| from huggingface_hub import InferenceClient | |
| import gradio as gr | |
| client = InferenceClient() | |
| def respond( | |
| prompt: str, | |
| history, | |
| ): | |
| if not history: | |
| history = [{"role": "system", "content": "You are a friendly chatbot"}] | |
| history.append({"role": "user", "content": prompt}) | |
| yield history | |
| response = {"role": "assistant", "content": ""} | |
| for message in client.chat_completion( | |
| history, | |
| temperature=0.95, | |
| top_p=0.9, | |
| max_tokens=512, | |
| stream=True, | |
| model="HuggingFaceH4/zephyr-7b-beta" | |
| ): | |
| response["content"] += message.choices[0].delta.content or "" | |
| yield history + [response] | |
| with gr.Blocks() as demo: | |
| gr.Markdown("# Chat with Hugging Face Zephyr 7b 🤗") | |
| chatbot = gr.Chatbot( | |
| label="Agent", | |
| type="messages", | |
| avatar_images=( | |
| None, | |
| "https://em-content.zobj.net/source/twitter/376/hugging-face_1f917.png", | |
| ), | |
| ) | |
| prompt = gr.Textbox(max_lines=1, label="Chat Message") | |
| prompt.submit(respond, [prompt, chatbot], [chatbot]) | |
| prompt.submit(lambda: "", None, [prompt]) | |
| if __name__ == "__main__": | |
| demo.launch() |