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app-4.py

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+import os
+import keras
+from keras.applications import inception_v3 as inc_net
+from keras.preprocessing import image
+from skimage.segmentation import mark_boundaries
+import numpy as np
+import matplotlib.pyplot as plt
+import gradio as gr
+from lime import lime_image
+
+# Load the pre-trained InceptionV3 model
+inet_model = inc_net.InceptionV3()
+
+def transform_img_fn(img_path):
+    """Preprocess image for InceptionV3"""
+    img = image.load_img(img_path, target_size=(299, 299))
+    x = image.img_to_array(img)
+    x = np.expand_dims(x, axis=0)
+    return inc_net.preprocess_input(x)
+
+def explain_image(img_path):
+    """Generate LIME explanation and visualization"""
+    # Preprocess image
+    processed_img = transform_img_fn(img_path)
+    
+    # Create LIME explainer
+    explainer = lime_image.LimeImageExplainer()
+    
+    # Generate explanation
+    explanation = explainer.explain_instance(
+        processed_img[0].astype('double'), 
+        inet_model.predict, 
+        top_labels=5, 
+        hide_color=0, 
+        num_samples=1000
+    )
+    
+    # Get image and mask
+    temp, mask = explanation.get_image_and_mask(
+        explanation.top_labels[0],
+        positive_only=False,
+        num_features=10,
+        hide_rest=False
+    )
+    
+    # Get top 5 predictions
+    predictions = inet_model.predict(processed_img)
+    top_5_indices = np.argsort(predictions[0])[-5:][::-1]
+    top_5_labels = [inc_net.decode_predictions(predictions, top=5)[0][i][1] for i in range(5)]
+    top_5_probs = [inc_net.decode_predictions(predictions, top=5)[0][i][2] for i in range(5)]
+    
+    # Create visualization
+    fig, ax = plt.subplots(figsize=(6, 6))
+    
+    # Explanation visualization
+    ax.imshow(mark_boundaries(temp / 2 + 0.5, mask))
+    ax.set_title('Pros (Green) vs Cons (Red)')
+    ax.axis('off')
+    
+    plt.tight_layout()
+    
+    # Create a string for the top 5 predictions
+    predictions_str = "Top 5 Predictions:\n"
+    for i, (label, prob) in enumerate(zip(top_5_labels, top_5_probs)):
+        predictions_str += f"{i+1}. {label}: {prob:.4f}\n"
+    
+    # Generate heatmap
+    ind = explanation.top_labels[0]
+    dict_heatmap = dict(explanation.local_exp[ind])
+    heatmap = np.vectorize(dict_heatmap.get)(explanation.segments)
+    
+    # Plot heatmap
+    fig_heatmap, ax_heatmap = plt.subplots(figsize=(6, 6))
+    heatmap_plot = ax_heatmap.imshow(heatmap, cmap='RdBu', vmin=-heatmap.max(), vmax=heatmap.max())
+    plt.colorbar(heatmap_plot, ax=ax_heatmap)
+    ax_heatmap.set_title('Heatmap Explanation')
+    ax_heatmap.axis('off')
+    
+    plt.tight_layout()
+    
+    return fig, predictions_str, fig_heatmap
+
+# Create Gradio interface
+demo = gr.Interface(
+    fn=explain_image,
+    inputs=gr.Image(type="filepath", label="Input Image"),
+    outputs=[
+        gr.Plot(label="Explanation"),
+        gr.Textbox(label="Top 5 Predictions"),
+        gr.Plot(label="Heatmap Explanation")
+    ],
+    title="LIME Image Classifier Explainer",
+    description="Upload an image to see which areas positively (green) and negatively (red) influence the classification, the top 5 predictions, and a heatmap explanation."
+)
+
+# Launch the app
+if __name__ == "__main__":
+    demo.launch()

requirements-3.txt

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+gradio
+keras
+tensorflow
+lime
+numpy
+matplotlib
+scikit-image