app.py

import gradio as gr
from comp import generate_response
import re

# --- Constants ---
WORKFLOW_SYSTEM_PROMPT = """你是一位分析对话和提取用户工作流的专家。
根据提供的聊天记录，识别用户的核心目标或意图。
然后，将对话分解为一系列可执行的步骤，以实现该目标。
输出应分为两部分，并明确分隔：
**意图**: [用户目标的简洁描述]
**步骤**:
[步骤的编号列表]
"""

# --- Helper Functions ---
def parse_workflow_response(response):
    intent_match = re.search(r"\*\*Intent\*\*:\s*(.*)", response, re.IGNORECASE)
    steps_match = re.search(r"\*\*Steps\*\*:\s*(.*)", response, re.DOTALL | re.IGNORECASE)

    intent = intent_match.group(1).strip() if intent_match else "未能识别意图。"
    steps = steps_match.group(1).strip() if steps_match else "未能识别步骤。"
    
    return intent, steps

# --- Gradio UI ---

with gr.Blocks() as demo:
    gr.Markdown("# Ling 灵动工作台")
    gr.Markdown("这是一个对 Zero GPU 使用 Ring-mini-2.0 模型能力的验证项目。它会和用户聊天，并实时提取其中潜在有用的工作流。在合适的时机，它会告知用户，并提醒这些工作流未来可以被复用。")

    with gr.Row():
        with gr.Column(scale=2):
            gr.Markdown("## 聊天")
            chat_chatbot = gr.Chatbot(label="聊天", bubble_full_width=False)
            with gr.Row():
                chat_msg = gr.Textbox(
                    label="请输入你的消息",
                    scale=4,
                )
                send_btn = gr.Button("发送", scale=1)
            
        with gr.Column(scale=1):
            gr.Markdown("## 工作流提取")
            intent_textbox = gr.Textbox(label="任务意图", interactive=False)
            steps_textbox = gr.Textbox(
                label="提取步骤", interactive=False, lines=15
            )
    
    chat_clear = gr.ClearButton([chat_msg, chat_chatbot, intent_textbox, steps_textbox], value="清除")

    def user(user_message, history):
        return "", history + [[user_message, None]]

    def bot(history):
        user_message = history[-1][0]
        history[-1][1] = ""
        # Main chat model call (uses default system prompt)
        for response in generate_response(user_message, history[:-1]):
            if "</think>" in response:
                parts = response.split("</think>", 1)
                thinking_text = parts[0].replace("<think>", "")
                body_text = parts[1]

                md_output = f"**Thinking...**\n```\n{thinking_text}\n```\n\n{body_text}"
                history[-1][1] = md_output
            else:
                history[-1][1] = response
            yield history

    def update_workflow(history):
        if not history or not history[-1][0]:
            return "", ""

        # The last user message is the main prompt for the workflow agent
        user_message = history[-1][0]
        # The rest of the conversation is the history
        chat_history_for_workflow = history[:-1]

        # Call the model with the workflow system prompt
        full_response = ""
        for response in generate_response(
            user_message,
            chat_history_for_workflow,
            system_prompt=WORKFLOW_SYSTEM_PROMPT
        ):
            full_response = response
        
        intent, steps = parse_workflow_response(full_response)
        return intent, steps

    # Handler for pressing Enter in the textbox
    (   chat_msg.submit(user, [chat_msg, chat_chatbot], [chat_msg, chat_chatbot], queue=False)
        .then(bot, chat_chatbot, chat_chatbot)
        .then(update_workflow, chat_chatbot, [intent_textbox, steps_textbox])
    )

    # Handler for clicking the Send button
    (   send_btn.click(user, [chat_msg, chat_chatbot], [chat_msg, chat_chatbot], queue=False)
        .then(bot, chat_chatbot, chat_chatbot)
        .then(update_workflow, chat_chatbot, [intent_textbox, steps_textbox])
    )

if __name__ == "__main__":
    demo.launch(share=True)