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979dbb7 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 | import os
import gradio as gr
from dotenv import load_dotenv
import asyncio
# Import the new deep research logic
from research_agent import deep_research, ResearchReport
# Load environment variables
load_dotenv()
async def interact(user_message, history):
"""
Handle user interaction for Deep Research.
"""
if not user_message:
yield history, ""
return
# Append user message to Gradio history
history = history or []
history.append({"role": "user", "content": user_message})
# Yield initial state
yield history, ""
# Call the deep_research generator
# It yields status strings (progress updates) and finally a ResearchReport object
async for update in deep_research(user_message):
if isinstance(update, str):
# It's a status update
# We can show this as a temporary system message or just log it
# For a chat interface, we can append a system message that updates
if history[-1]["role"] != "assistant":
history.append({"role": "assistant", "content": update})
else:
history[-1]["content"] = update
yield history, ""
elif isinstance(update, ResearchReport):
# Final report
report_md = f"# Research Report: {user_message}\n\n"
report_md += f"## Executive Summary\n{update.executive_summary}\n\n"
for section in update.sections:
report_md += f"### {section.title}\n{section.content}\n\n"
if section.sources:
report_md += "**Sources:**\n" + "\n".join([f"- {s}" for s in section.sources]) + "\n\n"
report_md += f"## Risks & Uncertainties\n{update.risks_uncertainties}\n\n"
report_md += "## What to Watch Next\n" + "\n".join([f"- {item}" for item in update.what_to_watch_next])
# Replace the last status message with the final report
history[-1]["content"] = report_md
yield history, ""
# Create the Gradio Interface
with gr.Blocks() as demo:
gr.Markdown("# Pydantic AI Deep Research Agent")
gr.Markdown("Enter a stock ticker (e.g., NVDA) or a research topic to generate a detailed report.")
chatbot = gr.Chatbot(label="Agent", height=700)
msg = gr.Textbox(placeholder="Enter ticker or topic...", label="Research Query")
# Submit handler
msg.submit(
interact,
inputs=[msg, chatbot],
outputs=[chatbot, msg]
)
if __name__ == "__main__":
demo.launch()
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