`async def __call__(self, request):
image_payload_bytes = request.files['file']
# image_payload_bytes = await request.body()
pil_image = Image.open(BytesIO(image_payload_bytes))
pil_images = [pil_image] # Our current batch size is one
input_tensor = torch.cat([self.preprocessor(i).unsqueeze(0) for i in pil_images])
with torch.no_grad():
output_tensor = self.model(input_tensor)
return {"class_index": int(torch.argmax(output_tensor[0]))}`
resp = requests.post(f"{parameters.u}:{parameters.p}/image_predict", files={"file": open(parameters.d, 'rb')}) print("Inference done!\n", resp.text)
It shows Internal Server Error .
But if I use requests.post(data = open(parameters.d, ‘rb’)), it worked.
Now I want to find a way to upload a batch of images to the server and predict them.
Please help me.