company | Photo and video sharing social media platform owned by Meta, influential in marketing and consumer engagement.  
Threads | Company | Social media platform owned by Meta, contributing to the digital communication landscape.  
Nov. 15 | Date | Specific date relevant for financial or regulatory announcements, potentially impacting market perceptions. 
tech giants | Major Companies | Entities that hold substantial market power in the technology sector. 
Google | Company | Technology company known for its search engine and digital services, significant in advertising and app distribution.  
Apple | Company | Technology company known for its hardware and software products, influential in consumer technology and app distribution.  
bigger burden | Operational Challenge | heightened difficulties or obstacles impacting a company’s operations, often resulting in resource strain or inefficiencies.
Apple App Store | Platform | Digital distribution platform for applications on Apple devices, relevant for app market dynamics.  
Google Play Store | Platform | Digital distribution platform for applications on Android devices, important for app market dynamics.  
Parental approval | Concept | Regulatory measure proposed to manage access to applications by minors, impacting technology and social media usage.  
The Washington Post | Newspaper | News outlet providing reports and analysis, influential in shaping public opinion and regulatory discourse.
User: Check out the companies making headlines in midday trading. Berkshire Hathaway — The conglomerate's A shares rose more than 1% as investors cheered a strong earnings report from Warren Buffett's company. On Saturday, Berkshire reported a 12.6% jump in operating earnings in the first quarter , driven by a rebound in the conglomerate's insurance business. The stock briefly topped $500,000 apiece Monday. Catalent — Catalent shares plunged 25.9% after the company said it will delay the release of its fiscal third-quarter results and expects significant cuts to its 2023 guidance. Tyson Foods — Shares plummeted 16.4% after the food production company posted an unexpected loss for the recent quarter and cut its revenue outlook for the year. Tyson Foods now said it anticipates between $53 billion and $54
Assistant:

Berkshire Hathaway | Company | Large conglomerate holding company led by Warren Buffett, known for its diversified investments and insurance operations.  
A shares | Stock Category | Specific class of stock representing ownership in a company, often with different voting rights or price levels.  
Warren Buffett | Individual | Well-known investor and CEO of Berkshire Hathaway, influential in finance and investment sectors.  
earnings report | Financial Report | Document detailing a company's financial performance over a specified period, crucial for investor analysis.  
operating earnings | Financial Metric | Measure of profit earned from a company's core business operations, excluding deductions.  
insurance business | Industry Segment | Sector of business activities related to risk management and indemnity services.  
$500,000 apiece | Stock Price | The price per share of stock, reflecting the market value of a company's equity on a per-share basis.  
Catalent | Company | Provider of advanced delivery technologies, development, and manufacturing solutions for drugs, biologics, and consumer health products.  
fiscal third-quarter results | Financial Term | Reporting timeline for a company's third quarter financial performance, influencing stock valuations and investor expectations.  
2023 guidance | Financial Forecast | A company's expectations and predictions for financial performance in the year 2023, used for investor planning.  
Tyson Foods | Company | American multinational corporation and the world's second largest processor and marketer of chicken, beef, and pork.  
loss | Financial Outcome | A negative financial result for a company, when expenses exceed income, impacting company valuation.  
revenue outlook | Financial Forecast | Projection or estimate of future income generated from normal business operations, guiding investor and analyst expectations.  
$53 billion and $54 | Financial Range | Financial forecast indicating expected revenue, significant in evaluating business performance.

System: 
        Ты - система классификации обращений в техподдержку. Проанализируй обращение и верни ТОЛЬКО JSON-объект нужного формата, без дополнительного текста
        
User: 
        Важно: ответ должен содержать ТОЛЬКО JSON, без вступительного текста или пояснений.
Выбери подходящую категорию из списка. Если категория не подходит ни к одной из текущих, отметь как new_category: true.
Пример ответа:
        {
            "category": "одна из категорий: СПП/Скидка ВБ, Логистика, Акции, Цены, Технические проблемы, Запрос на выгрузку данных",
            "new_category": true/false,
            "summary": "краткое описание проблемы"
        }

Текст обращения:

ИНН:072600395246
ID:431342
№ диалога:34576346
ННБ

Обращение от 16.08.24. Продавец допустил ошибки при редактировании розничной цены НМ 18053854 (номер задания на обновление 35182286) и карточка попала в Карантин цен. Позднее продавец решил изменить скидку на другой товар НМ 217156781 (номер задания на обновление 35182296). Одновременно с постановкой скидки на второй артикул сработало изменение цены из карантина по первому артикулу.

Почему и каким образом была применена ошибочная цена из карантина?
        ISSUE: ИНН:027206263880
ID:1287909
№ диалога:39429746
НБ
у прода товар раскупили по 85 рублей, при цене 389. Он прислал скрины что на сайте цена (проверили, цена верная) правильная при заказе - 389 арт 185435063, 2389046189 сборочное задание. можно проверить? 

REPLY: Даша а вы запрашивали доступы в дэшборды цен? там вся инфа есть если что
REPLY: со стороны цен вижу, что до 30.10 цена на артикул 185435063 была как раз 85 рублей, пролилась по автоакции 6.05.24
REPLY: @karlash.viktor
Assistant:

```json
{
    "category": "Цены",
    "new_category": false,
    "summary": "Несоответствие цены товара на сайте и при заказе, проверка через дэшборды цен."
}
```

System: You are an expert Named Entity Recognition (NER) system. Label all identifiable entities, abstract concepts, and meaningful ideas in the provided input text, emphasizing relevance to the financial domain.

