news_agent.py

import streamlit as st
from duckduckgo_search import DDGS
from swarm import Swarm, Agent
from datetime import datetime
from dotenv import load_dotenv

load_dotenv()
MODEL = "llama3.2:latest"
client = Swarm()

st.set_page_config(page_title="AI News Processor", page_icon="📰")
st.title("📰 News Inshorts Agent")

def search_news(topic):
    """Search for news articles using DuckDuckGo"""
    with DDGS() as ddg:
        results = ddg.text(f"{topic} news {datetime.now().strftime('%Y-%m')}", max_results=3)
        if results:
            news_results = "\n\n".join([
                f"Title: {result['title']}\nURL: {result['href']}\nSummary: {result['body']}" 
                for result in results
            ])
            return news_results
        return f"No news found for {topic}."

# Create specialized agents
search_agent = Agent(
    name="News Searcher",
    instructions="""
    You are a news search specialist. Your task is to:
    1. Search for the most relevant and recent news on the given topic
    2. Ensure the results are from reputable sources
    3. Return the raw search results in a structured format
    """,
    functions=[search_news],
    model=MODEL
)

synthesis_agent = Agent(
    name="News Synthesizer",
    instructions="""
    You are a news synthesis expert. Your task is to:
    1. Analyze the raw news articles provided
    2. Identify the key themes and important information
    3. Combine information from multiple sources
    4. Create a comprehensive but concise synthesis
    5. Focus on facts and maintain journalistic objectivity
    6. Write in a clear, professional style
    Provide a 2-3 paragraph synthesis of the main points.
    """,
    model=MODEL
)

summary_agent = Agent(
    name="News Summarizer",
    instructions="""
    You are an expert news summarizer combining AP and Reuters style clarity with digital-age brevity.

    Your task:
    1. Core Information:
       - Lead with the most newsworthy development
       - Include key stakeholders and their actions
       - Add critical numbers/data if relevant
       - Explain why this matters now
       - Mention immediate implications

    2. Style Guidelines:
       - Use strong, active verbs
       - Be specific, not general
       - Maintain journalistic objectivity
       - Make every word count
       - Explain technical terms if necessary

    Format: Create a single paragraph of 250-400 words that informs and engages.
    Pattern: [Major News] + [Key Details/Data] + [Why It Matters/What's Next]

    Focus on answering: What happened? Why is it significant? What's the impact?

    IMPORTANT: Provide ONLY the summary paragraph. Do not include any introductory phrases, 
    labels, or meta-text like "Here's a summary" or "In AP/Reuters style."
    Start directly with the news content.
    """,
    model=MODEL
)

def process_news(topic):
    """Run the news processing workflow"""
    with st.status("Processing news...", expanded=True) as status:
        # Search
        status.write("🔍 Searching for news...")
        search_response = client.run(
            agent=search_agent,
            messages=[{"role": "user", "content": f"Find recent news about {topic}"}]
        )
        raw_news = search_response.messages[-1]["content"]
        
        # Synthesize
        status.write("🔄 Synthesizing information...")
        synthesis_response = client.run(
            agent=synthesis_agent,
            messages=[{"role": "user", "content": f"Synthesize these news articles:\n{raw_news}"}]
        )
        synthesized_news = synthesis_response.messages[-1]["content"]
        
        # Summarize
        status.write("📝 Creating summary...")
        summary_response = client.run(
            agent=summary_agent,
            messages=[{"role": "user", "content": f"Summarize this synthesis:\n{synthesized_news}"}]
        )
        return raw_news, synthesized_news, summary_response.messages[-1]["content"]

# User Interface
topic = st.text_input("Enter news topic:", value="artificial intelligence")
if st.button("Process News", type="primary"):
    if topic:
        try:
            raw_news, synthesized_news, final_summary = process_news(topic)
            st.header(f"📝 News Summary: {topic}")
            st.markdown(final_summary)
        except Exception as e:
            st.error(f"An error occurred: {str(e)}")
    else:
        st.error("Please enter a topic!")