Update app.py

app.py

@@ -8,7 +8,6 @@ from ultralyticsplus import YOLO
 torch.hub.download_url_to_file('https://raw.githubusercontent.com/Owaiskhan9654/test_test/main/20.jpeg', '20.jpeg')
 torch.hub.download_url_to_file('https://raw.githubusercontent.com/Owaiskhan9654/test_test/main/30.jpeg', '30.jpeg')
 torch.hub.download_url_to_file('https://raw.githubusercontent.com/Owaiskhan9654/test_test/main/17.jpeg', '17.jpeg')
-
 def yolov8_inference(
     image: gr.inputs.Image = None,
     model_path: gr.inputs.Dropdown = None,
@@ -31,35 +30,35 @@ def yolov8_inference(
     model.conf = conf_threshold
     model.iou = iou_threshold
     results = model.predict(image, imgsz=image_size, )#return_outputs=True)
-    print(results)
+    print("Outputs", results[0].numpy())
+    # data = np.array(results[0].numpy(), dtype=np.float32)
+    print("Boxexes",results[0].boxes.boxes)
     object_prediction_list = []
-    for _, image_results in enumerate(results):
-        if len(image_results)!=0:
-            image_predictions_in_xyxy_format = image_results#['det']
-            for pred in image_predictions_in_xyxy_format:
-                print(pred)
-                x1, y1, x2, y2 = (
-                    int(pred[0]),
-                    int(pred[1]),
-                    int(pred[2]),
-                    int(pred[3]),
-                )
-                bbox = [x1, y1, x2, y2]
-                score = pred[4]
-                category_name = model.model.names[int(pred[5])]
-                category_id = pred[5]
-                object_prediction = ObjectPrediction(
-                    bbox=bbox,
-                    category_id=int(category_id),
-                    score=score,
-                    category_name=category_name,
-                )
-                object_prediction_list.append(object_prediction)
-
+    outputs = results[0].boxes.boxes.numpy()
+    if len(outputs)!=0
+        for pred in outputs:
+            print(type(pred),pred)
+            x1, y1, x2, y2 = (
+                int(pred[0]),
+                int(pred[1]),
+                int(pred[2]),
+                int(pred[3]),
+            )
+            bbox = [x1, y1, x2, y2]
+            score = pred[4]
+            category_name = model.model.names[int(pred[5])]
+            category_id = pred[5]
+            object_prediction = ObjectPrediction(
+                bbox=bbox,
+                category_id=int(category_id),
+                score=score,
+                category_name=category_name,
+            )
+            object_prediction_list.append(object_prediction)
+            
     image = read_image(image)
     output_image = visualize_object_predictions(image=image, object_prediction_list=object_prediction_list)
-    return output_image['image']
-        
+    return output_image['image']        
 
 inputs = [
     gr.inputs.Image(type="filepath", label="Input Image"),
@@ -71,7 +70,7 @@ inputs = [
 ]
 
 outputs = gr.outputs.Image(type="filepath", label="Output Image")
-title = "Ultralytics YOLOv8: State-of-the-Art YOLO Models"
+title = "Custom YOLOv8: Trained on Industrial Equipments predictions"
 
 examples = [['20.jpeg', 'owaiskha9654/yolov8-custom_objects', 224, 0.25, 0.45], ['30.jpeg', 'owaiskha9654/yolov8-custom_objects', 224, 0.25, 0.45],]# ['17.jpeg', 'owaiskha9654/yolov8-custom_objects', 1280, 0.25, 0.45]]
 demo_app = gr.Interface(