Create app.py

app.py

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+## CSCI4750/5750: homework03 submission
+## load the dataset 
+def hw03_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 = hw03_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):
+      train_feature = X_train[i]
+      train_feature_2d =train_feature.reshape(28,28)
+    
+      # Make it unsigned integers:
+      data = train_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]))])   # ["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()
+    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))
+    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 15 neighbors for visualization
+    if k <15:
+      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=3)
+
+  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, 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(0, 100, 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: Digit classification using KNN algorithm", 
+                         description= "Click examples below for a quick demo",
+                         theme = 'huggingface',
+                         layout = 'vertical',
+                         live=True
+                         )
+interface.launch(debug=True)