Enhance app.py by adding an override option for run name in the _prepare_script function and updating the run_training function to use an auto-generated dataset directory name. Modify UI to reflect changes from dataset_name to output_name for better clarity in user input.

app.py

@@ -165,7 +165,8 @@ def _prepare_script(
     dataset_config: Optional[str] = None,
     override_max_epochs: Optional[int] = None,
     override_save_every: Optional[int] = None,
-    ) -> Path:
+    override_run_name: Optional[str] = None,
+) -> Path:
     """Create a temporary copy of train_QIE.sh with injected variables.
 
     Only variables that must vary per-run are replaced. The rest of the script
@@ -251,6 +252,8 @@ def _prepare_script(
     if override_save_every is not None and override_save_every > 0:
         txt = re.sub(r"--save_every_n_epochs\s+\d+",
                      f"--save_every_n_epochs {override_save_every}", txt)
+    if override_run_name:
+        txt = re.sub(r"^RUN_NAME=.*$", f"RUN_NAME={_bash_quote(override_run_name)}", txt, flags=re.MULTILINE)
 
     # Write to a temp file alongside this repo for easier inspection
     run_dir = TRAINING_DIR / ".gradio_runs"
@@ -332,7 +335,7 @@ def _startup_clone_musubi_tuner() -> None:
 
 @spaces.GPU(duration=7200)
 def run_training(
-    dataset_name: str,
+    output_name: str,
     caption: str,
     image_uploads: Any,
     control0_uploads: Any,
@@ -350,8 +353,8 @@ def run_training(
     save_every: int,
 ) -> Iterable[str]:
     # Basic validation
-    if not dataset_name.strip():
-        yield "[ERROR] DATASET_NAME is required."
+    if not output_name.strip():
+        yield "[ERROR] OUTPUT NAME is required."
         return
     if not caption.strip():
         yield "[ERROR] CAPTION is required."
@@ -362,7 +365,9 @@ def run_training(
     # Resolve data root and create dataset directories (auto-decide)
     global DATA_ROOT_RUNTIME
     DATA_ROOT_RUNTIME = _ensure_data_root(None)
-    ds_name = dataset_name.strip()
+    # Auto-generate dataset directory name
+    import time
+    ds_name = f"dataset_{int(time.time())}"
     ds_dir = os.path.join(DATA_ROOT_RUNTIME, ds_name)
     img_folder_name = DEFAULT_IMAGE_FOLDER
     img_dir = os.path.join(ds_dir, img_folder_name)
@@ -435,6 +440,7 @@ def run_training(
         dataset_config=ds_conf,
         override_max_epochs=max_epochs if max_epochs and max_epochs > 0 else None,
         override_save_every=save_every if save_every and save_every > 0 else None,
+        override_run_name=output_name.strip(),
     )
 
 
@@ -470,7 +476,7 @@ def build_ui() -> gr.Blocks:
         """)
 
         with gr.Row():
-            dataset_name = gr.Textbox(label="DATASET_NAME (folder under DATA_ROOT)", placeholder="my_dataset", lines=1)
+            output_name = gr.Textbox(label="OUTPUT NAME", placeholder="my_lora_output", lines=1)
             caption = gr.Textbox(label="CAPTION", placeholder="A photo of ...", lines=2)
 
         with gr.Row():
@@ -509,7 +515,7 @@ def build_ui() -> gr.Blocks:
         run_btn.click(
             fn=run_training,
             inputs=[
-                dataset_name, caption, images_input,
+                output_name, caption, images_input,
                 ctrl0_files, ctrl1_files, ctrl2_files, ctrl3_files, ctrl4_files, ctrl5_files, ctrl6_files, ctrl7_files,
                 models_root, output_dir_base, dataset_config,
                 max_epochs, save_every,