from flask import Flask, request, jsonify
from flask_cors import CORS
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
import logging
import os

# Logging setup
logging.basicConfig(level=logging.DEBUG, format='%(asctime)s - %(levelname)s - %(message)s')

# Hugging Face Model Hub Repository
MODEL_REPO = "./"  # Replace with your Hugging Face model repo name

# Load tokenizer and model from Hugging Face Model Hub
try:
    logging.info("Loading model and tokenizer from Hugging Face Model Hub...")
    tokenizer = AutoTokenizer.from_pretrained(MODEL_REPO)
    dtype = torch.bfloat16 if torch.cuda.is_available() else torch.float32
    model = AutoModelForCausalLM.from_pretrained(MODEL_REPO, torch_dtype=dtype).to(
        "cuda" if torch.cuda.is_available() else "cpu"
    )
    logging.info("Model loaded successfully.")
except Exception as e:
    logging.error("Failed to load the model or tokenizer.", exc_info=True)
    raise e

# Flask app initialization
app = Flask(__name__)
CORS(app)  # Enable CORS

def generate_response(prompt):
    """Generate a response from the model given a prompt."""
    try:
        logging.debug(f"Generating response for prompt: {prompt}")
        inputs = tokenizer(prompt, return_tensors='pt').to(model.device)
        input_length = inputs.input_ids.shape[1]
        outputs = model.generate(
            **inputs, max_new_tokens=128, do_sample=True, temperature=0.7, top_p=0.7, top_k=50, return_dict_in_generate=True
        )
        token = outputs.sequences[0, input_length:]
        output_str = tokenizer.decode(token, skip_special_tokens=True)
        logging.debug(f"Generated response: {output_str}")
        return output_str
    except Exception as e:
        logging.error("Error during response generation", exc_info=True)
        return "Sorry, I encountered an error while generating the response."

@app.route('/chat', methods=['POST'])
def chat():
    """Endpoint to handle chat requests."""
    try:
        logging.debug("Received a POST request to /chat")
        data = request.json
        logging.debug(f"Request data: {data}")

        if not data or "message" not in data:
            return jsonify({"error": "Invalid request. 'message' field is required."}), 400

        user_input = data.get("message", "")
        prompt = f"<human>: {user_input}\n<bot>:"
        response = generate_response(prompt)
        return jsonify({"response": response}), 200
    except Exception as e:
        logging.error("Error in /chat endpoint", exc_info=True)
        return jsonify({"error": "Internal server error", "message": str(e)}), 500

if __name__ == "__main__":
    # Get the port from environment variable or default to 5000
    port = int(os.getenv("PORT", 5000))
    logging.info(f"Starting Flask app on port {port}")
    app.run(debug=True, host="0.0.0.0", port=port)