json output1

app.py

@@ -13,6 +13,7 @@ import matplotlib.pyplot as plt
 from transformers import AutoModelForMaskGeneration, AutoProcessor, pipeline
 import gradio as gr
 import spaces
+import json
 
 @dataclass
 class BoundingBox:
@@ -191,11 +192,22 @@ def process_image(image):
     annotated_image = plot_detections(original_image, detections)
     yellow_background_with_insects = create_yellow_background_with_insects(np.array(original_image), detections)
     yellow_background_with_boxes = draw_classification_boxes(yellow_background_with_insects.copy(), detections)
-    return annotated_image, yellow_background_with_boxes
+    
+    detection_results_json = []
+    for detection in detections:
+        result_dict = {
+            "score": detection.score,
+            "label": detection.label,
+            "box": detection.box.xyxy,
+            "mask": detection.mask.tolist() if detection.mask is not None else None
+        }
+        detection_results_json.append(result_dict)
+    
+    return annotated_image, yellow_background_with_boxes, json.dumps(detection_results_json)
 
 gr.Interface(
     fn=process_image,
     inputs=gr.Image(type="pil"),
-    outputs=[gr.Image(type="numpy"), gr.Image(type="numpy")],
+    outputs=[gr.Image(type="numpy"), gr.Image(type="numpy"), gr.JSON()],
     title="🐞 InsectSAM + GroundingDINO Inference",
-).launch()
+).launch()