Ensure the following:
Label All Meaningful Entities: Identify every meaningful entity related to financial analysis, economic dynamics, or market contexts.
Define New Concepts as Needed: Introduce and define entity types for abstract financial concepts or industry-specific terms not typically found in standard NER tasks.
Provide an Exhaustive Entity List: Include every relevant label mentioned in the input text.

Answer in the following format:
<entity from the text> | <entity concept> | <description of entity group/concept>,
<entity from the text> | <entity concept> | <description of entity group/concept>,
...

Here is an Example : 
Input: 
Lawmakers continue to try to police social media use among teens — but Meta, parent company to Facebook, Instagram, and Threads, is pushing another group of companies to do the security work. Meta is expected to announce a proposal on Nov. 15 that will push for tech giants like Google and Apple to carry a bigger burden in keeping teenagers off of potentially harmful platforms. Meta's vision is that these companies, which manage app stores such as the Apple App Store and Google Play Store, require parental approval for teenagers aged 13 to 15 to download applications, according to a report by The Washington Post.

Output:
Lawmakers | Regulatory agents | Individuals or groups responsible for creating and enacting laws, often influencing economic and regulatory environments.  
social media | Digital Channel | Online media channels for content sharing and user interaction, particularly influential in advertising and consumer engagement.
Meta | Company | Parent company of Facebook, Instagram, and Threads, involved in social media and technology sectors.  
Facebook | Company | Social media platform owned by Meta, significant player in digital advertising and social media markets.  
Instagram | Company | Photo and video sharing social media platform owned by Meta, influential in marketing and consumer engagement.  
Threads | Company | Social media platform owned by Meta, contributing to the digital communication landscape.  
Nov. 15 | Date | Specific date relevant for financial or regulatory announcements, potentially impacting market perceptions. 
tech giants | Major Companies | Entities that hold substantial market power in the technology sector. 
Google | Company | Technology company known for its search engine and digital services, significant in advertising and app distribution.  
Apple | Company | Technology company known for its hardware and software products, influential in consumer technology and app distribution.  
bigger burden | Operational Challenge | heightened difficulties or obstacles impacting a company’s operations, often resulting in resource strain or inefficiencies.
Apple App Store | Platform | Digital distribution platform for applications on Apple devices, relevant for app market dynamics.  
Google Play Store | Platform | Digital distribution platform for applications on Android devices, important for app market dynamics.  
Parental approval | Concept | Regulatory measure proposed to manage access to applications by minors, impacting technology and social media usage.  
The Washington Post | Newspaper | News outlet providing reports and analysis, influential in shaping public opinion and regulatory discourse.
User: KEY POINTS: Analysts polled by Refinitiv were expecting a net loss of 205 million euros for the quarter ending June 30. The low-cost airline said Monday that its next fiscal 12 months "will be a very challenging year." The Irish firm said that it expects traffic to drop by 60% in 2020/2021, but the uncertainty around the pandemic doesn't allow it to provide further guidance for the year. Ryanair posted a net loss of 185 million euros ($216.4 million) for the first quarter of its fiscal 2021 year, slightly better than market expectations. Analysts polled by Refinitiv were expecting a net loss of 205 million euros for the quarter ending June 30. The low-cost airline said Monday that its next fiscal 12 months "will be a very challenging
Assistant:

Analysts | Financial Analysts | Professionals who evaluate financial data and trends to provide insights and expectations for company performance.
Refinitiv | Data Provider | Global provider of financial market data and infrastructure, used for market analysis and investment decision making.
net loss | Financial Metric | Measure of a company's financial performance, indicating that expenses exceed revenues for a specific period.
205 million euros | Monetary Value | Specific amount of net loss anticipated by market analysts for the quarter.
Quarter ending June 30 | Financial Period | Specific timeframe for which financial performance is being reported and analyzed.
low-cost airline | Business Model | An airline offering lower fares and fewer comforts, often with significant implications for operational strategy and cost management.
next fiscal 12 months | Fiscal Year | Company's accounting period for budgeting and financial reporting purposes.
challenging year | Operational Challenge | A period anticipated to have considerable difficulties, often affecting operations and financial performance.
Irish firm | Company Nationality | Indicates the geographic location of the company's headquarters, potentially impacting regulatory and market conditions.
traffic | Operational Metric | Measure of airline passengers or cargo, significant in assessing company performance and market demand.
60% | Percentage | Indicates the anticipated decrease in airline traffic, reflecting reduced operational volume and revenue.
2020/2021 | Fiscal Year | Specific twelve-month period for which financial and operational expectations are set.
uncertainty around the pandemic | Market Condition | Ongoing unpredictable impact of COVID-19 affecting global markets and business operations.
guidance | Financial Forecasting | Company’s projection and communication of expected future financial performance, significant for investor decision-making.
Ryanair | Company | Low-cost airline based in Ireland, influential in the European aviation market.
185 million euros ($216.4 million) | Monetary Value | Actual net loss reported by the company for its fiscal first quarter.
market expectations | Market Sentiment | Consensus estimates or projections by analysts regarding a company's financial performance.