Update app.py

app.py

@@ -5,9 +5,45 @@ import gradio as gr
 from tensorflow.keras.preprocessing.image import load_img, img_to_array
 from tensorflow.keras.applications.densenet import preprocess_input, decode_predictions
 import numpy as np 
+from PIL import Image
 model = load_model('Densenet.h5')
 model.load_weights("pretrained_model.h5")
 class_names = ['Cardiomegaly', 'Emphysema', 'Effusion', 'Hernia', 'Infiltration', 'Mass', 'Nodule', 'Atelectasis', 'Pneumothorax', 'Pleural_Thickening', 'Pneumonia', 'Fibrosis', 'Edema', 'Consolidation']
+def get_gradcam(model, img, layer_name):
+    
+    img_array = img_to_array(img)
+    img_array = np.expand_dims(img_array, axis=0)
+    img_array = preprocess_input(img_array)
+    
+    grad_model = Model(inputs=model.inputs, outputs=[model.get_layer(layer_name).output, model.output])
+
+    with tf.GradientTape() as tape:
+        conv_outputs, predictions = grad_model(img_array)
+        class_idx = tf.argmax(predictions[0])
+
+    output = conv_outputs[0]
+    grads = tape.gradient(predictions, conv_outputs)[0]
+    guided_grads = tf.cast(output > 0, 'float32') * tf.cast(grads > 0, 'float32') * grads
+
+    weights = tf.reduce_mean(guided_grads, axis=(0, 1))
+    cam = tf.reduce_sum(tf.multiply(weights, output), axis=-1)
+    heatmap = np.maximum(cam, 0)
+    heatmap /= tf.reduce_max(heatmap)
+    heatmap_img = plt.cm.jet(heatmap)[..., :3]
+
+    # Load the original image
+    original_img = Image.fromarray(img)
+
+    # Resize the heatmap to match the original image size
+    heatmap_img = Image.fromarray((heatmap_img * 255).astype(np.uint8))
+    heatmap_img = heatmap_img.resize(original_img.size)
+
+    # Overlay the heatmap on the original image
+    overlay_img = Image.blend(original_img, heatmap_img, 0.5)
+
+    # Return the overlayed image
+    return overlay_img
+    
 def custom_decode_predictions(predictions, class_labels):
     
     decoded_predictions = []
@@ -23,18 +59,22 @@ def classify_image(img):
     img_array = img_to_array(img)
     img_array = np.expand_dims(img_array, axis=0)
     img_array = preprocess_input(img_array)
-    predictions = model.predict(img_array)
-    decoded_predictions = custom_decode_predictions(predictions, class_names)
-    return decoded_predictions
 
+
+    predictions1 = model.predict(img_array)
+    decoded_predictions = custom_decode_predictions(predictions1, class_names)
+    overlay_img = get_gradcam(model, img, layer_name)
+
+    # Return the decoded predictions and the overlayed image
+    return decoded_predictions, overlay_img
 # Gradio interface
 iface = gr.Interface(
     fn=classify_image, 
     inputs="image", 
-    outputs="text", 
-    title="Xray Classification - KIMS",
-    description="Classify cxr into one of 20 classes - Atelectasis, Cardiomegaly, Consolidation, Edema, Effusion, Emphysema, Fibrosis, Hernia, Infiltration, Mass, Nodule, Pleural Thickening, Pneumonia, Pneumothorax, Pneumoperitoneum, Pneumomediastinum, Subcutaneous Emphysema, Tortuous Aorta, Calcification of the Aorta, No Finding. Built by Dr Sai and Dr Ajavindu", 
-     theme = "dark")
+    outputs=["text", "image"],  # Add an "image" output for the overlayed image
+    title="Image Classification",
+    description="Classify images using your pre-trained model."
+)
 
 # Launch the interface
 iface.launch(inline = False)