Update app.py

app.py

@@ -55,40 +55,40 @@ def perform_inference(image):
     # Apply the mask to the original image (heatmap for visualization)
     heatmap_img = cv2.applyColorMap(mask_binary, cv2.COLORMAP_JET)
     segmented_image = cv2.addWeighted(image, 0.7, heatmap_img, 0.3, 0)
-    segmented_image_with_box = segmented_image.copy() #make a copy for drawing box
+    segmented_image_with_box = segmented_image.copy()
 
-
-    # Get bounding boxes for all contours
+    # Get bounding boxes for all contours and annotate
     for contour in contours:
         x, y, w, h = cv2.boundingRect(contour)
         cv2.rectangle(segmented_image_with_box, (x, y), (x + w, y + h), (0, 0, 255), 2)
+        cv2.putText(segmented_image_with_box, "Tumour Detected", (x, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 0, 255), 2)
 
 
     #Convert back to BGR
     segmented_image_with_box = cv2.cvtColor(segmented_image_with_box, cv2.COLOR_RGB2BGR)
-    #Return image with bounding box
     return (Image.fromarray(image),
             Image.fromarray(mask_binary.astype(np.uint8)),
             Image.fromarray(segmented_image_with_box))
 
 
+
 # Gradio app
 def gradio_app():
     with gr.Blocks() as demo:
         with gr.Row():
-          with gr.Column():
-              input_image = gr.Image(label="Input Image", type="numpy")
-              submit_btn = gr.Button("Submit")
+            with gr.Column():
+                input_image = gr.Image(label="Input Image", type="numpy")
+                submit_btn = gr.Button("Submit")
 
-          with gr.Column():
-              original_image_output = gr.Image(label="Original Image")
-              mask_output = gr.Image(label="Predicted Mask")
-              segmented_image_output= gr.Image(label="Segmented Image")
+            with gr.Column():
+                 original_image_output = gr.Image(label="Original Image")
+                 mask_output = gr.Image(label="Predicted Mask")
+                 segmented_image_output= gr.Image(label="Segmented Image")
 
 
         submit_btn.click(perform_inference, inputs=input_image, outputs=[original_image_output, mask_output, segmented_image_output])
-
     demo.launch()
 
+
 if __name__ == "__main__":
     gradio_app()