app_py.py

# -*- coding: utf-8 -*-
"""app.py

Automatically generated by Colab.

Original file is located at
    https://colab.research.google.com/drive/1fuzwSkyjYRLfvGxQGclxwX0Y9OD2IK6s
"""

# app.py

# Step 1: Install necessary libraries
# This is handled by requirements.txt in Hugging Face Spaces,
# but you would run this line in a fresh environment:
# !pip install -q gradio transformers torch sentencepiece

# Step 2: Import libraries
import gradio as gr
import re
from transformers import pipeline

# --- Backend Logic ---

# Step 3: Load the Hugging Face Model
print("Loading Hugging Face model (google/flan-t5-small)... This may take a moment.")
text_generator = pipeline(
    "text2text-generation",
    model="google/flan-t5-small"
)
print("Model loaded successfully!")


def parse_chat_file(file_content):
    """
    A robust parser for both WhatsApp and Telegram text exports.
    """
    lines = file_content.split('\n')
    chat_data = []
    pattern = re.compile(
        r'^(?:\u200e)?\[?(\d{1,2}[/.]\d{1,2}[/.]\d{2,4}),?\s+(\d{1,2}:\d{2}(?::\d{2})?(?:\s*[AP]M)?)\]?\s*-\s*([^:]+):\s*(.*)',
        re.IGNORECASE
    )
    for line in lines:
        match = pattern.match(line)
        if match:
            sender, message = match.group(3), match.group(4)
            if "created this group" not in message and "added" not in message and "changed the subject" not in message:
                 chat_data.append(f"{sender}: {message}")
        elif chat_data and line.strip():
            chat_data[-1] += "\n" + line
    return "\n".join(chat_data) if chat_data else "Could not parse chat file."


def process_chat_request(user_question, chat_history, state_data):
    """
    The main function that handles the chat logic using the local Hugging Face model.
    """
    context_size = state_data.get("context_size")
    chat_content = state_data.get("chat_content")
    temperature = state_data.get("temperature")

    if not all([context_size, chat_content, temperature is not None]):
        raise gr.Error("Chat content or configuration is missing. Please restart by uploading a file.")
    if not user_question:
        raise gr.Error("Please enter a question.")

    context_to_use = chat_content[-int(context_size):]

    prompt = f"""
    Based on the following chat history, provide a detailed answer to the user's question.

    CONTEXT:
    ---
    {context_to_use}
    ---

    QUESTION: {user_question}

    ANSWER:
    """

    try:
        result = text_generator(
            prompt,
            max_length=300,
            num_beams=3,
            temperature=temperature
        )
        bot_response = result[0]['generated_text']
    except Exception as e:
        raise gr.Error(f"An error occurred with the model: {e}")

    chat_history.append((user_question, bot_response))
    return "", chat_history

# --- Gradio UI Definition ---

with gr.Blocks(theme=gr.themes.Soft(primary_hue="orange", secondary_hue="orange"), title="Local Chat Analyzer") as demo:
    app_state = gr.State({})

    with gr.Column(visible=True) as welcome_page:
        gr.Markdown(
            """
            <div style='text-align: center; font-family: "Garamond", serif; padding-top: 30px;'>
                <h1 style='font-size: 3.5em;'>Local Chat Analyzer</h1>
                <p style='font-size: 1.5em; color: #555;'>Powered by a Hugging Face Model. No API key needed!</p>
            </div>
            """
        )
        gr.HTML(
            """
            <div style='text-align: center; padding: 20px;'>
                <img src='https://media.giphy.com/media/v1.Y2lkPTc5MGI3NjExd2Vjb3M2eGZzN2FkNWZpZzZ0bWl0c2JqZzZlMHVwZ2l4b2t0eXFpcyZlcD12MV9pbnRlcm5hbF9naWZfYnlfaWQmY3Q9Zw/YWjDA4k2n6d5Ew42zC/giphy.gif'
                     style='max-width: 350px; margin: auto; border-radius: 20px; box-shadow: 0 8px 16px rgba(0,0,0,0.1);' />
            </div>
            """
        )
        with gr.Row():
            with gr.Column():
                with gr.Accordion("How do I get my chat file?", open=False):
                    gr.Markdown("""
                    ### Exporting your WhatsApp Chat
                    1.  **On your phone**, open the WhatsApp chat you want to analyze.
                    2.  Tap the **three dots** (⋮) in the top-right corner.
                    3.  Select **More** > **Export chat**.
                    4.  Choose **Without media**. This will create a smaller `.txt` file.
                    5.  Save the file to your phone or email it to yourself to access it on your computer.
                    6.  For more details, visit the [official WhatsApp Help Center](https://faq.whatsapp.com/1180414079177245/).
                    """)
                gr.Markdown("### 1. Upload Your Chat File")
                chat_file_upload = gr.File(label="Upload WhatsApp/Telegram .txt Export")
            with gr.Column():
                gr.Markdown("### 2. Customize Parameters")
                context_slider = gr.Slider(500, 20000, value=5000, step=500, label="Context Window Size (Characters)")
                temp_slider = gr.Slider(0.1, 1.5, value=0.7, step=0.1, label="Temperature (Creativity)")
        lets_chat_button = gr.Button("💬 Start Chatting 💬", variant="primary")

    with gr.Column(visible=False) as chat_page:
        gr.Markdown("<h1 style='text-align: center;'>Chat Analyzer</h1>")
        chatbot_ui = gr.Chatbot(height=600, bubble_full_width=False)
        with gr.Row():
            user_input_box = gr.Textbox(placeholder="Ask a question about your chat...", scale=5)
            submit_button = gr.Button("Send", variant="primary", scale=1)

    def go_to_chat(current_state, chat_file, context_size, temperature):
        if chat_file is None:
            raise gr.Error("A chat file must be uploaded.")
        with open(chat_file.name, 'r', encoding='utf-8') as f:
            content = f.read()
        parsed_content = parse_chat_file(content)
        if "Could not parse" in parsed_content:
            raise gr.Error("Failed to parse the chat file. Please check the format.")
        new_state = {
            "chat_content": parsed_content,
            "context_size": context_size,
            "temperature": temperature,
        }
        return (
            new_state,
            gr.Column(visible=False),
            gr.Column(visible=True)
        )

    lets_chat_button.click(
        fn=go_to_chat,
        inputs=[app_state, chat_file_upload, context_slider, temp_slider],
        outputs=[app_state, welcome_page, chat_page]
    )
    submit_button.click(
        fn=process_chat_request,
        inputs=[user_input_box, chatbot_ui, app_state],
        outputs=[user_input_box, chatbot_ui]
    )
    user_input_box.submit(
        fn=process_chat_request,
        inputs=[user_input_box, chatbot_ui, app_state],
        outputs=[user_input_box, chatbot_ui]
    )

if __name__ == "__main__":
    demo.launch()