rest.py

import numpy as np
import pandas as pd
from tensorflow import keras
import tensorflow as tf

fashion_mnist = keras.datasets.fashion_mnist
(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()

# scale the values to 0.0 to 1.0
train_images = train_images / 255.0
test_images = test_images / 255.0

# reshape for feeding into the model
train_images = train_images.reshape(train_images.shape[0], 28, 28, 1)
test_images = test_images.reshape(test_images.shape[0], 28, 28, 1)

import json
data = json.dumps({"signature_name": "serving_default", "instances": test_images[0:3].tolist()})
print('Data: {} ... {}'.format(data[:50], data[len(data)-52:]))

import requests
headers = {"content-type": "application/json"}
json_response = requests.get('http://localhost:8501/v1/models/mnist/metadata')
print(json_response)
print(json_response.text)
json_response = requests.post('http://localhost:8501/v1/models/mnist:predict', data=data, headers=headers)
print(json_response)
print(json_response.text)
# predictions = json.loads(json_response.text)['predictions']