Update app.py

app.py

@@ -20,6 +20,8 @@ def detect_objects(image, score_threshold):
     target_sizes = torch.tensor([image.size[::-1]])
     results = processor.post_process_object_detection(outputs, target_sizes=target_sizes)
 
+    labels_output = [] 
+    
     # Draw bounding boxes around detected objects
     draw = ImageDraw.Draw(image)
     for result in results:
@@ -33,14 +35,16 @@ def detect_objects(image, score_threshold):
                 label_name = "Pneumonia" if label.item() == 0 else "Other"
                 draw.rectangle(box, outline="red", width=3)
                 draw.text((box[0], box[1]), f"{label_name}: {round(score.item(), 3)}", fill="red")
+                labels_output.append(f"{label_name}: {round(score.item(), 3)}")
     
-    return image
+    return image, "\n".join(labels_output)
 
 # Create the Gradio interface
 interface = gr.Interface(
     fn=detect_objects, 
     inputs=[gr.Image(type="pil"), gr.Slider(0, 1, value=0.5, label="Score Threshold")],  # Add slider for score threshold
-    outputs=gr.Image(type="pil"),  # Corrected output type
+    # outputs=gr.Image(type="pil"),  # Corrected output type
+    outputs=[gr.Image(type="pil"), gr.Textbox(label="Detected Objects")],
     title="Object Detection with Transformers",
     description="Upload an image to detect objects using a fine-tuned Conditional-DETR model."
 )