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import gradio as gr
from comp import generate_response
import re
# --- Constants ---
WORKFLOW_SYSTEM_PROMPT = """You are an expert in analyzing conversations and extracting user workflows.
Based on the provided chat history, identify the user's main goal or intent.
Then, break down the conversation into a series of actionable steps that represent the workflow to achieve that goal.
The output should be in two parts, clearly separated:
**Intent**: [A concise description of the user's goal]
**Steps**:
[A numbered list of steps]
"""
# --- 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 "Could not determine intent."
steps = steps_match.group(1).strip() if steps_match else "Could not determine steps."
return intent, steps
# --- Gradio UI ---
with gr.Blocks() as demo:
gr.Markdown("# Ling Playground")
with gr.Row():
with gr.Column(scale=2):
gr.Markdown("## Chat")
chat_chatbot = gr.Chatbot(label="Chat", bubble_full_width=False)
chat_msg = gr.Textbox(label="Your Message")
with gr.Column(scale=1):
gr.Markdown("## Workflow Extraction")
intent_textbox = gr.Textbox(label="Task Intent", interactive=False)
steps_textbox = gr.Textbox(
label="Extracted Steps", interactive=False, lines=15
)
chat_clear = gr.ClearButton([chat_msg, chat_chatbot, intent_textbox, steps_textbox])
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
( 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])
)
if __name__ == "__main__":
demo.launch(share=True) |