Update app.py

app.py

@@ -15,8 +15,8 @@ def respond(
     top_p,
     frequency_penalty,
     seed,
-    custom_model,
-    provider,
+    provider,         # Moved before custom_model
+    custom_model,     # Moved after provider
     model_search_term,
     selected_model
 ):
@@ -25,8 +25,8 @@ def respond(
     print(f"System message: {system_message}")
     print(f"Max tokens: {max_tokens}, Temperature: {temperature}, Top-P: {top_p}")
     print(f"Frequency Penalty: {frequency_penalty}, Seed: {seed}")
-    print(f"Selected model (custom_model): {custom_model}")
-    print(f"Selected provider: {provider}")
+    print(f"Selected provider: {provider}")         # Updated order
+    print(f"Selected model (custom_model): {custom_model}")  # Updated order
     print(f"Model search term: {model_search_term}")
     print(f"Selected model from radio: {selected_model}")
 
@@ -149,14 +149,6 @@ seed_slider = gr.Slider(
     label="Seed (-1 for random)"
 )
 
-# Custom model box
-custom_model_box = gr.Textbox(
-    value="",
-    label="Custom Model",
-    info="(Optional) Provide a custom Hugging Face model path. Overrides any selected featured model.",
-    placeholder="meta-llama/Llama-3.3-70B-Instruct"
-)
-
 # Provider selection
 providers_list = [
     "hf-inference",  # Default Hugging Face Inference
@@ -178,6 +170,14 @@ provider_radio = gr.Radio(
     info="Select which inference provider to use. Uses your Hugging Face PRO credits."
 )
 
+# Custom model box
+custom_model_box = gr.Textbox(
+    value="",
+    label="Custom Model",
+    info="(Optional) Provide a custom Hugging Face model path. Overrides any selected featured model.",
+    placeholder="meta-llama/Llama-3.3-70B-Instruct"
+)
+
 # Model selection components
 model_search_box = gr.Textbox(
     label="Filter Models",
@@ -279,4 +279,4 @@ print("Gradio interface initialized.")
 
 if __name__ == "__main__":
     print("Launching the demo application.")
-    demo.launch(show_api=True)
+    demo.launch(show_api=True)  # Fixed typo: demo. Launch -> demo.launch