from fastapi import FastAPI, Request
from fastapi.responses import JSONResponse
import httpx
from telegram import Update
from telegram.ext import ApplicationBuilder, CommandHandler, ContextTypes
import os

import logging
# from transformers import pipeline
from huggingface_hub import InferenceClient, login
import langid

# Configure logging
logging.basicConfig(format="%(asctime)s - %(levelname)s - %(message)s", level=logging.INFO)
logger = logging.getLogger(__name__)

# Replace this with your Hugging Face Space URL
HUGGING_FACE_SPACE_URL = "https://demaking-decision-helper-bot.hf.space"

# Get Telegram bot token from environment variables
TOKEN = os.getenv("TELEGRAM_BOT_TOKEN")
if not TOKEN:
    raise ValueError("Missing Telegram Bot Token. Please set TELEGRAM_BOT_TOKEN environment variable.")


# Get Hugging Face API token from environment variable
HF_HUB_TOKEN = os.getenv("HUGGINGFACEHUB_API_TOKEN")
if not HF_HUB_TOKEN:
    raise ValueError("Missing Hugging Face API token. Please set HUGGINGFACEHUB_API_TOKEN.")

# Login and initialize the client
login(token=HF_HUB_TOKEN)
client = InferenceClient(api_key=HF_HUB_TOKEN)


app = FastAPI()

# Function to detect language
def detect_language(user_input):
    try:
        lang, _ = langid.classify(user_input)
        return "hebrew" if lang == "he" else "english" if lang == "en" else "unsupported"
    except Exception as e:
        logging.error(f"Language detection error: {e}")
        return "unsupported"


# Function to generate response
def generate_response(text):
    language = detect_language(text)

    if language == "hebrew":
        content = "תענה בקצרה אבל תשתף את תהליך קבלת ההחלטות שלך, " + text
        model = "microsoft/Phi-3.5-mini-instruct"
    elif language == "english":
        content = "keep it short but tell your decision making process, " + text
        model = "mistralai/Mistral-Nemo-Instruct-2407"
    else:
        return "Sorry, I only support Hebrew and English."

    messages = [{"role": "user", "content": content}]
    
    completion = client.chat.completions.create( 
        model=model,
        messages=messages,
        max_tokens=2048,
        temperature=0.5,
        top_p=0.7
    )
    return completion.choices[0].message.content

    
@app.post("/generate_response")
async def generate_text(request: Request):
    """
    Endpoint to generate a response from the chat model.
    Expects a JSON with a "text" field.
    """
    try:
        data = await request.json()
        text = data.get("text", "").strip()
        if not text:
            return {"error": "No text provided"}
        response = generate_response(text)
        return {"response": response}
    except Exception as e:
        logging.error(f"Error processing request: {e}")
        return {"error": "An unexpected error occurred."}


@app.get("/")
async def root():
    """
    Root endpoint to check that the API is running.
    """
    return {"message": "Decision Helper API is running!"}


# -------------------------
# Function to fetch response from FastAPI
# -------------------------
async def call_hugging_face_space(input_data: str):
    """
    Sends a POST request to the FastAPI API with the user's imput and returns the JSON response.
    """
    async with httpx.AsyncClient(timeout=45.0) as client:
        try:
            response = await client.post(HUGGING_FACE_SPACE_URL, json={"input": input_data})
            response.raise_for_status()  # Raise exception for HTTP 4XX/5XX errors
            return response.json()
        except httpx.HTTPStatusError as e:
            logger.error(f"HTTP Error: {e.response.status_code} - {e.response.text}")
            return {"response": "Error: API returned an error."}
        except httpx.RequestError as e:
            logger.error(f"Request Error: {e}")
            return {"response": "Error: Request Error. Could not reach API."}
        except httpx.ConnectError as e:
            logger.error(f"Connection error: {e}")
            return {"error": "Could not connect to the Hugging Face Space"}
        except Exception as e:
            logger.error(f"Unexpected Error: {e}")
            return {"response": "Error: Unexpected error occurred."}


@app.post("/webhook/{token}")
async def webhook(token: str, request: Request):
    if token != TOKEN:
        logger.error(f"Tokens doesn't match. {e}")
        return JSONResponse(status_code=403, content={"message": "Forbidden"})

    update = Update.de_json(await request.json(), None)
    message_text = update.message.text

    result = await call_hugging_face_space(message_text)
    
    return JSONResponse(content=result)


def start_telegram_bot():
    application = ApplicationBuilder().token(TOKEN).build()

    # Set up a command handler
    async def start(update: Update, context: ContextTypes.DEFAULT_TYPE):
        await update.message.reply_text("Hello! Tell me your decision-making issue, and I'll try to help.")
        logger.info("Start command received.")

    
    async def handle_message(update: Update, context: ContextTypes.DEFAULT_TYPE):
        user_text = update.message.text
        logger.info(f"User message: {user_text}")
        
        # Send the user text to the FastAPI server and get the response.
        result = await call_hugging_face_space(user_text)
        response_text = result.get("response", "Error generating response.")
        
        logger.info(f"API Response: {response_text}")
        await update.message.reply_text(response_text)
        
        application.add_handler(CommandHandler("start", start))
        application.add_handler(MessageHandler(filters.TEXT & ~filters.COMMAND, handle_message))
    
        # Start the bot
        application.run_polling()


if __name__ == "__main__":
    import threading
    
    # Start the Telegram bot in a separate thread
    threading.Thread(target=start_telegram_bot).start()
    
    # Start the FastAPI app
    import uvicorn
    uvicorn.run(app, host="0.0.0.0", port=7860)