Upload folder using huggingface_hub

webserver/train_service.py

@@ -302,6 +302,26 @@ def run_finetune_job(job_id: str, input_paths: dict, run_dir: str, config: Train
 
         _update_job(jobs, job_id, message="Loading model and fine-tuning...", progress=30, phase="loading_model")
         progress_reporter = lambda **kwargs: _training_progress_update(jobs, job_id, **kwargs)
+        
+        model_path = input_paths.get("model")
+        BASE_DIR = os.path.join(ROOT_DIR, "webserver")
+        default_model_path = os.path.join(BASE_DIR, "weights", "Fine_tuned.pth")
+        if not model_path or not os.path.exists(model_path):
+            print(f"[JOB {job_id}] No valid user model uploaded. Falling back to built-in model: {default_model_path}")
+            model_path = default_model_path
+        else:
+            print(f"[JOB {job_id}] Using user-uploaded model from: {model_path}")
+        if not os.path.exists(model_path):
+            raise FileNotFoundError(f"Model file missing completely at: {model_path}")
+            
+        file_size = os.path.getsize(model_path)
+        if file_size < 1000000:
+            raise ValueError(
+                f"🚨 LFS POINTER ERROR: The model file is only {file_size} bytes! "
+                f"It is a broken Git LFS pointer, not the real weight. "
+                f"Please go to the Hugging Face website and drag-and-drop upload the 300MB Fine_tuned.pth to the 'weights' folder."
+            )
+        
         classifier, _, _, mae_model = load_mae_model_for_classification(
             input_paths["model"],
             input_length=3500,