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| import gradio as gr | |
| from huggingface_hub import InferenceClient | |
| import random | |
| client = InferenceClient("HuggingFaceH4/zephyr-7b-beta") | |
| # Initialize conversation state | |
| conversation_state = {"ask_question": False, "last_message": ""} | |
| def respond( | |
| message, | |
| history: list[tuple[str, str]], | |
| system_message, | |
| max_tokens, | |
| temperature, | |
| top_p, | |
| ): | |
| global conversation_state | |
| 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}) | |
| # Check if the chatbot should pose a question based on the user's previous response | |
| if conversation_state["ask_question"]: | |
| conversation_state["ask_question"] = False | |
| question = pose_follow_up_question(conversation_state["last_message"]) | |
| messages.append({"role": "assistant", "content": question}) | |
| 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 | |
| # Update conversation state with the last user message | |
| conversation_state["last_message"] = message.choices[0].delta.content | |
| # Check if the chatbot should ask a question based on the current response | |
| if "ask a question" in token.lower(): | |
| conversation_state["ask_question"] = True | |
| yield response | |
| def pose_follow_up_question(user_response): | |
| # Example follow-up questions based on user responses | |
| follow_up_questions = { | |
| "I believe scientists should prioritize ethical considerations in their research": | |
| "That's great! What do you think are some specific ethical considerations scientists should keep in mind?", | |
| "I'm not sure about the ethical implications of genetic engineering": | |
| "It's okay! Genetic engineering can be complicated. What aspects of it are you uncertain about?", | |
| "I think technology has the potential to both benefit and harm society": | |
| "You're absolutely right! How do you think society can balance the benefits and risks of emerging technologies?" | |
| } | |
| return follow_up_questions.get(user_response, "Could you tell me more about your thoughts on this topic?") | |
| # Gradio interface definition | |
| demo = gr.ChatInterface( | |
| respond, | |
| 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() | |