import gradio as gr from huggingface_hub import InferenceClient """ For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference """ client = InferenceClient("HuggingFaceH4/zephyr-7b-beta") def respond( message, history: list[tuple[str, str]], system_message, max_tokens, temperature, top_p, ): system_message = "you are doing exercises to stay fit! Whether it's cardio for your heart health, strength training for muscles, or flexibility exercises for mobility." 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 = "" 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 """ For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface """ demo = gr.ChatInterface( respond, additional_inputs=[ gr.Textbox(value = "you are doing exercises to stay fit! Whether it's cardio for your heart health, strength training for muscles, or flexibility exercises for mobility.", 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)", ), ], examples = [ ["I want to gain weight in 1 week."], ["Can you guide me through a quick phyisal exercises?"], ["what should be my diet for the whole weekl?"] ], title = 'Stay fit plan 🕊️' ) if __name__ == "__main__": demo.launch()