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import gradio as gr
from basic_llama_agent import BasicLammaAgent
agent_instance = BasicLammaAgent()
async def llmResponse(message, *args):
return await agent_instance(message)
agent_chat = gr.ChatInterface(
llmResponse,
title="Personalized News Agent",
description=(
"A conversational agent that helps you discover and analyze news on topics of your interest. "
"You can:\n"
"- Get the latest news articles for your query\n"
"- Ask for implications of a news article\n"
"- Request background events leading up to a news story\n"
"- Explore summarized social media reactions (positive/negative) to news events\n\n"
"The agent uses tools for news retrieval, implication generation, event chronology, and social sentiment analysis."
),
type="messages"
)
info_tab = gr.Blocks()
with info_tab:
gr.Markdown("# Personalized News Agent")
gr.Markdown(
"A conversational agent that helps you discover and analyze news on topics of your interest. "
"You can:\n"
"- Get the latest news articles for your query\n"
"- Ask for implications of a news article\n"
"- Request background events leading up to a news story\n"
"- Explore summarized social media reactions (positive/negative) to news events\n\n"
"The agent uses tools for news retrieval, implication generation, event chronology, and social sentiment analysis.")
gr.HTML("""
<iframe width="560" height="315" src="https://www.youtube.com/embed/jFagRX6-I2E?si=i7Ss9zNHRAL0Xnfi" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen></iframe>
""")
gr.Image(value="agent_diagram.png", label="Agent Workflow Diagram")
demo = gr.TabbedInterface([info_tab, agent_chat], ["Info", "Agent Chat"])
if __name__ == "__main__":
demo.launch()
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