Update app.py

app.py

@@ -136,11 +136,11 @@ def update_target_repo(username, source, precision):
 
 # Build the Gradio UI
 with gr.Blocks(theme=gr.themes.Soft()) as demo:
-    gr.Markdown("# 🚀 Auto-Purging Model Quantizer & Uploader")
+    gr.Markdown("# 🚀 FLUX.2-klein Dedicated Quantizer")
     gr.Markdown(
-        "Convert sharded Diffusers models (like FLUX, LongCat, Z-Image) to lower precisions (FP8, FP16, BF16).\n\n"
-        "**Auto-Delete Feature:** This tool is strictly designed to handle sharded folders. It will **automatically ignore and delete** any massive "
-        "`.safetensors` files located at the root of the repository to ensure your 16GB RAM limit is never breached and your target repository stays clean."
+        "Convert sharded **FLUX.2-klein** models (4B and 9B) to lower precisions (FP8, FP16, BF16).\n\n"
+        "**Auto-Delete OOM Protection:** This tool is strictly designed to handle the sharded `transformer` and `text_encoder` folders. "
+        "It will **automatically ignore and delete** the massive 16GB/7GB `.safetensors` files located at the root of the repository to ensure your 16GB RAM limit is never breached."
     )
 
     with gr.Row():
@@ -154,23 +154,22 @@ with gr.Blocks(theme=gr.themes.Soft()) as demo:
                 label="Your Hugging Face Username", 
                 placeholder="e.g., rootlocalghost"
             )
+            # Locked down to only FLUX.2-klein models
             source_repo = gr.Dropdown(
                 choices=[
                     "black-forest-labs/FLUX.2-klein-9B", 
-                    "black-forest-labs/FLUX.2-klein-4B",
-                    "Tongyi-MAI/Z-Image-Turbo",
-                    "meituan-longcat/LongCat-Image-Edit-Turbo"
+                    "black-forest-labs/FLUX.2-klein-4B"
                 ], 
                 value="black-forest-labs/FLUX.2-klein-9B", 
                 label="Source Repository",
-                allow_custom_value=True
+                allow_custom_value=False
             )
             
             target_components = gr.CheckboxGroup(
                 choices=["text_encoder", "transformer", "vae"],
                 value=["text_encoder", "transformer"],
-                label="Components to Quantize (Folders)",
-                info="Select which folders should be cast to the new precision. Everything else is copied as-is."
+                label="Components to Quantize",
+                info="Select which folders should be cast to the new precision. Unselected folders will be copied as-is."
             )
             
             precision = gr.Dropdown(