Update app.py

app.py

@@ -21,7 +21,6 @@ def draw_detections(image, detections):
     np_image = cv2.cvtColor(np_image, cv2.COLOR_RGB2BGR)
     
     for detection in detections:
-        # Extract scores, labels, and bounding boxes correctly
         score = detection['score']
         label = detection['label']
         box = detection['box']
@@ -30,40 +29,38 @@ def draw_detections(image, detections):
         x_max = box['xmax']
         y_max = box['ymax']
 
-        # Draw rectangles and text on the image
+        # Draw rectangles and label with a larger font size
         cv2.rectangle(np_image, (x_min, y_min), (x_max, y_max), (0, 255, 0), 2)
-        cv2.putText(np_image, f'{label} {score:.2f}', (x_min, max(y_min - 10, 0)),
-                    cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 2)
+        label_text = f'{label} {score:.2f}'
+        label_size = cv2.getTextSize(label_text, cv2.FONT_HERSHEY_SIMPLEX, 0.8, 2)[0]
+        label_x = x_min
+        label_y = y_min - label_size[1] if y_min - label_size[1] > 10 else y_min + label_size[1]
+        cv2.putText(np_image, label_text, (label_x, label_y), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (255, 255, 255), 2)
     
     # Convert BGR to RGB for displaying
     final_image = cv2.cvtColor(np_image, cv2.COLOR_BGR2RGB)
-    # Convert the numpy array to PIL Image
     final_pil_image = Image.fromarray(final_image)
     return final_pil_image
 
 def get_pipeline_prediction(pil_image):
     try:
-        # Run the object detection pipeline
         pipeline_output = od_pipe(pil_image)
-        
-        # Draw the detection results on the image
         processed_image = draw_detections(pil_image, pipeline_output)
-        
-        # Provide both the image and the JSON detection results
         return processed_image, pipeline_output
     except Exception as e:
-        # Log the error
         print(f"An error occurred: {str(e)}")
-        # Return a message and an empty JSON
         return pil_image, {"error": str(e)}
 
+# Setting up Gradio interface with tabs for the outputs
 demo = gr.Interface(
     fn=get_pipeline_prediction,
-    inputs=gr.Image(label="Input image", type="pil"),
+    inputs=gr.inputs.Image(label="Input image", type="pil"),
     outputs=[
-        gr.Image(label="Annotated Image"),
-        gr.JSON(label="Detected Objects")
-    ]
+        gr.outputs.Image(type="pil", label="Annotated Image"),
+        gr.outputs.JSON(label="Detected Objects")
+    ],
+    outputs_per_tab =[['image'], ['json']],
+    tabs=["Annotated Image", "Detection Results"]
 )
 
 demo.launch()