Spylif/FastAPI-Vector-Model

1from fastapi import FastAPI, Request2from pydantic import BaseModel3from transformers import CLIPProcessor, CLIPModel4from PIL import Image5import torch6import requests7from io import BytesIO8 9app = FastAPI()10 11# use TensorFlow format -- Akash 22 april 2512model = CLIPModel.from_pretrained("laion/CLIP-ViT-B-32-laion2B-s34B-b79K")13processor = CLIPProcessor.from_pretrained("laion/CLIP-ViT-B-32-laion2B-s34B-b79K")14 15 16class ImageInput(BaseModel):17    image_url: str18 19@app.post("/vector")20def vectorize_image(data: ImageInput):21    try:22        response = requests.get(data.image_url)23        image = Image.open(BytesIO(response.content)).convert("RGB")24 25        inputs = processor(images=image, return_tensors="pt")26        with torch.no_grad():27            features = model.get_image_features(**inputs)28        29        return {"vector": features[0].tolist()}30    except Exception as e:31        return {"error": str(e)}32