update app

app.py

@@ -15,6 +15,7 @@ import json
 from torchvision import transforms
 from typing import Dict, Optional
 from PIL import Image, ImageDraw
+from huggingface_hub import hf_hub_download
 from lang_sam import LangSAM
 
 from wilor.models import load_wilor
@@ -27,15 +28,18 @@ from ultralytics import YOLO
 LIGHT_PURPLE=(0.25098039,  0.274117647,  0.65882353)
 STEEL_BLUE=(0.2745098, 0.5098039, 0.7058824)
 
+wilor_checkpoint_path = hf_hub_download(repo_id="zerchen/hort_models", filename="wilor_final.ckpt")
+hort_checkpoint_path = hf_hub_download(repo_id="zerchen/hort_models", filename="hort_final.pth.tar")
+
 # Download and load checkpoints
-wilor_model, wilor_model_cfg = load_wilor(checkpoint_path = './pretrained_models/wilor_final.ckpt' , cfg_path= './pretrained_models/model_config.yaml')
+wilor_model, wilor_model_cfg = load_wilor(checkpoint_path = wilor_checkpoint_path, cfg_path= './pretrained_models/model_config.yaml')
 hand_detector = YOLO('./pretrained_models/detector.pt')
 # Setup the renderer
 renderer = Renderer(wilor_model_cfg, faces=wilor_model.mano.faces)
 # Setup the SAM model
 sam_model = LangSAM(sam_type="sam2.1_hiera_large")
 # Setup the HORT model
-hort_model = load_hort("./pretrained_models/hort_final.pth.tar")
+hort_model = load_hort(hort_checkpoint_path)
 
 device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
 wilor_model = wilor_model.to(device)