Update app.py

app.py

@@ -177,7 +177,7 @@ def infer(name, path, index, vc_input, vc_upload, tts_text, tts_voice, f0_up_key
 
 def load_model():
     categories = []
-    with open("weights/folder_info.json", "r", encoding="utf-8") as f:
+    with open("/weights/folder_info.json", "r", encoding="utf-8") as f:
         folder_info = json.load(f)
     for category_name, category_info in folder_info.items():
         if not category_info['enable']:
@@ -186,7 +186,7 @@ def load_model():
         category_folder = category_info['folder_path']
         models = []
         print(f"Creating category {category_title}...")
-        with open(f"weights/{category_folder}/model_info.json", "r", encoding="utf-8") as f:
+        with open(f"/weights/{category_folder}/model_info.json", "r", encoding="utf-8") as f:
             models_info = json.load(f)
         for character_name, info in models_info.items():
             if not info['enable']:
@@ -194,16 +194,16 @@ def load_model():
             model_title = info['title']
             model_name = info['model_path']
             model_author = info.get("author", None)
-            model_cover = f"weights/{category_folder}/{character_name}/{info['cover']}"
-            model_index = f"weights/{category_folder}/{character_name}/{info['feature_retrieval_library']}"
+            model_cover = f"/weights/{category_folder}/{character_name}/{info['cover']}"
+            model_index = f"/weights/{category_folder}/{character_name}/{info['feature_retrieval_library']}"
             if info['feature_retrieval_library'] == "None":
                 model_index = None
             if model_index:
                assert os.path.exists(model_index), f"Model {model_title} failed to load index."
             if not (model_author in authors or "/" in model_author or "&" in model_author):
                 authors.append(model_author)
-            model_path =  f"weights/{category_folder}/{character_name}/{model_name}"
-            cpt = torch.load(f"weights/{category_folder}/{character_name}/{model_name}", map_location="cpu")
+            model_path =  f"/weights/{category_folder}/{character_name}/{model_name}"
+            cpt = torch.load(f"/weights/{category_folder}/{character_name}/{model_name}", map_location="cpu")
             model_version = cpt.get("version", "v1")
             print(f"Indexed model {model_title} by {model_author} ({model_version})")
             models.append((character_name, model_title, model_author, model_cover, model_version, model_path, model_index))