Create app.py

app.py

@@ -0,0 +1,64 @@
+import gradio as gr
+import numpy as np
+import tensorflow as tf
+from sklearn.neighbors import KNeighborsClassifier
+from sklearn.tree import DecisionTreeClassifier
+from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier, GradientBoostingClassifier
+import joblib
+import pickle
+
+def fashion_MNIST_prediction(test_image, model='KNN'):
+    test_image_flatten = test_image.reshape((-1, 28*28))
+    fashion_mnist = tf.keras.datasets.fashion_mnist
+    (X_train, y_train), (X_test, y_test) = fashion_mnist.load_data()
+    class_names = ("T-shirt/top", "Trouser", "Pullover", "Dress", "Coat", "Sandal", "Shirt", "Sneaker", "Bag", "Ankle boot")
+    img_shape = X_train.shape
+    n_samples = img_shape[0]
+    width = img_shape[1]
+    height = img_shape[2]
+    x_train_flatten = X_train.reshape(n_samples, width*height)
+    
+    if model == 'KNN':
+        with open('knn_model.pkl', 'rb') as f:
+            knn = pickle.load(f)
+        ans = knn.predict(test_image_flatten)
+        ans_prediction = knn.predict_proba(test_image_flatten)
+        return class_names[ans[0]], dict(zip(class_names, map(float, ans_prediction[0])))
+    
+    elif model == 'DecisionTreeClassifier':
+        tree_model = joblib.load('tree_model.joblib')
+        ans = tree_model.predict(test_image_flatten)
+        ans_prediction = tree_model.predict_proba(test_image_flatten)
+        return class_names[ans[0]], dict(zip(class_names, map(float, ans_prediction[0])))
+    
+    elif model == 'RandomForestClassifier':
+        best_model = joblib.load('best_model.pkl')
+        ans = best_model.predict(test_image_flatten)
+        ans_prediction = best_model.predict_proba(test_image_flatten)
+        return class_names[ans[0]], dict(zip(class_names, map(float, ans_prediction[0])))
+
+    elif model == 'AdaBoostClassifier':
+        best_estimator = joblib.load('best_adaboost_model.joblib')
+        ans = best_estimator.predict(test_image_flatten)
+        ans_prediction = best_estimator.predict_proba(test_image_flatten)
+        return class_names[ans[0]], dict(zip(class_names, map(float, ans_prediction[0])))
+
+    elif model == 'GradientBoostingClassifier':
+        best_estimator = joblib.load('best_gbc_model.joblib')
+        ans = best_estimator.predict(test_image_flatten)
+        ans_prediction = best_estimator.predict_proba(test_image_flatten)
+        return class_names[ans[0]], dict(zip(class_names, map(float, ans_prediction[0])))
+
+    else:
+        return "Invalid Model Selection"
+    
+input_image = gr.inputs.Image(shape=(28, 28), image_mode='L')
+input_model = gr.inputs.Dropdown(['KNN', 'DecisionTreeClassifier', 'RandomForestClassifier', 'AdaBoostClassifier', 'GradientBoostingClassifier'])
+
+output_label = gr.outputs.Textbox(label="Predicted Label")
+output_probability = gr.outputs.Label(num_top_classes=10, label="Predicted Probability Per Class")
+
+gr.Interface(fn=fashion_MNIST_prediction, 
+             inputs=[input_image, input_model], 
+             outputs=[output_label, output_probability],
+             title="Fashion MNIST classification").launch(debug=True)