Update app.py

app.py

@@ -1,7 +1,59 @@
 import gradio as gr
 import sambanova_gradio
 
-gr.load(
-    name='Meta-Llama-3.2-3B-Instruct',
-    src=sambanova_gradio.registry,
-).launch()
+# Define a function to load the selected model
+def load_model(model_choice):
+    model = gr.load(
+        name=model_choice,
+        src=sambanova_gradio.registry
+    )
+    return model
+
+# 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"
+]
+
+# Create Gradio interface
+with gr.Blocks() as demo:
+    gr.Markdown("# Meta-Llama Model Selector")
+    
+    # Dropdown for selecting the model
+    model_choice = gr.Dropdown(
+        choices=model_choices, 
+        label="Choose a Meta-Llama Model",
+        value="Meta-Llama-3.2-3B-Instruct"  # default value
+    )
+    
+    # Textbox for user input and output
+    input_box = gr.Textbox(
+        label="Input",
+        placeholder="Ask a question...",
+        examples=["Explain quantum gravity to a 5-year old.", 
+                  "How many R are there in the word Strawberry?"]
+    )
+    
+    # Output for the model's response
+    output_box = gr.Textbox(label="Output")
+    
+    # Button to generate the output
+    generate_button = gr.Button("Generate Response")
+    
+    # Action when button is clicked
+    def generate_response(model_choice, user_input):
+        model = load_model(model_choice)
+        output = model.predict(user_input)
+        return output
+    
+    generate_button.click(
+        generate_response, 
+        inputs=[model_choice, input_box], 
+        outputs=output_box
+    )
+
+# Launch the Gradio app
+demo.launch(share=True)