.ipynb_checkpoints/app-checkpoint.py

from fastapi import FastAPI, File, UploadFile, HTTPException
from fastapi.middleware.cors import CORSMiddleware
from PIL import Image, UnidentifiedImageError
from transformers import AutoProcessor, Blip2ForConditionalGeneration
import torch
import io

app = FastAPI()

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

# Load the model and processor
try:
    model = Blip2ForConditionalGeneration.from_pretrained("ybelkada/blip2-opt-2.7b-fp16-sharded")
    model.load_adapter('blip-cpu-model')
    processor = AutoProcessor.from_pretrained("Salesforce/blip2-opt-2.7b")
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
except Exception as e:
    raise RuntimeError(f"Failed to load the model or processor: {str(e)}")

@app.post("/generate-caption/")
async def generate_caption(file: UploadFile = File(...)):
    try:
        image = Image.open(io.BytesIO(await file.read()))
    except UnidentifiedImageError:
        # Raise a 400 error if the file is not a valid image
        raise HTTPException(status_code=400, detail="Uploaded file is not a valid image.")
    except Exception as e:
        # Catch any other unexpected errors related to image processing
        raise HTTPException(status_code=500, detail=f"An unexpected error occurred while processing the image: {str(e)}")    
    
    try:
        inputs = processor(images=image, return_tensors="pt").to(device, torch.float16)

        with torch.no_grad():
            caption_ids = model.generate(**inputs, max_length=128)
            caption = processor.decode(caption_ids[0], skip_special_tokens=True)

        return {"caption": caption}
    except Exception as e:
        # Catch any errors during the caption generation process
        raise HTTPException(status_code=500, detail=f"An error occurred while generating the caption: {str(e)}")