Update app.py

app.py

@@ -40,47 +40,13 @@ video_directory = "/home/user/app/video"
  #      im_bgr = r.plot()  
  #      im_rgb = im_bgr[..., ::-1]  # Convert BGR to RGB
 
-def response(image):
-  print(image)
-  results = model(image)
-  text = ""
-  name_weap = ""
-    
-  for r in results:
-    conf = np.array(r.boxes.conf)
-    cls = np.array(r.boxes.cls)
-    cls = cls.astype(int)
-    xywh = np.array(r.boxes.xywh)
-    xywh = xywh.astype(int)  
-      
-    for con, cl, xy in zip(conf, cls, xywh):
-        cone = con.astype(float)
-        conef = round(cone,3)
-        conef = conef * 100
-        text += (f"Detected {name[cl]} with confidence {round(conef,1)}% at ({xy[0]},{xy[1]})\n")
-        
-        if cl == 0:
-            name_weap += name[cl] + '\n'
-        elif cl == 1:
-            name_weap += name[cl] + '\n'
-        elif cl == 2:
-            out = model2(image)
-            name_weap += out[0]["label"] + '\n'
-        elif cl == 3:
-            out = model2(image)
-            name_weap += out[0]["label"] + '\n'
-
-        
-    # im_rgb = Image.fromarray(im_rgb)
-    
-      
-    return name_weap, text
-
-
 
 def response2(image: gr.Image = None,image_size: gr.Slider = 640, conf_threshold: gr.Slider = 0.3, iou_threshold: gr.Slider = 0.6):
 
-    results = model.predict(image, conf=conf_threshold, iou=iou_threshold, imgsz=image_size)
+    results = model.predict(image, conf=conf_threshold, iou=iou_threshold, imgsz=image_size
+                           
+    text = ""
+    name_weap = ""
     
     box = results[0].boxes
 
@@ -89,14 +55,37 @@ def response2(image: gr.Image = None,image_size: gr.Slider = 640, conf_threshold
         im = Image.fromarray(im_array[..., ::-1])
 
     
-    weapon_name, text_detection = response(image)
-   
+    
+    for r in results:
+        conf = np.array(r.boxes.conf)
+        cls = np.array(r.boxes.cls)
+        cls = cls.astype(int)
+        xywh = np.array(r.boxes.xywh)
+        xywh = xywh.astype(int)  
+      
+        for con, cl, xy in zip(conf, cls, xywh):
+            cone = con.astype(float)
+            conef = round(cone,3)
+            conef = conef * 100
+            text += (f"Detected {name[cl]} with confidence {round(conef,1)}% at ({xy[0]},{xy[1]})\n")
+            
+            if cl == 0:
+                name_weap += name[cl] + '\n'
+            elif cl == 1:
+                name_weap += name[cl] + '\n'
+            elif cl == 2:
+                out = model2(image)
+                name_weap += out[0]["label"] + '\n'
+            elif cl == 3:
+                out = model2(image)
+                name_weap += out[0]["label"] + '\n'
+      
     
     # xywh = int(results.boxes.xywh)
     # x = xywh[0]
     # y = xywh[1]
            
-    return im, text_detection, weapon_name
+    return im, text, name_weap
 
 
 inputs = [