Making a TensorFlow Serving POST request to the REST client API

tensorflow_serving_request.py

import json, requests
from tensorflow.keras.preprocessing.image import img_to_array, load_img
import numpy as np

image_path = 'sunflower.jpg'
# Loading and pre-processing our input image
img = image.img_to_array(image.load_img(image_path, target_size=(128, 128))) / 255.
img = np.expand_dims(img, axis=0)
payload = {"instances": img.tolist()}

# sending post request to TensorFlow Serving server
json_response = requests.post('http://localhost:9000/v1/models/FlowerClassifier:predict', json=payload)
pred = json.loads(json_response.content.decode('utf-8'))

# Decoding the response using decode_predictions() helper function
# You can pass "k=5" to get top 5 predicitons
get_top_k_predictions(pred, k=3)