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 class WhatifInput(BaseModel): question: str answer: 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)} @app.post("/whatif") async def generate_whatif(whatif_input: WhatifInput): try: @app.post("/whatif_chat") async def generate_whatif_chat(user_input: UserInput): try: if user_input.stream: return StreamingResponse(generate_stream_whatif_chat(user_input.query,user_input.messages,stream=True),media_type="application/json") else: return generate_stream_whatif_chat(user_input.query,user_input.messages,stream=False) except Exception as e: return {"message": str(e)}