import gradio as gr from huggingface_hub import InferenceClient # Initialize the InferenceClient with the specified model client = InferenceClient("WhiteRabbitNeo/Llama-3.1-WhiteRabbitNeo-2-70B") def respond( message, history: list[tuple[str, str]], system_message, max_tokens, temperature, top_p, ): messages = [{"role": "system", "content": system_message}] for val in history: if val[0]: messages.append({"role": "user", "content": val[0]}) if val[1]: messages.append({"role": "assistant", "content": val[1]}) messages.append({"role": "user", "content": message}) response = "" try: for message in client.chat_completion( messages, max_tokens=max_tokens, stream=True, temperature=temperature, top_p=top_p, ): token = message['choices'][0]['delta']['content'] response += token yield response except Exception as e: yield f"An error occurred: {str(e)}" # Define the system message with a cybersecurity focus system_message = ( "You are a cybersecurity expert chatbot, providing assistance on penetration testing, ransomware analysis, and code classification. " "Your responses should be concise, accurate, and tailored to cybersecurity professionals." ) # Create the Gradio interface with dark/light mode toggle demo = gr.Interface( fn=respond, inputs=[ gr.Textbox(value=system_message, label="System Message"), gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max New Tokens"), gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"), gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (Nucleus Sampling)"), gr.Checkbox(label="Dark Mode", value=False), # Dark mode toggle ], outputs=[gr.Textbox()], theme="dark", # Default theme ) def toggle_theme(dark_mode): """Toggle between dark and light themes based on user input.""" return "dark" if dark_mode else "light" # Update the theme based on the checkbox value demo.change(fn=toggle_theme, inputs=[demo.inputs[4]], outputs=[demo]) if __name__ == "__main__": demo.launch()