init!

app.py

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+#Web application (sample codes from student)
+import matplotlib as mpl
+import matplotlib.pyplot as plt
+import gradio as gr
+import pickle
+import tensorflow as tf
+from tensorflow import keras
+from sklearn.neighbors import KNeighborsClassifier
+
+#Step 1
+fashion_mnist = keras.datasets.fashion_mnist
+(X_train_full, y_train_full), (X_test, y_test) = fashion_mnist.load_data()
+
+class_names = ["T-shirt/top", "Trouser", "Pullover", "Dress", "Coat",
+               "Sandal", "Shirt", "Sneaker", "Bag", "Ankle boot"]
+
+%matplotlib inline
+import numpy as np
+import matplotlib as mpl
+import matplotlib.pyplot as plt
+
+
+# recommended way to save data into image
+from PIL import Image
+for i in range(6):
+  idx = np.random.randint(0,len(X_train_full))
+  some_image = X_train_full[idx] # select one image sample
+  some_image_label = class_names[y_train_full[idx]] # select one image sample
+  some_image = some_image.reshape(28, 28) # reshape from rank-1 tensor (784,) to rank-2 tensor (28,28) 
+  im = Image.fromarray(some_image)
+  im.save('image'+str(i)+'.jpg')
+
+input_module1 = gr.inputs.Image(label = "test_image", image_mode='L', shape = (28,28))
+
+input_module2 = gr.inputs.Dropdown(choices=["Random Forest", "Decision Tree", "AdaBoost", "Gradient Tree Boosting", "KNN"], label = "Select Algorithm")
+
+output_module1 = gr.outputs.Textbox(label = "Predicted Class")
+
+output_module2 = gr.outputs.Label(label = "Predict Probability")
+
+
+def fashion_images(input1, input2):
+    image = input1.reshape(1, 28*28)/255.0
+
+    import pickle
+    with open('knn_model_best.pkl', 'rb') as file:
+        best_knn_model = pickle.load(file)
+
+    with open('gradientboost_model_best.pkl', 'rb') as file:
+        best_gbt_model = pickle.load(file)
+
+    with open('adaboost_model_best.pkl', 'rb') as file:
+        best_adaboost_model = pickle.load(file)
+
+    with open('decision_tree_model_best.pkl', 'rb') as file:
+        best_tree_model = pickle.load(file)
+        
+    with open('random_forest_model_best.pkl', 'rb') as file:
+        best_RF_model = pickle.load(file)
+
+
+    if input2 == 'Random Forest':
+        y_test_predicted_proba = best_RF_model.predict_proba(image)
+        y_test_predicted_label = best_RF_model.predict(image)
+        output = class_names[y_test_predicted_label[0]]
+
+    elif input2 == 'Gradient Tree Boosting':
+        y_test_predicted_proba = best_gbt_model.predict_proba(image)
+        y_test_predicted_label = best_gbt_model.predict(image)
+        output = class_names[y_test_predicted_label[0]]
+        
+    elif input2 == 'AdaBoost':
+        y_test_predicted_proba = best_adaboost_model.predict_proba(image)
+        y_test_predicted_label = best_adaboost_model.predict(image)
+        output = class_names[y_test_predicted_label[0]]
+        
+    elif input2 == 'Decision tree':
+        y_test_predicted_proba = best_tree_model.predict_proba(image)
+        y_test_predicted_label = best_tree_model.predict(image)
+        output = class_names[y_test_predicted_label[0]]
+        
+    elif input2 == 'Random Forest':
+        y_test_predicted_proba = best_RF_model.predict_proba(image)
+        y_test_predicted_label = best_RF_model.predict(image)
+        output = class_names[y_test_predicted_label[0]]
+
+    elif input2 == 'KNN': 
+        y_test_predicted_proba = best_knn_model.predict_proba(image)
+        y_test_predicted_label = best_knn_model.predict(image)
+        output = class_names[y_test_predicted_label[0]]
+    
+    output_prob = {}
+    for name, prob in zip(class_names, y_test_predicted_proba[0]):
+        output_prob[name] = prob
+
+    return output, output_prob
+
+# Step 6.4: Put all three component together into the gradio's interface function 
+interface = gr.Interface(fn=fashion_images, 
+             inputs=[input_module1, input_module2], 
+             outputs=[output_module1,output_module2],
+             examples=[["image0.jpg", "Random Forest"], 
+                       ["image1.jpg", "Decision tree"], 
+                       ["image2.jpg", "KNN"], 
+            )
+interface.launch()