import gradio as gr from openai import OpenAI client = OpenAI( base_url="https://integrate.api.nvidia.com/v1", api_key="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" ) 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 = "" completion = client.chat.completions.create( model="nvidia/nemotron-4-340b-instruct", messages=messages, max_tokens=max_tokens, temperature=temperature, top_p=top_p, stream=True, ) for chunk in completion: if chunk.choices[0].delta.content is not None: token = chunk.choices[0].delta.content response += token yield response demo = gr.ChatInterface( respond, title="Friendly Chatbot", additional_inputs=[ gr.Textbox(value="You are a friendly Chatbot.", 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)", ), ], ) if __name__ == "__main__": demo.launch()