Update app.py

app.py

@@ -101,7 +101,7 @@ def prediction(image_path):
         ax[1].imshow(image_np,aspect='auto');
 
 
-        return objects
+        return objects,fig
     
     
         
@@ -110,7 +110,7 @@ def prediction(image_path):
     model_PATH_TO_CKPT = "frozen_inference_graph.pb"
     path_to_labels = "tf_label_map.pbtxt"
 
-    result = main(image_path,model_path,model_PATH_TO_CKPT,path_to_labels)
+    result,fig = main(image_path,model_path,model_PATH_TO_CKPT,path_to_labels)
     
     # print("result-",result)
     # list_to_be_sorted= [{'class': 'Y', 'score': 99.97, 'box': (157, 191, 269, 288)}, {'class': '6', 'score': 99.93, 'box': (158, 191, 247, 267)}, {'class': '9', 'score': 99.88, 'box': (156, 190, 179, 196)}, {'class': '4', 'score': 99.8, 'box': (156, 189, 198, 219)}, {'class': '1', 'score': 99.65, 'box': (157, 189, 222, 244)}, {'class': 'F', 'score': 63.4, 'box': (155, 185, 157, 175)}]
@@ -129,7 +129,7 @@ def prediction(image_path):
     response = {"predictions": [result]}
     total_time_end = time.time()
     print("total time : ",round((total_time_end-total_time_start),2))
-    return simplejson.dumps(response)
+    return simplejson.dumps(response),fig
 
 inputs = gr.inputs.Image(type = 'filepath')
 EXAMPLES = ["img1.jpg","img2.jpg","img3.jpg","img4.jpg"]
@@ -141,7 +141,7 @@ DESCRIPTION = """An image is occluded if the image is blocked by any object.
 demo_app = gr.Interface(
     fn= prediction, 
     inputs=inputs,
-    outputs= "text",
+    outputs= ["text", "image"],
     title = "Tag Diciphering",
     description = DESCRIPTION,
     examples = EXAMPLES,