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