Update app.py

app.py

@@ -29,13 +29,10 @@ def draw_detections(image, detections):
         x_max = box['xmax']
         y_max = box['ymax']
 
-        # Draw rectangles and label with a larger font size
+        # Draw rectangles and text with a larger font
         cv2.rectangle(np_image, (x_min, y_min), (x_max, y_max), (0, 255, 0), 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)
+        cv2.putText(np_image, label_text, (x_min, y_min - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (255, 255, 255), 3)
     
     # Convert BGR to RGB for displaying
     final_image = cv2.cvtColor(np_image, cv2.COLOR_BGR2RGB)
@@ -51,18 +48,19 @@ def get_pipeline_prediction(pil_image):
         print(f"An error occurred: {str(e)}")
         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"),
-    outputs=[
-        gr.Image(label="Annotated Image", type="pil"),
-        gr.JSON(label="Detected Objects")
-    ],
-    examples=[
-        ["sample1.jpg"], ["sample2.jpg"]  # Update the example paths as needed
-    ],
-    title="Object Detection Interface"
-)
+# Define the Gradio blocks interface
+with gr.Blocks() as demo:
+    gr.Markdown("## Object Detection")
+    with gr.Row():
+        inp_image = gr.Image(label="Input image", type="pil", tool=None)
+        btn_run = gr.Button('Run Detection')
+    
+    with gr.Tab("Annotated Image"):
+        out_image = gr.Image()
+    
+    with gr.Tab("Detection Results"):
+        out_json = gr.JSON()
+
+    btn_run.click(get_pipeline_prediction, inputs=inp_image, outputs=[out_image, out_json])
 
 demo.launch()