| import gradio as gr | |
| from gradio import ChatMessage | |
| import time | |
| sleep_time = 0.5 | |
| def simulate_thinking_chat(message, history): | |
| start_time = time.time() | |
| response = ChatMessage( | |
| content="", | |
| metadata={"title": "_Thinking_ step-by-step", "id": 0, "status": "pending"} | |
| ) | |
| yield response | |
| thoughts = [ | |
| "First, I need to understand the core aspects of the query...", | |
| "Now, considering the broader context and implications...", | |
| "Analyzing potential approaches to formulate a comprehensive answer...", | |
| "Finally, structuring the response for clarity and completeness..." | |
| ] | |
| accumulated_thoughts = "" | |
| for thought in thoughts: | |
| time.sleep(sleep_time) | |
| accumulated_thoughts += f"- {thought}\n\n" | |
| response.content = accumulated_thoughts.strip() | |
| yield response | |
| response.metadata["status"] = "done" | |
| response.metadata["duration"] = time.time() - start_time | |
| yield response | |
| response = [ | |
| response, | |
| ChatMessage( | |
| content="Based on my thoughts and analysis above, my response is: This dummy repro shows how thoughts of a thinking LLM can be progressively shown before providing its final answer." | |
| ) | |
| ] | |
| yield response | |
| demo = gr.ChatInterface( | |
| simulate_thinking_chat, | |
| title="Thinking LLM Chat Interface 🤔", | |
| type="messages", | |
| ) | |
| if __name__ == "__main__": | |
| demo.launch() | |