Update app.py

app.py

@@ -4,24 +4,24 @@ import cv2
 import json
 import os
 import gradio as gr
-# from detectron2.detectron2.engine import DefaultPredictor
-# from detectron2.detectron2.config import get_cfg
-# from detectron2 import model_zoo
-# import torch_utils
-# import dnnlib
-# # Create output directory if it doesn't exist
-# output_dir = "key/"
-# os.makedirs(output_dir, exist_ok=True)
-# output_file = os.path.join(output_dir, "keypoints.json")
-
-# # Load pre-trained Keypoint R-CNN model
-# cfg = get_cfg()
-# cfg.merge_from_file(model_zoo.get_config_file("COCO-Keypoints/keypoint_rcnn_R_50_FPN_3x.yaml"))
-# cfg.MODEL.WEIGHTS = model_zoo.get_checkpoint_url("COCO-Keypoints/keypoint_rcnn_R_50_FPN_3x.yaml")
-# cfg.MODEL.DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
-
-# # Load the predictor
-# predictor = DefaultPredictor(cfg)
+from detectron2.detectron2.engine import DefaultPredictor
+from detectron2.detectron2.config import get_cfg
+from detectron2 import model_zoo
+import torch_utils
+import dnnlib
+# Create output directory if it doesn't exist
+output_dir = "key/"
+os.makedirs(output_dir, exist_ok=True)
+output_file = os.path.join(output_dir, "keypoints.json")
+
+# Load pre-trained Keypoint R-CNN model
+cfg = get_cfg()
+cfg.merge_from_file(model_zoo.get_config_file("COCO-Keypoints/keypoint_rcnn_R_50_FPN_3x.yaml"))
+cfg.MODEL.WEIGHTS = model_zoo.get_checkpoint_url("COCO-Keypoints/keypoint_rcnn_R_50_FPN_3x.yaml")
+cfg.MODEL.DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
+
+# Load the predictor
+predictor = DefaultPredictor(cfg)
 
 def process_image(image, user_height_cm):
     # Convert Gradio image input to OpenCV format