| 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) |