import base64
from typing import List, Tuple

from fastapi import FastAPI, UploadFile, File, HTTPException, Request
from fastapi.responses import JSONResponse, HTMLResponse
from fastapi.templating import Jinja2Templates

import google.generativeai as genai

# Configure the Google Generative AI API key.
genai.configure(api_key="")

app = FastAPI()
templates = Jinja2Templates(directory="templates")

def generate_content_from_images(images: List[Tuple[str, bytes]]) -> str:
    """
    Given a list of tuples (mime_type, image_bytes), encode each image
    in base64, build a list of image dicts, append the fixed prompt,
    and call the Gemini model.
    """
    image_data_list = []
    for mime_type, image_bytes in images:
        image_data = base64.b64encode(image_bytes).decode("utf-8")
        image_data_list.append({'mime_type': mime_type, 'data': image_data})
    
    prompt = """analyse question , analyse option , and give me question , it's options and answer in json format 

fixed json format

below is example of json format ,the json structure is same like below

{
  \"question\": \"What is the capital of France?\",
  \"options\": [
    \"a. Berlin\",
    \"b. Madrid\",
    \"c. Paris\",
    \"d. Rome\"
  ],
  \"answer\": \"c\"
}

only json format nothing else"""
    
    payload = image_data_list.copy()
    payload.append(prompt)
    
    # Use your exact code snippet.
    model = genai.GenerativeModel(model_name="gemini-2.0-flash")
    response = model.generate_content(payload)
    return response.text

@app.get("/", response_class=HTMLResponse)
async def index(request: Request):
    return templates.TemplateResponse("index.html", {"request": request})

@app.post("/upload", response_class=JSONResponse)
async def upload_images(files: List[UploadFile] = File(...)):
    if not files:
        raise HTTPException(status_code=400, detail="No files uploaded.")
    images = []
    for file in files:
        if not file.content_type.startswith("image/"):
            raise HTTPException(status_code=400, detail="Only image files are accepted.")
        image_bytes = await file.read()
        images.append((file.content_type, image_bytes))
    try:
        result = generate_content_from_images(images)
        return {"result": result}
    except Exception as e:
        return JSONResponse(status_code=500, content={"detail": f"Error processing the image(s): {str(e)}"})

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