Update app.py

app.py

@@ -31,9 +31,9 @@ image_path = [['test_images/2a998cfb0901db5f8210.jpg','cham_diem_yolov8', 640, 0
 # model = YOLO('linhcuem/chamdiemgianhang_yolov8_300epochs')
 # model = YOLO('linhcuem/chamdiemgianhang_yolov8_ver21')
 # model = YOLO('linhcuem/cham_diem_yolov8_ver20')
-model_ids = ['linhcuem/checker_TB_yolov8_ver1', 'linhcuem/cham_diem_yolov8', 'linhcuem/chamdiemgianhang_yolov8_300epochs', 'linhcuem/cham_diem_yolov8_ver20', 'linhcuem/chamdiemgianhang_yolov8_ver21']
-current_model_id = model_ids[-1]
-model = YOLO(current_model_id)
+# model_ids = ['linhcuem/checker_TB_yolov8_ver1', 'linhcuem/cham_diem_yolov8', 'linhcuem/chamdiemgianhang_yolov8_300epochs', 'linhcuem/cham_diem_yolov8_ver20', 'linhcuem/chamdiemgianhang_yolov8_ver21']
+# current_model_id = model_ids[-1]
+# model = YOLO(current_model_id)
 
 
 ###################################################
@@ -44,7 +44,7 @@ def yolov8_img_inference(
     conf_threshold= 0.25,
     iou_threshold = 0.45,
 ):
-    # model = YOLO(model_path)
+    model = YOLO(model_path)
     model.conf = conf_threshold
     model.iou = iou_threshold
     # model.overrides['conf'] = conf_threshold
@@ -56,14 +56,7 @@ def yolov8_img_inference(
     render = render_result(model=model, image=image, result=results[0])
     # get the model names list
     names = model.names
-    # get the 'obj' class id
-    # obj_id = list(names)[list(names.values()).index('lo_ytv')]
-    # ('hop_dln','hop_jn','hop_vtg','hop_ytv','lo_kids', 'lo_ytv','loc_dln','loc_jn','loc_kids','loc_ytv')]
-    # obj_id = list(names)[list(names.values()).index([0])]
-    # count 'car' objects in the results
-    # count_result = results[0].boxes.cls[0].item()
-    #count_result = results[0]boxes.cls[0].tolist()
-
+  
     object_counts = {x: 0 for x in names}
     for r in results:
         for c in r.boxes.cls:
@@ -81,35 +74,7 @@ def yolov8_img_inference(
     
 
     return render, {names[k]: v for k, v in present_objects.items()}
-    # results = model.predict(image, imgsz=image_size, return_outputs=True)
-    # results = model.predict(image)
-    # 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:
-    #             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']
-    # render = render_result(model=model, image=image, result=results[0])
+
 
 def yolov8_vid_inference(video_path):
     cap = cv2.VideoCapture(video_path)
@@ -171,9 +136,9 @@ interface_image = gr.Interface(
     fn=yolov8_img_inference,
     inputs=[
         gr.Image(type='pil'),
-        # gr.Dropdown(["linhcuem/chamdiemgianhang_yolov8_ver1"],
-        #            default="linhcuem/chamdiemgianhang_yolov8_ver1"),
-        gr.Dropdown(model_ids, value=model_ids[-1]),
+        gr.Dropdown(["linhcuem/checker_TB_yolov8_ver1","linhcuem/cham_diem_yolov8","linhcuem/chamdiemgianhang_yolov8_300epochs", "linhcuem/chamdiemgianhang_yolov8_ver21", "linhcuem/cham_diem_yolov8_ver20" ],
+                   default="linhcuem/checker_TB_yolov8_ver1", label="Model"),
+        
         gr.Slider(maximum=1280, step=32, value=640),
         gr.Slider(maximum=1.0, step=0.05, value=0.25),
         gr.Slider(maximum=1.0, step=0.05, value=0.45),