app.py

from fastapi import FastAPI, File, UploadFile
from transformers import AutoImageProcessor, AutoModelForImageClassification
from PIL import Image
import torch, io, os, uvicorn

app = FastAPI()

MODEL_NAME = "yangy50/garbage-classification"

processor = AutoImageProcessor.from_pretrained(MODEL_NAME)
model = AutoModelForImageClassification.from_pretrained(MODEL_NAME)
model.eval()

@app.get("/")
def root():
    return {"status": "ok"}

@app.post("/predict")
async def predict(file: UploadFile = File(...)):
    image = Image.open(io.BytesIO(await file.read())).convert("RGB")
    inputs = processor(images=image, return_tensors="pt")

    with torch.no_grad():
        outputs = model(**inputs)

    probs = torch.softmax(outputs.logits, dim=1)[0]
    return {
        model.config.id2label[i]: float(probs[i])
        for i in range(len(probs))
    }

if __name__ == "__main__":
    uvicorn.run(app, host="0.0.0.0", port=int(os.environ.get("PORT", 7860)))