from fastapi import FastAPI, HTTPException, UploadFile, File,Request,Depends,status,BackgroundTasks
from fastapi.security import OAuth2PasswordBearer
from pydantic import BaseModel
from typing import Optional, List
from uuid import uuid4
import os
from dotenv import load_dotenv
from rag import *
from fastapi.responses import StreamingResponse
import json
from prompt import *
from fastapi.middleware.cors import CORSMiddleware
import requests
import pandas as pd



load_dotenv()

## setup authorization
api_keys = [os.environ.get("FASTAPI_API_KEY")]

oauth2_scheme = OAuth2PasswordBearer(tokenUrl="token")  # use token authentication


def api_key_auth(api_key: str = Depends(oauth2_scheme)):
    if api_key not in api_keys:
        raise HTTPException(
            status_code=status.HTTP_401_UNAUTHORIZED,
            detail="Forbidden"
        )

dev_mode = os.environ.get("DEV")

if dev_mode == "True":
    app = FastAPI()
else:
    app = FastAPI(dependencies=[Depends(api_key_auth)])

app.add_middleware(CORSMiddleware, allow_origins=["*"], allow_credentials=True, allow_methods=["*"], allow_headers=["*"])

# Pydantic model for the form data
class verify_response_model(BaseModel):
    response: str = Field(description="The response from the user to the question")
    answers: list[str] = Field(description="The possible answers to the question to test if the user read the entire book")
    question: str = Field(description="The question asked to the user to test if they read the entire book")

class UserInput(BaseModel):
    query: str
    stream: Optional[bool] = False
    messages: Optional[list[dict]] = []

class Artwork(BaseModel):
    name: str
    artist: str
    image_url: str
    date: str
    description: str


# Global variable to store the data
artworks_data = []

def load_data():
    global artworks_data

    # Provide the path to your local spreadsheet
    spreadsheet_path = "data.xlsx"

    # Read the spreadsheet into a DataFrame
    df = pd.read_excel(spreadsheet_path, sheet_name='Sheet1')  # Adjust sheet_name as needed

    df = df.fillna(False)
    # Convert DataFrame to a list of dictionaries
    df_filtered = df[df['Publication'] == True]

    artworks_data = df_filtered.to_dict(orient='records')

    print("Data loaded successfully")

load_data()

#endpoinds
@app.get("/artworks/{artist_name}")
async def get_artworks_by_artist(artist_name: str):
    artist_name_lower = artist_name.lower()
    results = []

    for artwork in artworks_data:
        if artist_name_lower in artwork['Artiste'].lower():
            result = {
                'name':artwork['Titre français'],
                'artist':artwork['Artiste'],
                'image_url':artwork['Image_URL'],
                'date':str(artwork['Date']),  # Ensure date is a string
                'description':artwork['Media']
            }
            results.append(result)

    if not results:
        raise HTTPException(status_code=404, detail="Artist not found")

    return results

@app.post("/generate_sphinx")
async def generate_sphinx():
    try:
        sphinx : sphinx_output = generate_sphinx_response()
        return {"question": sphinx.question, "answers": sphinx.answers}
    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))
    
@app.post("/verify_sphinx")
async def verify_sphinx(response: verify_response_model):
    try:
        score : bool = verify_response(response.response, response.answers, response.question)
        return {"score": score}
    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))
    
@app.post("/generate")
async def generate(user_input: UserInput):
    try:
        print(user_input.stream,user_input.query)
        if user_input.stream:
            return StreamingResponse(generate_stream(user_input.query,user_input.messages,stream=True),media_type="application/json")
        else:
            return generate_stream(user_input.query,user_input.messages,stream=False)
    except Exception as e:
        return {"message": str(e)}