fixes

app.py

@@ -1,4 +1,5 @@
 from io import BytesIO
+from tempfile import NamedTemporaryFile
 
 from fastapi import FastAPI, Response, status, UploadFile
 from torchvision.io import read_image
@@ -24,20 +25,21 @@ async def healthcheck():
     return Response(status_code=status.HTTP_200_OK)
 
 
-@app.post('/detectObjectsFromURL')
+@app.post('/detectObjects')
 async def infer(image: UploadFile):
-    img = read_image(image.filename)
+    with NamedTemporaryFile() as f:
+        f.write(image.file.read())
+        f.seek(0)
+        img = read_image(f.name)
 
     batch = [preprocess(img)]
 
-    # Step 4: Use the model and visualize the prediction
     prediction = model(batch)[0]
     labels = [weights.meta["categories"][i] for i in prediction["labels"]]
     box = draw_bounding_boxes(img, boxes=prediction["boxes"],
                               labels=labels,
                               colors="red",
                               width=4, font_size=30)
-    im: Image.Image
     im = to_pil_image(box.detach())
     with BytesIO() as bio:
         im.save(bio, format='PNG')