get model configs from local file instead of urls

inference_manager.py

@@ -259,36 +259,43 @@ class InferenceManager:
         torch.cuda.empty_cache()
         print("Memory released and cache cleared.")
 
+
 class ModelManager:
-    def __init__(self, config_urls):
+    def __init__(self, model_directory):
         """
-        Initialize the ModelManager by loading all models specified by the URLs.
+        Initialize the ModelManager by scanning all `.model.json` files in the given directory.
 
-        :param config_urls: List of URLs pointing to model config files (e.g., ["model1/config.json", "model2/config.json"]).
+        :param model_directory: The directory to scan for model config files (e.g., "/path/to/models").
         """
         self.models = {}
-        self.load_models(config_urls)
+        self.model_directory = model_directory
+        self.load_models()
 
-    def load_models(self, config_urls):
+    def load_models(self):
         """
-        Load and initialize InferenceManager instances for each config URL.
+        Scan the model directory for `.model.json` files and initialize InferenceManager instances for each one.
 
-        :param config_urls: List of config file URLs.
+        :param model_directory: Directory to scan for `.model.json` files.
         """
-        for url in config_urls:
-            model_name = self.get_model_name_from_url(url)
-            print(f"Initializing model: {model_name} from {url}")
+        model_files = glob.glob(os.path.join(self.model_directory, "*.model.json"))
+        if not model_files:
+            print(f"No model configuration files found in {self.model_directory}")
+            return
+
+        for file_path in model_files:
+            model_name = self.get_model_name_from_url(file_path)
+            print(f"Initializing model: {model_name} from {file_path}")
             try:
                 # Initialize InferenceManager for each model
-                self.models[model_name] = InferenceManager(config_path=url)
+                self.models[model_name] = InferenceManager(config_path=file_path)
             except Exception as e:
-                print(f"Failed to initialize model {model_name} from {url}: {e}")
+                print(f"Failed to initialize model {model_name} from {file_path}: {e}")
 
     def get_model_name_from_url(self, url):
         """
-        Extract the model name from the config URL (filename without extension).
+        Extract the model name from the config file path (filename without extension).
 
-        :param url: The URL of the configuration file.
+        :param url: The file path of the configuration file.
         :return: The model name (file name without extension).
         """
         filename = os.path.basename(url)
@@ -331,10 +338,7 @@ class ModelManager:
             model.release(model.base_model_pipeline)
         except Exception as e:
             print(f"Failed to release model {model_id}: {e}")
-
-
-
-
+            
 # Hugging Face file download function - returns only file path
 def download_from_hf(filename, local_dir=None):
     try: