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import gradio as gr |
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import numpy as np |
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from tensorflow.keras.models import load_model |
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from tensorflow.keras.preprocessing.image import load_img, img_to_array |
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import matplotlib.pyplot as plt |
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model_path = './model-0.9980.hdf5' |
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try: |
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model = load_model(model_path) |
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except OSError: |
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print("Error: Could not load the model. Please check the path.") |
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exit() |
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class_labels = { |
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0: 'Stage = I, Situation = Tumor, Tumour Type = Benign, Tumour Class = Adenoma', |
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1: 'Stage = I, Situation = Tumor, Tumour Type = Benign, Tumour Class = Angiomyolipoma', |
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2: 'Stage = I, Situation = Tumor, Tumour Type = Benign, Tumour Class = Angiomyolipoma ', |
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3: 'Stage = I, Situation = Tumor, Tumour Type = Benign, Tumour Class = Angiomyolipoma and Adenomas', |
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4: 'Stage = I, Situation = Tumor, Tumour Type = Benign, Tumour Class = Lipomas', |
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5: 'Stage = I, Situation = Tumor, Tumour Type = Malignant, Tumour Class = RCC', |
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6: 'Stage = I, Situation = Tumor, Tumour Type = Malignant, Tumour Class = RCC ', |
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7: 'Stage = I, Situation = Tumor, Tumour Type = Malignant, Tumour Class = Secondary', |
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8: 'Stage = II, Situation = Tumor, Tumour Type = Malignant, Tumour Class = RCC', |
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9: 'Stage = II, Situation = Tumor, Tumour Type = Malignant, Tumour Class = Secondary', |
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10: 'Stage = III, Situation = Tumor, Tumour Type = Malignant, Tumour Class = RCC', |
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11: 'Stage = IV, Situation = Tumor, Tumour Type = Malignant, Tumour Class = Secondary', |
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12: 'Stage = Null, Situation = Normal case with cyst, Tumour Type = Null, Tumour Class = Null', |
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13: 'Stage = Null, Situation = Normal case, Tumour Type = Null, Tumour Class = Null' |
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} |
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def predict_class(image): |
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img_array = img_to_array(image) |
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img_array = np.expand_dims(img_array, axis=0) |
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img_array = img_array / 255.0 |
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predictions = model.predict(img_array) |
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predicted_class_indices = np.argsort(predictions[0])[::-1][:5] |
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label_confidences = { |
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class_labels[idx]: float(predictions[0][idx]) |
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for idx in predicted_class_indices |
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} |
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top_predicted_class = max(label_confidences, key=label_confidences.get) |
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top_probability = label_confidences[top_predicted_class] |
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top_predicted_class = top_predicted_class.replace(","," :: ") |
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return top_predicted_class, top_probability |
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input = gr.Image(label="Upload Kidney CT Image") |
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output = [gr.Label(label="Top Predicted Class", type="text"), gr.Label(label="Probabilities", type="text")] |
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interface = gr.Interface( |
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fn=predict_class, |
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inputs=gr.Image(shape=(256,256),label="Upload Kidney CT Image"), |
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outputs=[gr.Label(label="Top Predicted Class", type="text"), gr.Label(label="Probabilities", type="text")], |
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title="ResNext-Powered Kidney Tumour Detection", |
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description="Upload a CT image to classify the tumor and get the top predicted class with its probabilities.", |
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) |
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interface.launch() |
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