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d22cebd 5bea4b9 c5cba2b d22cebd a6779c6 d22cebd 5bea4b9 d22cebd 5bea4b9 c5cba2b a6779c6 d22cebd 57bfe77 d22cebd 5bea4b9 d22cebd 5bea4b9 d22cebd c5cba2b a6779c6 c5cba2b a6779c6 c5cba2b a6779c6 dc11fe9 a6779c6 d22cebd c5cba2b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 | from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
from transformers import AutoImageProcessor, AutoModel
import torch
from PIL import Image
import requests
from io import BytesIO
import uvicorn
app = FastAPI(title="Movie Linker - Image Embedding API (DINOv2)")
class ImageRequest(BaseModel):
image_url: str
# Load Model
img_model_id = 'facebook/dinov2-base'
img_processor = AutoImageProcessor.from_pretrained(img_model_id)
img_model = AutoModel.from_pretrained(img_model_id)
img_model.eval()
@app.get("/")
def home():
return {"status": "online", "model": img_model_id, "endpoint": "/embed/image"}
@app.post("/embed/image")
async def embed_image(request: ImageRequest):
try:
response = requests.get(request.image_url, timeout=10)
img = Image.open(BytesIO(response.content)).convert("RGB")
# Process image
inputs = img_processor(images=img, return_tensors="pt")
with torch.no_grad():
outputs = img_model(**inputs)
# Use CLS token for global representation (768 dimensions)
embedding = outputs.last_hidden_state[:, 0, :].squeeze().tolist()
return {
"success": True,
"image_url": request.image_url,
"model": img_model_id,
"dimension": len(embedding),
"embedding": embedding
}
except Exception as e:
raise HTTPException(status_code=400, detail=str(e))
if __name__ == "__main__":
uvicorn.run(app, host="0.0.0.0", port=7860)
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