Update app.py

app.py

@@ -11,25 +11,21 @@ client = OpenAI(
 )
 print("OpenAI client initialized.")
 
-
 def respond(
     message,
     history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-    frequency_penalty,
-    seed,
-    custom_model
+    system_message="You are a helpful assistant.",
+    max_tokens=512,
+    temperature=0.7,
+    top_p=0.95,
+    frequency_penalty=0.0,
+    seed=-1
 ):
-
     print(f"Received message: {message}")
     print(f"History: {history}")
     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}")
 
     # Convert seed to None if -1 (meaning random)
     if seed == -1:
@@ -53,8 +49,8 @@ def respond(
     messages.append({"role": "user", "content": message})
     print("Latest user message appended.")
 
-    # If user provided a model, use that; otherwise, fall back to a default model
-    model_to_use = custom_model.strip() if custom_model.strip() != "" else "meta-llama/Llama-3.3-70B-Instruct"
+    # Set the model to "meta" by default
+    model_to_use = "meta-llama/Llama-3.3-70B-Instruct"
     print(f"Model selected for inference: {model_to_use}")
 
     # Start with an empty string to build the response as tokens stream in
@@ -80,10 +76,10 @@ def respond(
 
 # GRADIO UI
 
-chatbot = gr.Chatbot(height=600, show_copy_button=True, placeholder="Select a model and begin chatting", likeable=True, layout="panel")
+chatbot = gr.Chatbot(height=600, show_copy_button=True, placeholder="Start chatting!", likeable=True, layout="panel")
 print("Chatbot interface created.")
 
-system_message_box = gr.Textbox(value="", placeholder="You are a helpful assistant.", label="System Prompt")
+system_message_box = gr.Textbox(value="You are a helpful assistant.", label="System Prompt", visible=False)
 
 max_tokens_slider = gr.Slider(
     minimum=1,
@@ -121,21 +117,7 @@ seed_slider = gr.Slider(
     label="Seed (-1 for random)"
 )
 
-# The custom_model_box is what the respond function sees as "custom_model"
-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"
-)
-
-def set_custom_model_from_radio(selected):
-    """
-    This function will get triggered whenever someone picks a model from the 'Featured Models' radio.
-    We will update the Custom Model text box with that selection automatically.
-    """
-    print(f"Featured model selected: {selected}")
-    return selected
+# Removed the custom_model_box as the model is pre-set
 
 demo = gr.ChatInterface(
     fn=respond,
@@ -146,7 +128,6 @@ demo = gr.ChatInterface(
         top_p_slider,
         frequency_penalty_slider,
         seed_slider,
-        custom_model_box,
     ],
     fill_height=True,
     chatbot=chatbot,
@@ -155,49 +136,11 @@ demo = gr.ChatInterface(
 print("ChatInterface object created.")
 
 with demo:
-    with gr.Accordion("Model Selection", open=False):
-        model_search_box = gr.Textbox(
-            label="Filter Models",
-            placeholder="Search for a featured model...",
-            lines=1
-        )
-        print("Model search box created.")
-
-        models_list = [
-            "meta-llama/Llama-3.3-70B-Instruct"
-        ]
-        print("Models list initialized.")
-
-        featured_model_radio = gr.Radio(
-            label="Select a model below",
-            choices=models_list,
-            value="meta-llama/Llama-3.3-70B-Instruct",
-            interactive=True
-        )
-        print("Featured models radio button created.")
-
-        def filter_models(search_term):
-            print(f"Filtering models with search term: {search_term}")
-            filtered = [m for m in models_list if search_term.lower() in m.lower()]
-            print(f"Filtered models: {filtered}")
-            return gr.update(choices=filtered)
-
-        model_search_box.change(
-            fn=filter_models,
-            inputs=model_search_box,
-            outputs=featured_model_radio
-        )
-        print("Model search box change event linked.")
-
-        featured_model_radio.change(
-            fn=set_custom_model_from_radio,
-            inputs=featured_model_radio,
-            outputs=custom_model_box
-        )
-        print("Featured model radio button change event linked.")
+    # No need for a model selection accordion since the model is fixed to "meta-llama"
+    pass
 
 print("Gradio interface initialized.")
 
 if __name__ == "__main__":
     print("Launching the demo application.")
-    demo.launch()
+    demo.launch()