Update app.py

app.py

@@ -37,12 +37,12 @@ def load_model(model_repo_id):
     return detection_model
 
 
-def predict(pilimg):
+def predict(pilimg,Threshold):
 
     image_np = pil_image_as_numpy_array(pilimg)
-    return predict2(image_np),predict3(image_np)
+    return predict2(image_np,float(Threshold)),predict3(image_np,float(Threshold))
 
-def predict2(image_np):
+def predict2(image_np,Threshold):
 
     results = detection_model(image_np)
 
@@ -60,7 +60,7 @@ def predict2(image_np):
         category_index,
         use_normalized_coordinates=True,
         max_boxes_to_draw=20,
-        min_score_thresh=0.38,
+        min_score_thresh=float(Threshold),#0.38,
         agnostic_mode=False,
         line_thickness=2)
 
@@ -69,7 +69,7 @@ def predict2(image_np):
     return result_pil_img2
 
 
-def predict3(image_np):
+def predict3(image_np,Threshold):
 
     results = detection_model2(image_np)
 
@@ -87,7 +87,7 @@ def predict3(image_np):
         category_index,
         use_normalized_coordinates=True,
         max_boxes_to_draw=20,
-        min_score_thresh=.38,
+        min_score_thresh=float(Threshold),#.38,
         agnostic_mode=False,
         line_thickness=2)
 
@@ -168,7 +168,7 @@ 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=0.99, value=0.6 ,label="Threshold(WIP)",info="[not in used]to set prediction confidence threshold")],
+    inputs=[gr.Image(type="pil"),gr.Slider(minimum=0.01, maximum=0.99, value=0.6 ,label="Threshold",info="[not in used]to set prediction confidence threshold")],
     outputs=[gr.Image(type="pil",label="Base Model"),gr.Image(type="pil",label="Tuned Model")],
     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",