Update app.py

app.py

@@ -1,62 +1,10 @@
 import gradio as gr
 import sambanova_gradio
 
-# Function to load the selected model
-def load_model(model_choice):
-    # Directly load the model without using the ChatInterface
-    model = sambanova_gradio.registry(model_choice)
-    return model
-
-# Function to generate response
-def generate_response(model_choice, user_input):
-    model = load_model(model_choice)
-    output = model(user_input)  # Assuming model is callable like a function
-    return output
-
-# Available model choices
-model_choices = [
-    "Meta-Llama-3.2-1B-lnstruct",
-    "Meta-Llama-3.2-3B-Instruct",
-    "Meta-Llama-3.1-88-Instruct",
-    "Meta-Llama-3.1-405B-lnstruct",
-    "Meta-Llama-3.1-70B-lnstruct"
-]
-
-# Gradio interface
 with gr.Blocks() as demo:
-    gr.Markdown("# Meta-Llama Model Selector")
-
-    # Dropdown for model selection
-    model_choice = gr.Dropdown(
-        choices=model_choices,
-        label="Choose a Meta-Llama Model",
-        value="Meta-Llama-3.2-3B-Instruct"  # Default selected model
-    )
-
-    # Textbox for user input
-    input_box = gr.Textbox(
-        label="Input",
-        placeholder="Ask a question..."
-    )
-
-    # Output textbox for displaying the response
-    output_box = gr.Textbox(label="Output")
-
-    # Button to generate a response
-    generate_button = gr.Button("Generate Response")
-
-    # Example inputs
-    examples = gr.Examples(
-        examples=["Explain quantum gravity to a 5-year-old.", "How many R are there in the word Strawberry?"],
-        inputs=input_box
-    )
-
-    # Define button click action
-    generate_button.click(
-        fn=generate_response,
-        inputs=[model_choice, input_box],
-        outputs=output_box
-    )
+    with gr.Tab("405B"):
+        gr.load('Meta-Llama-3.1-405B-Instruct', src=sambanova_gradio.registry)
+    with gr.Tab("70B"):
+        gr.load('Meta-Llama-3.1-70B-Instruct-8k', src=sambanova_gradio.registry)
 
-# Launch the Gradio app
-demo.launch(share=True)
+demo.launch()