rm: redundant files

api_client.py

@@ -1,70 +0,0 @@
-import os
-import time
-import numpy as np
-import requests
-import matplotlib.pyplot as plt
-
-# Disable tensorflow warnings
-os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
-
-from keras.datasets import mnist
-from typing import List
-
-
-# Set random seed for reproducibility
-np.random.seed(50)
-
-# Number of images taken from test dataset to make prediction
-N_IMAGES = 9
-
-def get_image_prediction(image: List):
-    """Get Model prediction for a given image
-    :param
-        image: List
-            Grayscale Image
-    :return: Json
-        HTTP Response format:
-            {
-                "prediction": predicted_label,
-                "ml-latency-ms": latency_in_milliseconds
-                    (Measures time only for ML operations preprocessing with predict)
-        }
-    """
-    # Making prediction request API
-    response = requests.post(url='http://127.0.0.1:5000/predict', json={'image': image})
-    # Parse the response JSON
-    return response.json()
-
-# Load the dataset from keras.datasets
-(x_train, y_train), (x_test, y_test) = mnist.load_data()
-
-# Select N-th (N_IMAGES) random indices from x_test
-indices = np.random.choice(len(x_test), N_IMAGES, replace=False)
-
-# Get the images and labels based on the selected indices
-images, labels, predictions = x_test[indices], y_test[indices], []
-
-# Iterate over each image, invoke prediction API and save results to predictions array
-for i in range(N_IMAGES):
-    # Send the POST request to the Flask server
-    start_time = time.time()
-    model_response = get_image_prediction(images[i].tolist())
-    print('Model Response:', model_response)
-    print('Total Measured Time (ms):', round((time.time() - start_time) * 1000, 3))
-    # Save prediction data into predictions array
-    predictions.append(model_response['prediction'])
-
-def plot_images_and_results_plot(images_, labels_, predictions_):
-    """Plotting the images with their labels and predictions
-    """
-    fig, axes = plt.subplots(N_IMAGES, 1, figsize=(6, 10))
-
-    for i in range(N_IMAGES):
-        axes[i].imshow(images_[i], cmap='gray')
-        axes[i].axis('off')
-        axes[i].set_title("Label/Prediction: {}/{}".format(labels_[i], predictions_[i]))
-
-    plt.tight_layout()
-    plt.show()
-
-plot_images_and_results_plot(images, labels, predictions)