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?
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.