Update app.py

app.py

@@ -40,7 +40,9 @@ def load_model(model_repo_id):
 def predict(pilimg,Threshold):
 
     image_np = pil_image_as_numpy_array(pilimg)
-    #Thresholdf=float(Threshold)
+    if Threshold == None
+        Threshold=0.38
+    
     return predict2(image_np),predict3(image_np),Threshold
 
 def predict2(image_np):
@@ -170,13 +172,13 @@ test12 = os.path.join(os.path.dirname(__file__), "data/test12.jpeg")
 base_image = gr.Interface(
     fn=predict,
 #    inputs=[gr.Image(type="pil"),gr.Slider(minimum=0.01, maximum=1, value=0.38 ,label="Threshold",info="[not in used]to set prediction confidence threshold")],
-    inputs=[gr.Image(type="pil"),gr.Textbox(value=0.38 ,label="Threshold",info="to set prediction confidence threshold")],
+    inputs=[gr.Image(type="pil"),gr.Textbox(value=0.38 ,label="Change default 0.38 Threshold",info="to set prediction confidence threshold")],
 
-    outputs=[gr.Image(type="pil",label="Base Model Inference"),gr.Image(type="pil",label="Tuned Model Inference"),gr.Textbox()],
+    outputs=[gr.Image(type="pil",label="Base Model Inference"),gr.Image(type="pil",label="Tuned Model Inference"),gr.Textbox(label="Tuned Model Inference")],
     title="Luffy and Chopper Head detection. SSD mobile net V2 320x320",
     description="Upload a Image for prediction or click on below examples. Prediction confident >38% will be shown in dectected images. Threshold slider is WIP",
-    examples=[[test1],[test2],[test3],[test4],[test5],[test6],[test7],[test8],[test9],[test10],[test11],[test12],],
-    cache_examples=True
+    examples=[[test1,0.38],[test2,0.38],[test3,0.38],[test4,0.38],[test5,0.38],[test6,0.38],[test7,0.38],[test8,0.38],[test9,0.38],[test10,0.38],[test11,0.38],[test12,0.38],],
+    cache_examples=True,examples_per_page=12
     )#.launch(share=True)
 
 # tuned_image = gr.Interface(
@@ -210,7 +212,7 @@ base_image = gr.Interface(
 
 video = gr.Interface(
     fn=lambda x,y: [x,y], #video_demo,
-    inputs=[gr.Video(label="Base Model Video",interactive=False),gr.Video(label="Tuned Model Video",interactive=False)], 
+    inputs=[gr.Video(label="Base Model Video",interactive=False,visible=False),gr.Video(label="Tuned Model Video",interactive=False,visible=False)], 
     outputs=[gr.Video(label="Base Model Inferenced Video"), gr.Video(label="Tuned Model Inferenced Video")],
     examples=[
         [a, b]