Update app.py

app.py

@@ -102,8 +102,8 @@ def upload_to_hub(path, upload_repo, hf_path, token):
     print(f"Upload successful, go to https://huggingface.co/{upload_repo} for details.")
 
 def process_model(model_id, q_method, oauth_token: gr.OAuthToken | None):
-    if oauth_token.token is None:
-        return ("You must be logged in to use MLX-my-repo", "error.png")
+    if oauth_token is None or oauth_token.token is None:
+        raise gr.Error("You must be logged in to use MLX-my-repo", "error.png")
 
     token = oauth_token.token
     # Verify the token
@@ -113,7 +113,7 @@ def process_model(model_id, q_method, oauth_token: gr.OAuthToken | None):
         username = user_info["name"]
         print(f"✅ Logged in as {username}")
     except Exception as e:
-        return (f"❌ Authentication failed: {e}", "error.png")
+        raise gr.Error(f"❌ Authentication failed: {e}", "error.png")
 
     try:
         model_name = model_id.split('/')[-1]
@@ -148,46 +148,49 @@ def process_model(model_id, q_method, oauth_token: gr.OAuthToken | None):
         print(f"❌ Error: {e}")
         return (f"Error: {e}", "error.png")
     finally:
-        # clear_hf_cache_space()
+        clear_hf_cache_space()
         print("Folder cleaned up successfully!")
 
 css="""/* Custom CSS to allow scrolling */
 .gradio-container {overflow-y: auto;}
 """
+
+model_id = HuggingfaceHubSearch(
+    label="Hub Model ID",
+    placeholder="Search for model id on Huggingface",
+    search_type="model",
+)
+
+q_method = gr.Dropdown(
+    ["FP16", "Q2", "Q3", "Q4", "Q6", "Q8"],
+    label="Conversion Method",
+    info="MLX conversion type (FP16 for float16, Q2–Q8 for quantized models)",
+    value="Q4",
+    filterable=False,
+    visible=True
+)
+
+iface = gr.Interface(
+    fn=process_model,
+    inputs=[
+        model_id,
+        q_method,
+    ],
+    outputs=[
+        gr.Markdown(label="output"),
+        gr.Image(show_label=False),
+    ],
+    title="Create your own MLX Models, blazingly fast ⚡!",
+    description="The space takes an HF repo as an input, converts it to MLX format (FP16 or quantized), and creates a Public/Private repo under your HF user namespace.",
+    api_name=False
+)
+
 # Create Gradio interface
 with gr.Blocks(css=css) as demo:
     gr.Markdown("You must be logged in to use MLX-my-repo.")
     gr.LoginButton(min_width=250)
 
-    model_id = HuggingfaceHubSearch(
-        label="Hub Model ID",
-        placeholder="Search for model id on Huggingface",
-        search_type="model",
-    )
-
-    q_method = gr.Dropdown(
-        ["FP16", "Q2", "Q3", "Q4", "Q6", "Q8"],
-        label="Conversion Method",
-        info="MLX conversion type (FP16 for float16, Q2–Q8 for quantized models)",
-        value="Q4",
-        filterable=False,
-        visible=True
-    )
-
-    iface = gr.Interface(
-        fn=process_model,
-        inputs=[
-            model_id,
-            q_method,
-        ],
-        outputs=[
-            gr.Markdown(label="output"),
-            gr.Image(show_label=False),
-        ],
-        title="Create your own MLX Models, blazingly fast ⚡!",
-        description="The space takes an HF repo as an input, converts it to MLX format (FP16 or quantized), and creates a Public/Private repo under your HF user namespace.",
-        api_name=False
-    )
+    iface.render()
 
 def restart_space():
     HfApi().restart_space(repo_id="olegshulyakov/mlx-my-repo", token=HF_TOKEN, factory_reboot=True)