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("molbal/CRA-v1-7B") def respond( message, history: list[tuple[str, str]], system_message, max_tokens, temperature, top_p, ): messages = [ {"role": "system", "content": "You are a writer’s assistant."}, {"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, num_ctx=16384, repeat_penalty=1.05, ): 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="Understand how the story flows, what motivations the characters have and how they will interact with each other and the world as a step by step thought process before continuing the story.", 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=0.8, step=0.05, label="Top-p (nucleus sampling)", ), ], ) demo.launch() # Add an alert to mention that this runs on CPU gr.Markdown("**Note: This model runs on CPU, so it will be slow.**")