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app.py

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+import torch
+from transformers import ViTForImageClassification, ViTFeatureExtractor
+from PIL import Image
+import gradio as gr
+import numpy as np
+import matplotlib.pyplot as plt
+
+# Load the pretrained Vision Transformer model and feature extractor
+model_name = "google/vit-base-patch16-224"
+model = ViTForImageClassification.from_pretrained(model_name)
+feature_extractor = ViTFeatureExtractor.from_pretrained(model_name)
+model.eval()
+
+# Function to apply Grad-CAM visualization
+def generate_grad_cam(image, target_layer):
+    # Preprocess the image
+    inputs = feature_extractor(images=image, return_tensors="pt")
+    input_tensor = inputs['pixel_values']
+    
+    # Forward pass to get logits
+    input_tensor.requires_grad = True
+    outputs = model(input_tensor)
+    
+    # Get the target score
+    score = outputs.logits[0].max()
+    
+    # Backpropagate to get gradients
+    model.zero_grad()
+    score.backward()
+    
+    # Get the gradients and activations from the target layer
+    gradients = model.get_input_embeddings().weight.grad
+    activations = model.get_input_embeddings().weight.data
+    
+    # Calculate Grad-CAM
+    pooled_gradients = torch.mean(gradients, dim=[0, 2, 3])
+    for i in range(activations.size(1)):
+        activations[:, i, :, :] *= pooled_gradients[i]
+    
+    heatmap = torch.mean(activations, dim=1).squeeze()
+    heatmap = np.maximum(heatmap.detach().numpy(), 0)
+    heatmap = heatmap / np.max(heatmap)
+    
+    return heatmap
+
+# Prediction and Grad-CAM function
+def predict_and_explain(image):
+    # Predict the class
+    inputs = feature_extractor(images=image, return_tensors="pt")
+    with torch.no_grad():
+        outputs = model(**inputs)
+    
+    logits = outputs.logits
+    predicted_class_idx = logits.argmax(-1).item()
+    
+    # Predefined medical conditions (adjust based on your dataset)
+    labels = ["Class 1 - Normal", "Class 2 - Condition A", "Class 3 - Condition B"]
+    predicted_label = labels[predicted_class_idx]
+    
+    # Generate Grad-CAM heatmap
+    heatmap = generate_grad_cam(image, target_layer="vit.encoder.layer.11.output")
+    
+    # Visualize the heatmap on the original image
+    img = np.array(image)
+    heatmap_resized = np.array(Image.fromarray(heatmap).resize((img.shape[1], img.shape[0])))
+    
+    # Overlay heatmap on the original image
+    plt.imshow(img)
+    plt.imshow(heatmap_resized, cmap='jet', alpha=0.5)
+    plt.axis('off')
+    
+    # Save the overlayed image
+    plt.savefig("grad_cam_result.png")
+    
+    return predicted_label, "grad_cam_result.png"
+
+# Gradio interface
+interface = gr.Interface(
+    fn=predict_and_explain,
+    inputs=gr.inputs.Image(type="pil"),
+    outputs=[
+        "text",
+        gr.outputs.Image(type="file", label="Grad-CAM Visualization")
+    ],
+    title="Medical Image Analysis Tool with Explainability",
+    description="Upload an X-ray or MRI image to get a prediction for a medical condition with explainability through Grad-CAM.",
+    live=True
+)
+
+# Launch the app
+if __name__ == "__main__":
+    interface.launch()

requirements.txt

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+torch
+transformers
+gradio
+numpy
+pillow
+matplotlib