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from json import load |
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import gradio as gr |
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import tensorflow as tf |
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import numpy as np |
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input_module1 = gr.Image(label = "test_image", image_mode='L') |
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input_module2 = gr.Dropdown(choices=['KNN', 'Softmax'], label = "Select Algorithm") |
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output_module1 = gr.Textbox(label = "Predicted Class") |
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output_module2 = gr.Label(label = "Predict Probability") |
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def fashion_images(input1, input2): |
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from PIL import Image |
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img_pil = Image.fromarray(input1) |
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img_28x28 = np.array(img_pil.resize((28, 28), Image.ANTIALIAS)) |
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numpy_image = img_28x28.reshape(1, 28*28) |
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print("numpy_image: ", numpy_image.shape) |
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if input2 == "KNN": |
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model = joblib.load('best_knn_model.joblib') |
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else: |
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model = joblib.load('best_logistic_model.joblib') |
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out = model.predict(numpy_image)[0] |
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out_prob = model.predict_proba(numpy_image)[0] |
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print("out: ",out) |
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print("out_prob: ",out_prob.shape) |
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class_names = ["T-shirt/top", "Trouser", "Pullover", "Dress", "Coat", "Sandal", "Shirt", "Sneaker", "Bag", "Ankle boot"] |
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final = class_names[out] |
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class_prob = { class_names[i]:prob for i, prob in enumerate(out_prob)} |
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return final, class_prob |
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gr.Interface(fn=fashion_images, inputs=[input_module1, input_module2], outputs=[output_module1,output_module2]).launch(debug=True) |
