How can I return a NumPy array using FastAPI?

Question

I have a TensorFlow Keras deep learning model in the form of an h5 file.

How can I upload an image and return a NumPy array in FastAPI?

import numpy as np
import cv2
from fastapi import FastAPI, File, UploadFile
import numpy as np
from tensorflow.keras.models import load_model
import tensorflow as tf

model=load_model("complete_model.h5")
app = FastAPI()

def prepare(image):
    IMG_SIZE = 224
    new_array = cv2.resize(image, (IMG_SIZE, IMG_SIZE))
    return new_array.reshape(-1, IMG_SIZE,IMG_SIZE,3)

@app.post("/")
async def root(file: UploadFile = File(...)):
    global model
    content = await file.read()
    nparr = np.fromstring(content, np.uint8)
    img = cv2.imdecode(nparr, cv2.IMREAD_COLOR).astype(np.float32)
    prediction = model.predict(prepare(img))
    return prediction

When uploading the image using Swagger UI, I get the following error:

line 137, in jsonable_encoder
data = dict(obj)
TypeError: 'numpy.float32' object is not iterable

Working code without FastAPI:

import numpy as np
import numpy as np
from tensorflow.keras.models import load_model
import tensorflow as tf
import cv2

model=load_model("complete_model.h5")

def prepare(image):
    IMG_SIZE = 224
    new_array = cv2.resize(image, (IMG_SIZE, IMG_SIZE))
    return new_array.reshape(-1, IMG_SIZE,IMG_SIZE,3)

img = cv2.imread("./test.jpeg").astype(np.float32)
prediction = model.predict(prepare(img))
print(prediction)

Result in the terminal:

[[0.25442022 0.74557984]]

How can I get the same result while using FastAPI?

Answer 1

The error is thrown when returning the response (i.e., prediction in your case) from your endpoint. It looks like FastAPI is trying to convert the NumPy array to a dict, using the jsonable_encoder, as shown in the error you provided here (have a look at the discussion here, as well as the documentation). Thus, what you could do is to convert the NumPy array into a Python list and then serialise it into a JSON object:

return json.dumps(prediction.tolist())

On OpenAPI (Swagger UI), you will still be able to see the expected result. However, if you need to convert it back to a NumPy array, you can parse the JSON string as shown below.

arr = np.asarray(json.loads(resp.json()))  # resp.json() if using Python requests

If you would like to return the NumPy array as raw bytes and display the image in the browser or download it, have a look at this answer.