# import os
# import logging
# from fastapi import FastAPI
# from fastapi.middleware.cors import CORSMiddleware
# from fastapi.responses import JSONResponse, FileResponse
# from fastapi.staticfiles import StaticFiles
# from dotenv import load_dotenv
# from openai import AsyncOpenAI

# # Import your custom modules
# from chainlit.auth import create_jwt
# import chainlit as cl
# import uvicorn
# # Load environment variables from .env file
# load_dotenv()

# # Initialize logging
# logging.basicConfig(level=logging.INFO)
# logger = logging.getLogger(__name__)  # Use __name__ to get the root logger

# # Initialize FastAPI app
# app = FastAPI()

# # # CORS middleware setup
# # app.add_middleware(
# #     CORSMiddleware,
# #     allow_origins=["*"],  # Specify domains or use ["*"] for open access
# #     allow_credentials=True,
# #     allow_methods=["*"],  # Specify methods or use ["*"] for all methods
# #     allow_headers=["*"],  # Specify headers or use ["*"] for all headers
# # )

# client = AsyncOpenAI(api_key=os.environ["OPENAI_API_KEY"])

# settings = {
#     "model": "gpt-3.5-turbo",
#     "temperature": 0.7,
#     "max_tokens": 500,
#     "top_p": 1,
#     "frequency_penalty": 0,
#     "presence_penalty": 0,
# }

# app.mount("/", StaticFiles(directory="static", html=True), name="static")
# logger.info("Static files are being served from the 'static' directory.")

# @app.get("/")
# def read_root():
#     # Serve your static HTML file at the root.
#     logger.info(f"Serving static file for path")
#     return FileResponse('static/index.html')

# @app.get("/api")
# def read_api():
#     # This endpoint simply returns a JSON message.
#     return {"message": "Hello from the FastAPI API!"}
    
# @app.get("/custom-auth")
# def custom_auth():
#     # Verify the user's identity with custom logic.
#     token = create_jwt(cl.User(identifier="Test User"))
#     logger.info("Custom auth token generated.")
#     print("teeeeeee", token)
#     return JSONResponse({"token": token})

# @cl.on_chat_start
# async def on_chat_start():
#     cl.user_session.set(
#         "message_history",
#         [{"role": "system", "content": "You are a helpful assistant."}],
#     )
#     await cl.Message(content="Connected to Chainlit!").send()
#     logger.info("Chat started with Chainlit.")

# @cl.on_message
# async def on_message(message: cl.Message):
#     message_history = cl.user_session.get("message_history")
#     message_history.append({"role": "user", "content": message.content})

#     msg = cl.Message(content="")
#     await msg.send()

#     stream = await client.chat.completions.create(
#         messages=message_history, stream=True, **settings
#     )

#     async for part in stream:
#         if token := part.choices[0].delta.content or "":
#             await msg.stream_token(token)

#     message_history.append({"role": "assistant", "content": msg.content})
#     await msg.update()
#     logger.info("Message processed and response sent.")

#########################################################################################################################

from fastapi import FastAPI
from fastapi.staticfiles import StaticFiles
from fastapi.responses import FileResponse, JSONResponse
import os
import logging
from fastapi.middleware.cors import CORSMiddleware
from openai import AsyncOpenAI
 # Import your custom modules
from chainlit.auth import create_jwt
from dotenv import load_dotenv
import chainlit as cl
import uvicorn
import asyncio
from socket import gaierror
# Load environment variables from .env file
load_dotenv()

# Initialize logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)  # Use __name__ to get the root logger

app = FastAPI()

# CORS middleware setup
app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],  # Specify domains or use ["*"] for open access
    allow_credentials=True,
    allow_methods=["*"],  # Specify methods or use ["*"] for all methods
    allow_headers=["*"],  # Specify headers or use ["*"] for all headers
)
client = AsyncOpenAI(api_key=os.environ["OPENAI_API_KEY"])

settings = {
    "model": "gpt-3.5-turbo",
    "temperature": 0.7,
    "max_tokens": 500,
    "top_p": 1,
    "frequency_penalty": 0,
    "presence_penalty": 0,
}

# Assuming your static files are in a directory named 'static'.
# app.mount("/static", StaticFiles(directory="static"), name="static")
# app.mount("/assets", StaticFiles(directory="static/assets"), name="assets")
@app.get("/")
def read_root():
    # Serve your static HTML file at the root.
    return {"message": "Hello from the FastAPI API!"}

@app.get("/api")
def read_api():
    # This endpoint simply returns a JSON message.
    return {"message": "Hello from the FastAPI API!"}

@app.get("/custom-auth")
def custom_auth():
    # Verify the user's identity with custom logic.
    token = create_jwt(cl.User(identifier="Test User"))
    logger.info("Custom auth token generated.")
    print("teeeeeee", token)
    return JSONResponse({"token": token})

@cl.on_chat_start
async def on_chat_start():
    cl.user_session.set(
        "message_history",
        [{"role": "system", "content": "You are a helpful assistant."}],
    )
    await cl.Message(content="Connected to Chainlit!").send()
    logger.info("Chat started with Chainlit.")

@cl.on_message
async def on_message(message: cl.Message):
    message_history = cl.user_session.get("message_history")
    message_history.append({"role": "user", "content": message.content})

    msg = cl.Message(content="")
    await msg.send()

    try:
        stream = await client.chat.completions.create(
            messages=message_history, stream=True, **settings
        )

        async for part in stream:
            if token := part.choices[0].delta.content or "":
                await msg.stream_token(token)

    except gaierror as e:
        logger.error(f"Network error during OpenAI API call: {e}")
        await msg.update(content="Sorry, there was a network error.")
    except asyncio.TimeoutError:
        logger.error("Timeout error during OpenAI API call.")
        await msg.update(content="Sorry, the request timed out.")
    except Exception as e:
        logger.error(f"Unexpected error during OpenAI API call: {e}")
        await msg.update(content="Sorry, an unexpected error occurred.")

    message_history.append({"role": "assistant", "content": msg.content})
    await msg.update()
    logger.info("Message processed and response sent.")