updated

app.py

@@ -1,4 +1,3 @@
-<<<<<<< HEAD
 from flask import Flask, request, jsonify
 from flask_cors import CORS
 import torch
@@ -70,76 +69,3 @@ if __name__ == "__main__":
     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)
-=======
-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)
->>>>>>> 6ff0da104f5a2eb5ee298dc0164db0c0b16215e2