import gradio as gr from huggingface_hub import InferenceClient client = InferenceClient("HuggingFaceH4/zephyr-7b-beta") def _history_to_messages(history): messages = [] if not history: return messages # Gradio 5 uses list[dict]; older ChatInterface used list[tuple] if isinstance(history[0], dict): for item in history: role = item.get("role") content = item.get("content") if role in {"user", "assistant"} and content: messages.append({"role": role, "content": content}) return messages for user_msg, assistant_msg in history: if user_msg: messages.append({"role": "user", "content": user_msg}) if assistant_msg: messages.append({"role": "assistant", "content": assistant_msg}) return messages def respond(message, history, system_message, max_tokens, temperature, top_p): messages = [{"role": "system", "content": system_message}] messages.extend(_history_to_messages(history)) messages.append({"role": "user", "content": message}) response = "" try: for chunk in client.chat_completion( messages, max_tokens=int(max_tokens), stream=True, temperature=temperature, top_p=top_p, ): token = chunk.choices[0].delta.content if token: response += token yield response except Exception as exc: yield f"Model call failed: {exc}. Add HF_TOKEN in Space secrets if needed." demo = gr.ChatInterface( respond, additional_inputs=[ gr.Textbox( value="You are a friendly chatbot. Speak as if you are age n and a given sex. Example: n = 17 and sex = male", label="System message", ), gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"), gr.Slider(minimum=0.1, maximum=2.0, 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"), ], ) if __name__ == "__main__": demo.launch()