Update models.py

models.py

@@ -63,70 +63,60 @@ def download_model_with_retry(repo_id, filename, max_retries=None, **kwargs):
 
 def load_face_analysis():
     """
-    Load face analysis model with proper error handling.
-    This version pre-downloads models to the exact path
-    insightface expects, to bypass its internal downloader.
+    Load face analysis model with proper model downloading from HuggingFace.
+    Downloads from DIAMONIK7777/antelopev2 which has the correct model structure.
     """
     print("Loading face analysis model...")
     
-    # The library expects models to be at: [root]/[name]
-    # We will set root = './models' and name = 'antelopev2'
-    # The final path it will check is './models/antelopev2'
+    # Use a persistent location for models
+    local_model_root = '/data/models' if os.path.exists('/data') else './models'
+    model_name = 'antelopev2'
     
-    local_model_root = './models' 
-    model_name = FACE_DETECTION_CONFIG['model_name'] # 'antelopev2'
-    
-    # This is the directory where the .onnx files must be:
-    local_model_path = os.path.join(local_model_root, model_name) # ./models/antelopev2
+    # This is the directory where the .onnx files must be
+    local_model_path = os.path.join(local_model_root, model_name)
     
     try:
-        # --- NEW: Download logic based on user's snippet ---
-        print(f"  Ensuring insightface models are present in {local_model_path}...")
+        # Download from DIAMONIK7777/antelopev2 repo (correct structure)
+        print(f"  Downloading antelopev2 models to {local_model_path}...")
         
-        # Check if models are already downloaded
-        test_file_path = os.path.join(local_model_path, "scrfd_10g_bnkps.onnx")
+        from huggingface_hub import snapshot_download
         
-        if not os.path.exists(test_file_path):
-            print(f"    Models not found locally, downloading from {MODEL_REPO}...")
-            
-            snapshot_kwargs = {
-                "repo_id": MODEL_REPO,
-                "repo_type": "model",
-                "allow_patterns": f"{model_name}/*.onnx", # Download only files from antelopev2 folder
-                "local_dir": local_model_root, # Download *into* ./models
-                "local_dir_use_symlinks": False
-            }
-            if HUGGINGFACE_TOKEN:
-                snapshot_kwargs["token"] = HUGGINGFACE_TOKEN
-            
-            # This will download files, preserving the repo folder structure,
-            # creating ./models/antelopev2/[...].onnx
-            snapshot_download(**snapshot_kwargs)
-            print("    [OK] Downloaded insightface models.")
-        else:
-            print("    [OK] Insightface models found locally.")
-        # --- END NEW ---
-
-        # Now, initialize FaceAnalysis
-        # It will look for models in root + name = ./models/antelopev2
+        # Download entire repo
+        snapshot_kwargs = {
+            "repo_id": "DIAMONIK7777/antelopev2",
+            "local_dir": local_model_path,
+            "local_dir_use_symlinks": False,
+            "repo_type": "model"
+        }
+        if HUGGINGFACE_TOKEN:
+            snapshot_kwargs["token"] = HUGGINGFACE_TOKEN
+        
+        antelope_download = snapshot_download(**snapshot_kwargs)
+        print(f"  [OK] Models downloaded to: {antelope_download}")
+        
+        # Initialize FaceAnalysis with correct paths
+        # Note: root should be the PARENT of the model directory
         face_app = FaceAnalysis(
             name=model_name,
-            root=local_model_root, 
+            root=local_model_root,  # Parent directory
             providers=['CUDAExecutionProvider', 'CPUExecutionProvider']
         )
+        
         face_app.prepare(
             ctx_id=FACE_DETECTION_CONFIG['ctx_id'], 
             det_size=FACE_DETECTION_CONFIG['det_size']
         )
+        
         print("  [OK] Face analysis model loaded successfully")
+        print(f"  [OK] Using providers: {face_app.det_model.providers}")
         return face_app, True
         
     except Exception as e:
-        print(f"  [WARNING] Face detection not available: {e}")
+        print(f"  [ERROR] Face detection not available: {e}")
         import traceback
         traceback.print_exc()
         return None, False
-
+        
 
 def load_depth_detector():
     """