fixing CI

app.py

@@ -18,13 +18,19 @@ AVAILABLE_MODELS = [
     "ucf",
 ]
 
-# load the model from HF Model Registry
 def load_model(model_name, config_path, weights_path):
     with open(config_path, 'r') as f:
         config = yaml.safe_load(f)
     
     config['model_name'] = model_name
     
+    # download the pretrained model from Hugging Face
+    if 'pretrained' in config:
+        pretrained_filename = f"{model_name}_best.pth"
+        repo_id = "ArissBandoss/deepfake-video-classifier"
+        pretrained_path = hf_hub_download(repo_id=repo_id, filename=pretrained_filename)
+        config['pretrained'] = pretrained_path
+    
     model_class = DETECTOR[model_name]
     model = model_class(config).to(device)
     
@@ -33,6 +39,7 @@ def load_model(model_name, config_path, weights_path):
     model.eval()
     return model
 
+
 # preprocess a single video
 def preprocess_video(video_path, output_dir, frame_num=32):
     os.makedirs(output_dir, exist_ok=True)

training/config/detector/ucf.yaml

@@ -2,7 +2,7 @@
 log_dir: /data/home/zhiyuanyan/DeepfakeBench/debug_logs/ucf
 
 # model setting
-pretrained: /teamspace/studios/this_studio/DeepfakeBench/training/pretrained/xception-b5690688.pth   # path to a pre-trained model, if using one
+pretrained: xception_best.pth   # path to a pre-trained model, if using one
 # pretrained: '/home/zhiyuanyan/.cache/torch/hub/checkpoints/resnet34-b627a593.pth'   # path to a pre-trained model, if using one
 model_name: ucf   # model name
 backbone_name: xception  # backbone name

training/config/detector/xception.yaml

@@ -2,7 +2,7 @@
 log_dir: /teamspace/studios/this_studio/DeepfakeBench/logs/testing_bench
 
 # model setting
-pretrained: /teamspace/studios/this_studio/DeepfakeBench/training/pretrained/xception-b5690688.pth   # path to a pre-trained model, if using one
+pretrained: xception_best.pth   # path to a pre-trained model, if using one
 model_name: xception   # model name
 backbone_name: xception  # backbone name