app.py

from fastapi import FastAPI, UploadFile, File
from fastapi.responses import JSONResponse
from PIL import Image
import torch, torchvision.transforms as T
from transformers import MobileNetV2ForSemanticSegmentation
import io

# Load the model
model = MobileNetV2ForSemanticSegmentation.from_pretrained("seg_model")
model.eval()

preprocess = T.Compose([
    T.Resize(513),
    T.ToTensor(),
    T.Normalize(mean=[0.485,0.456,0.406], std=[0.229,0.224,0.225])
])

app = FastAPI()

@app.get("/")
def root():
    return {"status": "API up for segmentation"}

@app.post("/predict")
async def predict(file: UploadFile = File(...)):
    img = Image.open(await file.read()).convert("RGB")
    x = preprocess(img).unsqueeze(0)
    with torch.no_grad():
        outputs = model(x).logits
        seg = outputs.argmax(1)[0].tolist()
    return JSONResponse(content={"segmentation_mask": seg})