app.py

import streamlit as st
from phi.agent import Agent
from phi.model.groq import Groq
from phi.tools.duckduckgo import DuckDuckGo
from phi.tools.yfinance import YFinanceTools
import os

# Streamlit App Configuration
st.set_page_config(
    page_title="Financial Analysis AI Agent",
    page_icon="💹",
    layout="wide"
)

# Web Search Agent
def create_web_search_agent():
    return Agent(
        name="Web Search Agent",
        role="Search the web for information",
        model=Groq(id="llama-3.2-3b-preview"),
        tools=[DuckDuckGo()],
        instructions=["Always include sources"],
        show_tools_call=True,
        markdown=True,
    )

# Financial Agent
def create_financial_agent():
    return Agent(
        name="Finance AI Agent",
        role="Analyze financial data and provide insights",
        model=Groq(id="llama-3.2-3b-preview"),
        tools=[
            YFinanceTools(
                stock_price=True, 
                analyst_recommendations=True, 
                stock_fundamentals=True,
                company_news=True
            )
        ],
        instructions=["Use tables to display the data"],
        show_tools_call=True,
        markdown=True,
    )

# Create Multi-Agent
multi_ai_agent = Agent(
    team=[create_web_search_agent(), create_financial_agent()],
    instructions=[
        "Always include sources", 
        "Use tables to display the data"
    ],
    show_tools_call=True,
    markdown=True,
)

# Streamlit App
def main():
    st.title("🤖 Financial Analysis AI Agent")
    st.write("Get comprehensive financial insights using AI-powered web search and analysis!")

    # Sidebar for configuration
    st.sidebar.header("🔧 Query Configuration")
    
    # Stock Symbol Input
    stock_symbol = st.sidebar.text_input(
        "Enter Stock Symbol", 
        value="NVDA", 
        help="Enter a valid stock ticker symbol"
    )

    # Query Type Selection
    query_type = st.sidebar.selectbox(
        "Select Analysis Type",
        [
            "Analyst Recommendations",
            "Latest Company News",
            "Stock Fundamentals",
            "Comprehensive Financial Overview"
        ]
    )

    # Generate Query
    if query_type == "Analyst Recommendations":
        query = f"Summarize analyst recommendations for {stock_symbol}"
    elif query_type == "Latest Company News":
        query = f"Provide the latest news for {stock_symbol}"
    elif query_type == "Stock Fundamentals":
        query = f"Analyze stock fundamentals for {stock_symbol}"
    else:
        query = f"Provide a comprehensive financial overview for {stock_symbol}"

    # Submit Button
    if st.sidebar.button("Generate Analysis"):
        with st.spinner("Analyzing financial data..."):
            # Capture the response
            response_container = st.container()
            with response_container:
                try:
                    # Stream the response
                    full_response = ""
                    response_placeholder = st.empty()
                    for chunk in multi_ai_agent.respond(query, stream=True):
                        full_response += chunk
                        response_placeholder.markdown(full_response)
                    
                    # Final display
                    st.success("Analysis Complete!")
                except Exception as e:
                    st.error(f"An error occurred: {e}")

    # Footer
    st.sidebar.markdown("---")
    st.sidebar.info(
        "💡 Tip: Use this tool to get quick financial insights. "
        "Always verify important financial decisions independently."
    )

# Run the Streamlit app
if __name__ == "__main__":
    main()