Update app.py

app.py

@@ -5,24 +5,29 @@ from ultralyticsplus import YOLO, render_result
 import cv2
 import numpy as np
 
-# def response(image):
-#   print(image)
-#   results = model(image)
-#   for i, r in enumerate(results):
+model = YOLO('best (1).pt')
+
+def response(image):
+  print(image)
+  results = model(image)
+    text = ""
+  for i, r in enumerate(results):
       
-#     # Plot results image
-#       im_bgr = r.plot()  
-#       im_rgb = im_bgr[..., ::-1]  # Convert BGR to RGB
+      conf = results.boxes.conf
+      cls = results.boxes.cls
+    # Plot results image
+      im_bgr = r.plot()  
+      im_rgb = im_bgr[..., ::-1]  # Convert BGR to RGB
+      text += f"Detected {cls} with confidence {conf} at \n"
+        
+    # im_rgb = Image.fromarray(im_rgb)
     
-#     # im_rgb = Image.fromarray(im_rgb)
       
-#       return im_rgb
+      return text
 
 name = ['grenade','knife','pistol','rifle']
 
 def response2(image: gr.Image = None,image_size: gr.Slider = 640, conf_threshold: gr.Slider = 0.3, iou_threshold: gr.Slider = 0.6):
-             
-    model = YOLO('best (1).pt')
 
     results = model.predict(image, conf=conf_threshold, iou=iou_threshold, imgsz=image_size)
 
@@ -30,16 +35,15 @@ def response2(image: gr.Image = None,image_size: gr.Slider = 640, conf_threshold
 
     render = render_result(model=model, image=image, result=results[0], rect_th = 1, text_th = 1)
    
-    text = ""
-    
    
-    conf = results.boxes.conf
-    cls = results.boxes.cls
+    text = response(image)
+   
+    
     # xywh = int(results.boxes.xywh)
     # x = xywh[0]
     # y = xywh[1]
         
-    text += f"Detected {cls} with confidence {conf} at \n"
+   
             
     return render,text