Update app.py

app.py

@@ -17,17 +17,14 @@ model = YOLO('best.pt')
 def show_preds_image(image_path):
     image = cv2.imread(image_path)
     outputs = model.predict(source=image_path)
-    results = outputs[0].cpu().numpy()
-    class_names = model.names 
+    results = outputs[0].cpu().numpy() 
+    class_name=""
+    for r in results:
+        # Get the class index
+        for c in r.boxes.cls:
+            class_name = model.names[int(c)]
+    print(class_name)
     for i, det in enumerate(results.boxes.xyxy):
-        x_min, y_min, x_max, y_max = det
-        # In this case, we don't have confidence and class index information,
-        # but we can get the class name using the index from class_names
-        class_index = i  # Assuming class index corresponds to the loop index
-        if class_index < len(class_names):
-            class_name = class_names[class_index]
-        else:
-            class_name = "Unknown"
         cv2.rectangle(
             image,
             (int(det[0]), int(det[1])),