Update app.py

app.py

@@ -9,20 +9,16 @@ class LoRAInferenceWrapper:
         self.client = InferenceClient(model_id, token=token)
 
     def load_lora_weights(self):
-    # Define the path to the LoRA model
-    lora_model_path = "./lora.model.pth"  # Update to the actual file name
-
-    # Check if the file exists at the given path
-    if os.path.exists(lora_model_path):
-        print(f"Found LoRA model at: {lora_model_path}")
-        with open(lora_model_path, 'rb') as f:
-            return f.read()  # Load the file content
-    else:
-        raise FileNotFoundError(f"LoRA model not found at path: {lora_model_path}")
-
-        # Load the LoRA weights from the local file
-        with open(lora_model_path, "rb") as f:
-            return f.read()  # Return the raw bytes of the LoRA file
+        # Define the path to the LoRA model
+        lora_model_path = "./lora.model.pth"  # Update to the actual file name
+
+        # Check if the file exists at the given path
+        if os.path.exists(lora_model_path):
+            print(f"Found LoRA model at: {lora_model_path}")
+            with open(lora_model_path, 'rb') as f:
+                return f.read()  # Load the file content
+        else:
+            raise FileNotFoundError(f"LoRA model not found at path: {lora_model_path}")
 
     def preprocess_lora_weights(self, lora_weights):
         # Preprocess the LoRA weights (e.g., Base64 encoding for JSON compatibility)