from flask import Flask, request, jsonify, send_from_directory
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
import torch
import os

app = Flask(__name__)

# --- Load the model ---
model_name = "microsoft/phi-2"
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

print("Loading model... this may take a few minutes.")
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")
generator = pipeline("text-generation", model=model, tokenizer=tokenizer, device=0 if torch.cuda.is_available() else -1)
print("Model loaded.")

# --- Serve your custom HTML interface ---
@app.route("/")
def home():
    return send_from_directory(os.path.dirname(__file__), "index.html")

# --- API endpoint for AI ---
@app.route("/ask", methods=["POST"])
def ask():
    data = request.get_json()
    question = data.get("question", "")
    if not question:
        return jsonify({"answer": "Please ask a valid question."})

    try:
        response = generator(question, max_length=200, do_sample=True, temperature=0.7)
        answer = response[0]["generated_text"]
        return jsonify({"answer": answer})
    except Exception as e:
        print(e)
        return jsonify({"answer": "⚠️ There was an error generating the answer."})

if __name__ == "__main__":
    app.run(host="0.0.0.0", port=7860, debug=True)