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import gradio as gr |
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from tensorflow.keras.models import load_model |
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import numpy as np |
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from PIL import Image |
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model = load_model('xray_image_classifier_model.keras') |
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def predict(image): |
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img = image.resize((150, 150)) |
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img_array = np.array(img) / 255.0 |
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img_array = np.expand_dims(img_array, axis=0) |
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prediction = model.predict(img_array) |
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predicted_class = 'Pneumonia' if prediction > 0.5 else 'Normal' |
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return predicted_class |
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css = """ |
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.gradio-container { |
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background-color: #f5f5f5; |
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font-family: Arial, sans-serif; |
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} |
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.gr-button { |
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background-color: #007bff; |
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color: white; |
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border-radius: 5px; |
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font-size: 16px; |
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} |
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.gr-button:hover { |
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background-color: #0056b3; |
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} |
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.gr-textbox, .gr-image { |
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border: 2px dashed #007bff; |
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padding: 20px; |
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border-radius: 10px; |
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background-color: #ffffff; |
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} |
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.gr-box-text { |
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color: #007bff; |
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font-size: 22px; |
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font-weight: bold; |
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text-align: center; |
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} |
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h1 { |
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font-size: 36px; |
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color: #007bff; |
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text-align: center; |
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} |
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p { |
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font-size: 20px; |
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color: #333; |
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text-align: center; |
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} |
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""" |
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with gr.Blocks(css=css) as interface: |
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gr.Markdown("<h1>Chest X-ray Pneumonia Classifier</h1>") |
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gr.Markdown("<p>Upload an X-ray image to classify it as 'Pneumonia' or 'Normal'.</p>") |
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with gr.Row(): |
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image_input = gr.Image(label="Drop Image Here", type="pil", elem_classes=["gr-image", "gr-box-text"]) |
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output = gr.Textbox(label="Prediction", elem_classes=["gr-textbox", "gr-box-text"]) |
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submit_btn = gr.Button("Classify X-ray", elem_classes=["gr-button"]) |
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submit_btn.click(fn=predict, inputs=image_input, outputs=output) |
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interface.launch() |
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