Update app.py

app.py

@@ -86,7 +86,6 @@ def detect_objects2(model_name,url_input,image_input,threshold,type2):
     model = DetrForObjectDetection.from_pretrained(model_name)
         
 
-    global xxresult
 
     image = image_input
     
@@ -104,7 +103,7 @@ def detect_objects2(model_name,url_input,image_input,threshold,type2):
         total_text="Trench is Detected \n Image is Not Blurry \n"
     else:
         total_text="Trench is NOT Detected \n Image is Blurry \n"        
-        xxresult=1
+        
     print(type2)
     print(type(type2))
         
@@ -113,26 +112,51 @@ def detect_objects2(model_name,url_input,image_input,threshold,type2):
         total_text+="Measuring Tape (Vertical) for measuring Depth is Detected \n"
     else:
         total_text+="Measuring Tape (Vertical) for measuring Depth is NOT Detected \n"
-        if type2=="Trench Depth Measurement":
-            xxresult=1
+
 
     if det_lab.count(5) > 0:
         total_text+="Measuring Tape (Horizontal) for measuring Width is Detected \n"
     else:
         total_text+="Measuring Tape (Horizontal) for measuring Width is NOT Detected \n"
-        if type2=="Trench Width Measurement":
-            xxresult=1
+
 
     return total_text
     
-def tott():
-    global xxresult
+def tott(model_name,url_input,image_input,threshold,type2):
+        
+    #Extract model and feature extractor
+    feature_extractor = AutoFeatureExtractor.from_pretrained(model_name)
+    
+    
+        
+    model = DetrForObjectDetection.from_pretrained(model_name)
+        
+
+
+    image = image_input
+    
+    #Make prediction
+    processed_outputs = make_prediction(image, feature_extractor, model)
+    keep = processed_outputs["scores"] > threshold
+    det_lab = processed_outputs["labels"][keep].tolist() 
+    xxresult=0
+    if det_lab.count(1) > 0:
+        else:
+            xxresult=1
+    if det_lab.count(4) > 0:
+        else:
+            if type2=="Trench Depth Measurement":
+                xxresult=1
+    if det_lab.count(5) > 0:
+        else:
+            if type2=="Trench Width Measurement":
+                xxresult=1            
+    
     if xxresult==0:
-        text2 = "The photo is ACCEPTED"
+        return "The photo is ACCEPTED"
     else:
-        text2 = "The photo is NOT ACCEPTED"
-    xxresult==0
-    return text2
+        return "The photo is NOT ACCEPTED"
+
     
 def set_example_image(example: list) -> dict:
     return gr.Image.update(value=example[0])
@@ -190,7 +214,7 @@ with demo:
             output = gr.Textbox(label="Reason for the results")
             greet_btn = gr.Button("Results")
             greet_btn.click(fn=detect_objects2, inputs=[options,img_input,img_input,slider_input,options2], outputs=output, queue=True)
-            greet_btn.click(fn=tott, inputs=[], outputs=name, queue=True)
+            greet_btn.click(fn=tott, inputs=[options,img_input,img_input,slider_input,options2], outputs=name, queue=True)