# This Gradio app simulates a smart assistant similar to Alexa, but with the capabilities of LLaMA 3.2. # The assistant listens for the wake word "Hey Butler" and responds to user commands. # It can also sync with Bluetooth devices and perform tasks based on user input. # The assistant can be trained after each conversation to improve its responses. import gradio as gr import numpy as np import transformers_js_py # Assuming this is a fictional library for LLaMA 3.2 # Initialize the LLaMA 3.2 model model = transformers_js_py.LLaMA32Model() # Function to simulate the assistant's response def assistant_response(user_input, history): # Check if the wake word is present if "Hey Butler" in user_input: # Generate a response using the LLaMA 3.2 model response = model.generate_response(user_input, history) # Simulate Bluetooth sync sync_bluetooth() return response else: return "Please say 'Hey Butler' to activate the assistant." # Function to simulate Bluetooth sync def sync_bluetooth(): # Simulate Bluetooth sync (this is a placeholder function) print("Bluetooth synced successfully.") # Function to train the model after each conversation def train_model(user_input, assistant_response, history): # Train the model with the new conversation data model.train(user_input, assistant_response, history) # Define the Gradio interface with gr.Blocks() as demo: # Create a chatbot component chatbot = gr.Chatbot(type="messages") # Create a textbox for user input user_input = gr.Textbox(label="User Input") # Create a button to clear the chat history clear_button = gr.Button("Clear History") # Define the event listener for user input user_input.submit( assistant_response, inputs=[user_input, chatbot], outputs=[chatbot], queue=False ).then( train_model, inputs=[user_input, chatbot, chatbot], outputs=None, queue=False ) # Define the event listener for the clear button clear_button.click( lambda: None, None, chatbot, queue=False ) # Launch the Gradio app if __name__ == "__main__": demo.launch(show_error=True)