app.py

from flask import Flask, request, jsonify
from flask_cors import CORS  # To handle CORS if needed
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

app = Flask(__name__)
CORS(app)  # Enable CORS for frontend communication

MODEL_NAME = "tanusrich/Mental_Health_Chatbot"
device = "cuda" if torch.cuda.is_available() else "cpu"

tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)

try:
    model = AutoModelForCausalLM.from_pretrained(
        MODEL_NAME,
        device_map="auto",
        torch_dtype=torch.float16,  # Uses half-precision for lower RAM usage
        low_cpu_mem_usage=True  
    ).to(device)
except Exception as e:
    print(f"Error loading model: {e}")
    exit(1)

@app.route("/chat", methods=["POST"])
def chat():
    try:
        data = request.json
        user_input = data.get("message", "").strip()

        if not user_input:
            return jsonify({"error": "Message is required"}), 400

        inputs = tokenizer(user_input, return_tensors="pt").to(device)

        with torch.no_grad():
            outputs = model.generate(
                input_ids=inputs["input_ids"], 
                attention_mask=inputs["attention_mask"], 
                max_length=150
            )

        response_text = tokenizer.decode(outputs[0], skip_special_tokens=True)

        return jsonify({"response": response_text})

    except Exception as e:
        return jsonify({"error": f"Internal Server Error: {str(e)}"}), 500

if __name__ == "__main__":
    app.run(host="0.0.0.0", port=5000, debug=True)