# src/gradio_app.py
import gradio as gr
from agent import Agent
from create_database import load_and_process_dataset  # Import from create_database.py
import os
import uuid
import urllib.request
import logging

# Configure logging
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')

def download_model():
    model_url = "https://path/to/your/model.bin"
    model_path = "model.bin"

    if not os.path.exists(model_path):
        print("Downloading model...")
        urllib.request.urlretrieve(model_url, model_path)
        print("Model downloaded successfully.")

def respond(
    message,
    history: list[tuple[str, str]],
    system_message,
):
    model_path = "model.bin"  # Path to the downloaded model
    db_path = "agent.db"
    system_prompt = system_message

    # Check if the model is downloaded
    if not os.path.exists(model_path):
        download_model()

    # Check if the database exists, if not, initialize it
    if not os.path.exists(db_path):
        data_update_path = "data-update.txt"
        keyword_dir = "keyword"  # Updated keyword directory
        load_and_process_dataset(data_update_path, keyword_dir, db_path)

    agent = Agent(model_path, db_path, system_prompt)
    user_id = str(uuid.uuid4())  # Generate a unique user ID for each session

    response = agent.process_query(user_id, message)
    return response

"""
For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
"""
demo = gr.ChatInterface(
    respond,
    additional_inputs=[
        gr.Textbox(value="Vous êtes l'assistant intelligent de Les Chronique MTC. Votre rôle est d'aider les visiteurs en expliquant le contenu des Chroniques, Flash Infos et Chronique-FAQ de Michel Thomas. Utilisez le contexte fourni pour améliorer vos réponses et veillez à ce qu'elles soient précises et pertinentes.", label="System message"),
    ],
)

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