import logging
import os
from fastapi import FastAPI, Request
from contextlib import asynccontextmanager
from transformers import pipeline
from langdetect import detect
from huggingface_hub import login
import socket

# Global variables
HF_HUB_TOKEN = os.getenv("HUGGINGFACEHUB_API_TOKEN")

# Verify Hugging Face token
if not HF_HUB_TOKEN:
     raise ValueError("Missing Hugging Face API token. Please set HUGGINGFACEHUB_API_TOKEN in environment variables.")
login(token=HF_HUB_TOKEN)

# Load Hebrew and English text generation models
hebrew_generator = pipeline("text-generation", model="Norod78/hebrew-gpt_neo-small")
english_generator = pipeline("text-generation", model="distilgpt2")

# Function to detect language
def detect_language(user_input):
    try:
        lang = detect(user_input)
        return "hebrew" if lang == "he" else "english" if lang == "en" else "unsupported"
    except:
        return "unsupported"

# Function to generate a response
def generate_response(text):
    language = detect_language(text)
    if language == "hebrew":
        return hebrew_generator(text, max_length=100)[0]["generated_text"]
    elif language == "english":
        return english_generator(text, max_length=100)[0]["generated_text"]
    return "Sorry, I only support Hebrew and English."

# FastAPI lifespan event
@asynccontextmanager
async def lifespan(app: FastAPI):
    print("Starting application...")
    yield  # Wait until app closes
    print("Shutting down application...")

# Create FastAPI app
app = FastAPI(lifespan=lifespan)

@app.get("/")
async def root():
    return {"message": "Decision Helper API is running!"}

@app.post("/generate_response")
async def generate_text(request: Request):
    data = await request.json()
    text = data.get("text", "")
    if not text:
        return {"error": "No text provided"}
    
    response = generate_response(text)
    return {"response": response}

# Run the server
if __name__ == "__main__":
    import uvicorn
    uvicorn.run(app, host="0.0.0.0", port=7860)