fix

app.py

@@ -106,7 +106,7 @@ def save_model(model, model_name, quantization_type, group_size=128, username=No
             repo_id=repo_name,
             repo_type="model",
         )
-    return f'<h1>✅ DONE</h1><br/>Find your repo here: <a href="https://huggingface.co/{repo_name}" target="_blank" style="text-decoration:underline">{repo_name}</a>'
+    return f'<h1> 🤗 DONE</h1><br/>Find your repo here: <a href="https://huggingface.co/{repo_name}" target="_blank" style="text-decoration:underline">{repo_name}</a>'
 
 def quantize_and_save(profile: gr.OAuthProfile | None, oauth_token: gr.OAuthToken | None, model_name, quantization_type, group_size, quantized_model_name):
     if oauth_token is None : 
@@ -122,11 +122,12 @@ def quantize_and_save(profile: gr.OAuthProfile | None, oauth_token: gr.OAuthToke
         return "group_size must be a number"
     
     group_size = int(group_size)
-    # try:
-    quantized_model = quantize_model(model_name, quantization_type, group_size, oauth_token, profile.username)
-    return save_model(quantized_model, model_name, quantization_type, group_size, profile.username, oauth_token, quantized_model_name)
-    # except Exception as e : 
-    #     return e
+
+    try:
+        quantized_model = quantize_model(model_name, quantization_type, group_size, oauth_token, profile.username)
+        return save_model(quantized_model, model_name, quantization_type, group_size, profile.username, oauth_token, quantized_model_name)
+    except Exception as e : 
+        return e
 
 
 css="""/* Custom CSS to allow scrolling */
@@ -135,18 +136,42 @@ css="""/* Custom CSS to allow scrolling */
 with gr.Blocks(theme=gr.themes.Ocean(), css=css) as app:
     gr.Markdown(
         """
-        # 🚀 LLM Model Quantization App
+        # 🤗 LLM Model TorchAO Quantization App
         
-        Quantize your favorite Hugging Face models and save them to your profile!
+        Quantize your favorite Hugging Face models using TorchAO and save them to your profile!
         """
     )
 
-    
     gr.LoginButton(elem_id="login-button", elem_classes="center-button", min_width=250)
 
     m1 = gr.Markdown()
     app.load(hello, inputs=None, outputs=m1)
 
+
+    radio = gr.Radio(["show", "hide"], label="Show Instructions")
+    instructions = gr.Markdown(
+        """
+        ## Instructions
+        1. Login to your HuggingFace account
+        2. Enter the name of the Hugging Face LLM model you want to quantize (Make sure you have access to it)
+        3. Choose the quantization type.
+        4. Optionally, specify the group size.
+        5. Optionally, choose a custom name for the quantized model
+        6. Click "Quantize and Save Model" to start the process.
+        7. Once complete, you'll receive a link to the quantized model on Hugging Face.
+        
+        Note: This process may take some time depending on the model size and your hardware you can check the container logs to see where are you at in the process!
+        """,
+        visible=False
+    )
+    def update_visibility(radio):  # Accept the event argument, even if not used
+        value = radio  # Get the selected value from the radio button
+        if value == "show":
+            return gr.Textbox(visible=True) #make it visible
+        else:
+            return gr.Textbox(visible=False)
+    radio.change(update_visibility, radio, instructions)
+
     with gr.Row():
         with gr.Column():
             with gr.Row():
@@ -177,38 +202,10 @@ with gr.Blocks(theme=gr.themes.Ocean(), css=css) as app:
                         interactive=True,
                         show_label=False
                     )
-            # device = gr.Dropdown(
-            #     label="Device (int4 only works with cuda)",
-            #     choices=["cuda", "cpu"],
-            #     value="cuda"
-            # )
-            # with gr.Row():
-            #     username = gr.Textbox(
-            #         label="Hugging Face Username",
-            #         placeholder="Enter your Hugging Face username",
-            #         value="",
-            #         interactive=True,
-            #         elem_id="username-box"
-            #     )
         with gr.Column():
             quantize_button = gr.Button("Quantize and Save Model", variant="primary")
             output_link = gr.Markdown(label="Quantized Model Link", container=True)
     
-    gr.Markdown(
-        """
-        ## Instructions
-        1. Login to your HuggingFace account
-        2. Enter the name of the Hugging Face LLM model you want to quantize (Make sure you have access to it)
-        3. Choose the quantization type.
-        4. Optionally, specify the group size.
-        5. Optionally, choose a custom name for the quantized model
-        6. Click "Quantize and Save Model" to start the process.
-        7. Once complete, you'll receive a link to the quantized model on Hugging Face.
-        
-        Note: This process may take some time depending on the model size and your hardware you can check the container logs to see where are you at in the process!
-        """
-    )
-    
     
     # Adding CSS styles for the username box
     app.css = """