Delete app.py

app.py

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-## CSCI4750/5750: Demo 1
-## load the dataset 
-def demo1_derive_MNIST_train_test_data():
-    from sklearn.datasets import fetch_openml
-    import numpy as np
-    mnist = fetch_openml('mnist_784', version=1, as_frame=False)
-    X, y = mnist["data"], mnist["target"]
-    X_train, X_test, y_train, y_test = X[:60000], X[60000:], y[:60000], y[60000:]
-    y_train = y_train.astype(np.int) # convert to int
-    y_test = y_test.astype(np.int) # convert to int
-    return X_train, X_test, y_train, y_test
-
-X_train, X_test, y_train, y_test = demo1_derive_MNIST_train_test_data()
-print("X_train.shape: ", X_train.shape)
-print("X_test.shape: ", X_test.shape)
-print("y_train.shape: ", y_train.shape)
-print("y_test.shape: ", y_test.shape)
-
-train_features = X_train
-train_labels = y_train
-test_feature = X_test[0]
-K = 3
-print("train_features: ",train_features.shape)
-print("train_labels: ",train_labels.shape)
-print("test_feature: ",test_feature.shape)
-
-# Practice 5: deploy our KNN classifier to web application, with multiple outputs
-
-import scipy
-import gradio as gr
-import numpy as np
-import cv2
-import os
-
-def get_sample_images(num_images):
-    sample_images = []
-    for i in range(num_images):
-      test_feature = X_test[i]
-      test_feature_2d =test_feature.reshape(28,28)
-    
-      # Make it unsigned integers:
-      data = test_feature_2d.astype(np.uint8)
-
-      outdir =  "images_folder"
-      img_path = os.path.join(outdir, 'local_%05d.png' % (i,))
-      if not os.path.exists(outdir):
-         os.mkdir(outdir)
-      cv2.imwrite(img_path, data)
-
-      sample_images.append([img_path,int(np.random.choice([7,9,11,13,15,24]))])   # ["image path", "K"]
-    return sample_images
-
-# EXTRA: adapted from https://github.com/ageron/handson-ml2/blob/master/03_classification.ipynb
-def plot_digits(instances, images_per_row=3):
-    import matplotlib.pyplot as plt
-    import matplotlib as mpl
-    size = 28
-    images_per_row = min(len(instances), images_per_row)
-    # This is equivalent to n_rows = ceil(len(instances) / images_per_row):
-    n_rows = (len(instances) - 1) // images_per_row + 1
-
-    n = len(instances)
-
-    fig = plt.figure(figsize=(15,8))
-    for i in range(len(instances)):
-        # Debug, plot figure
-        fig.add_subplot(n_rows, images_per_row, i + 1)
-        #print(instances[i])
-        plt.imshow(instances[i].reshape(size,size), cmap = mpl.cm.binary)
-        plt.axis("off")
-        plt.title("Neighbor "+str(i+1), size=20)
-    fig.tight_layout()
-
-    plt.savefig('results.png', dpi=300)
-    return 'results.png'
-
-    
-## machine learning classifier
-def KNN_predict(train_features, train_labels, test_feature, K):
-  label_record = []
-  for i in range(len(train_features)):
-    train_point_feature = train_features[i]
-    test_point_feature = test_feature
-    ### (1) calculate distance between test feature and each of training data points
-
-    # get distance for data point i 
-    dis = scipy.spatial.distance.euclidean(train_point_feature, test_point_feature)
-    
-    # collect lable for datapoint i 
-    y = train_labels[i]
-    label_record.append((dis, y, train_point_feature))
-  
-  # sort data points by distance 
-  from operator import itemgetter
-  sorted_labels = sorted(label_record,key=itemgetter(0))
-  # get major class from top K neighbors 
-  major_class = []
-  neighbor_imgs = []
-  for k in range(K):
-    major_class.append(sorted_labels[k][1])
-
-    # at most 24 neighbors for visualization
-    if k <24:
-      neighbor_feature = sorted_labels[k][2]
-      neighbor_imgs.append(neighbor_feature)
-  
-  ### get final prediction
-  final_prediction = scipy.stats.mode(major_class).mode[0]
-
-  ### get neighbor images and save to local 
-  neighbor_imgs =np.array(neighbor_imgs) 
-  image_path = plot_digits(neighbor_imgs, images_per_row=6)
-
-  return final_prediction, image_path
-
-### main function for gradio to call to classify image
-def call_our_KNN(test_image, K=7):
-  test_image_flatten = test_image.reshape((-1, 28*28))
-  y_pred_each, image_path = KNN_predict(train_features, train_labels, test_image_flatten, int(K))
-  return y_pred_each, image_path
-
-
-### generate several example cases
-sample_images = get_sample_images(10)
-
-### configure inputs/outputs
-set_image = gr.inputs.Image(shape=(28, 28), image_mode='L')
-set_K = gr.inputs.Slider(1, 24, step=1, default=7)
-
-set_label = gr.outputs.Textbox(label="Predicted Digit")
-set_out_images = gr.outputs.Image(label="Closest Neighbors")
-
-
-### configure gradio, detailed can be found at https://www.gradio.app/docs/#i_slider
-interface = gr.Interface(fn=call_our_KNN, 
-                         inputs=[set_image, set_K], 
-                         outputs=[set_label,set_out_images],
-                         examples_per_page = 2,
-                         examples = sample_images, 
-                         title="CSCI4750/5750 Demo 1: Digit classification using KNN algorithm", 
-                         description= "Click examples below for a quick demo",
-                         theme = 'huggingface',
-                         layout = 'vertical'
-                         )
-interface.launch(debug=True)