main.py

# src/main.py
from src.agent import Agent
from src.create_database import load_and_process_dataset  # Import from create_database.py
import os
import uuid
import requests
import logging
from llama_cpp import Llama

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

# Create the directory if it doesn't exist
local_dir = "models"
os.makedirs(local_dir, exist_ok=True)

# Specify the filename for the model
filename = "unsloth.Q4_K_M.gguf"
model_path = os.path.join(local_dir, filename)

# Function to download the model file
def download_model(repo_id, filename, save_path):
    # Construct the URL for the model file
    url = f"https://huggingface.co/{repo_id}/resolve/main/{filename}"

    # Download the model file
    response = requests.get(url)
    if response.status_code == 200:
        with open(save_path, 'wb') as f:
            f.write(response.content)
        print(f"Model downloaded to {save_path}")
    else:
        print(f"Failed to download model: {response.status_code}")

# Download the model if it doesn't exist
if not os.path.exists(model_path):
    download_model("adeptusnull/llama3.2-1b-wizardml-vicuna-uncensored-finetune-test", filename, model_path)

def main():
    model_path = "models/unsloth.Q4_K_M.gguf"  # Path to the downloaded model
    db_path = "agent.db"
    system_prompt = "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."
    max_tokens = 500
    temperature = 0.7
    top_p = 0.95

    # 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)

    # Load the model
    llm = Llama(
        model_path=model_path,
        n_ctx=572,  # Set the maximum context length
        max_tokens=max_tokens  # Control the maximum number of tokens generated in the response
    )

    agent = Agent(llm, db_path, system_prompt, max_tokens, temperature, top_p)

    while True:
        user_id = str(uuid.uuid4())  # Generate a unique user ID for each session
        user_query = input("Entrez votre requête: ")
        if user_query.lower() == 'exit':
            break

        try:
            response = agent.process_query(user_id, user_query)
            print("Réponse:", response)
        except Exception as e:
            print(f"Erreur lors du traitement de la requête: {e}")

        # Clean up expired interactions
        agent.memory.cleanup_expired_interactions()

if __name__ == "__main__":
    main()