Update app.py

app.py

@@ -18,7 +18,7 @@ class InferenceConfig(Config):
 
 config = InferenceConfig()
 
-# Rebuild the Mask R-CNN model
+# Initialize the Mask R-CNN model
 model = modellib.MaskRCNN(mode="inference", config=config, model_dir=os.getcwd())
 
 # Load the Mask R-CNN model weights
@@ -34,7 +34,7 @@ def apply_mask_rcnn(image):
             image = cv2.cvtColor(image, cv2.COLOR_RGBA2RGB)
 
         # Resize the image to match the model input size (for inference)
-        resized_image = cv2.resize(image, (1024, 1024))  # Adjust based on the input shape of your model
+        resized_image = cv2.resize(image, (1024, 1024))  # Adjust based on model input requirements
         input_image = np.expand_dims(resized_image, axis=0)
 
         # Use Mask R-CNN to predict
@@ -62,27 +62,25 @@ def apply_mask_rcnn(image):
         return image  # Return original image if segmentation fails
 
 # Gradio interface definition
-inputs = gr.Image(source="upload", tool="editor", type="numpy", label="Upload an image")
-outputs = gr.Image(type="numpy", label="Segmented Image")
-
-# Gradio app layout
 with gr.Blocks() as demo:
     gr.Markdown("<h1 style='text-align: center;'>Image Segmentation with Mask R-CNN</h1>")
     gr.Markdown("Upload an image to see segmentation results using the Mask R-CNN model.")
-    
+
     # Input and output layout
     with gr.Row():
         with gr.Column():
             gr.Markdown("### Upload an Image")
-            inputs.render()  # Render the input (image upload)
-
-            # Submit button
-            gr.Button("Submit").click(fn=apply_mask_rcnn, inputs=inputs, outputs=outputs)
-            gr.Button("Clear").click(fn=lambda: None)
+            input_image = gr.Image(source="upload", tool="editor", type="numpy", label="Upload an image")
+            submit_btn = gr.Button("Submit")
+            clear_btn = gr.Button("Clear")
 
         with gr.Column():
             gr.Markdown("### Segmented Image Output")
-            outputs.render()  # Render the output (segmented image)
+            output_image = gr.Image(type="numpy", label="Segmented Image")
+
+    # Set up button functionality
+    submit_btn.click(fn=apply_mask_rcnn, inputs=input_image, outputs=output_image)
+    clear_btn.click(fn=lambda: None)
 
 # Launch the Gradio app
 demo.launch()