Update app.py

app.py

@@ -20,7 +20,7 @@ import mlx_lm
 from mlx_lm import convert
 
 HF_TOKEN = os.environ.get("HF_TOKEN")
-SPACE_HOST = os.environ.get("SPACE_HOST")
+SPACE_ID = os.environ.get("SPACE_ID")
 
 # I'm not sure if we need to add more stuff here
 QUANT_PARAMS = {
@@ -155,49 +155,47 @@ 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(redirect=f"https://{SPACE_HOST}/login/callback")
+    gr.LoginButton()
+
+    model_id = HuggingfaceHubSearch(
+        label="Hub Model ID",
+        placeholder="Search for model id on Huggingface",
+        search_type="model",
+    )
 
-    iface.render()
+    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
+    )
 
 def restart_space():
-    HfApi().restart_space(repo_id="olegshulyakov/mlx-my-repo", token=HF_TOKEN, factory_reboot=True)
+    HfApi().restart_space(repo_id=SPACE_ID, token=HF_TOKEN, factory_reboot=True)
 
 scheduler = BackgroundScheduler()
 scheduler.add_job(restart_space, "interval", seconds=21600)
 scheduler.start()
 
 # Launch the interface
-demo.queue(default_concurrency_limit=1, max_size=5).launch(debug=True, show_api=False)
+demo.launch()