app.py

import streamlit as st
import yfinance as yf
import pandas as pd
from groq import Groq
import os
from duckduckgo_search import DDGS
import json

class FinancialIntelligentAgent:
    def __init__(self, api_key):
        self.groq_client = Groq(api_key=api_key)
        self.tools = {
            "stock_info": self.get_stock_info,
            "news_search": self.get_duckduckgo_news,
            "market_sentiment": self.analyze_market_sentiment,
            "risk_assessment": self.assess_investment_risk
        }

    def get_stock_info(self, symbol):
        """Retrieve comprehensive stock information"""
        try:
            stock = yf.Ticker(symbol)
            info = stock.info
            return {
                "basic_info": {
                    "Company Name": info.get('longName', 'N/A'),
                    "Current Price": f"${info.get('currentPrice', 'N/A'):.2f}",
                    "Market Cap": f"${info.get('marketCap', 'N/A'):,}",
                    "Sector": info.get('sector', 'N/A'),
                    "Industry": info.get('industry', 'N/A')
                },
                "financial_metrics": {
                    "PE Ratio": info.get('trailingPE', 'N/A'),
                    "Dividend Yield": f"{info.get('dividendYield', 'N/A'):.2%}",
                    "52 Week Range": f"${info.get('fiftyTwoWeekLow', 'N/A')} - ${info.get('fiftyTwoWeekHigh', 'N/A')}"
                }
            }
        except Exception as e:
            return {"error": str(e)}

    def get_duckduckgo_news(self, symbol, limit=5):
        """Search and retrieve news about the stock"""
        try:
            with DDGS() as ddgs:
                news_results = list(ddgs.news(f"{symbol} stock recent news", max_results=limit))
                return [
                    {
                        "title": result.get('title', 'N/A'),
                        "link": result.get('url', ''),
                        "source": result.get('source', 'N/A')
                    } for result in news_results
                ]
        except Exception as e:
            return [{"error": str(e)}]

    def analyze_market_sentiment(self, symbol, news):
        """Analyze market sentiment based on news"""
        try:
            news_context = "\n".join([news['title'] for news in news])
            response = self.groq_client.chat.completions.create(
                model="llama3-70b-8192",
                messages=[
                    {"role": "system", "content": "You are a market sentiment analyst."},
                    {"role": "user", "content": f"Analyze the market sentiment for {symbol} based on these news headlines:\n{news_context}"}
                ]
            )
            return response.choices[0].message.content
        except Exception as e:
            return f"Sentiment analysis error: {e}"

    def assess_investment_risk(self, stock_info):
        """Perform comprehensive risk assessment"""
        try:
            stock_context = json.dumps(stock_info, indent=2)
            response = self.groq_client.chat.completions.create(
                model="llama3-70b-8192",
                messages=[
                    {"role": "system", "content": "You are a risk assessment expert in financial investments."},
                    {"role": "user", "content": f"Conduct a detailed investment risk assessment based on these stock details:\n{stock_context}"}
                ]
            )
            return response.choices[0].message.content
        except Exception as e:
            return f"Risk assessment error: {e}"

    def generate_comprehensive_analysis(self, symbol):
        """Generate a comprehensive financial analysis using multiple tools"""
        # Autonomous tool selection and execution
        analysis_results = {}
        
        # Step 1: Gather Stock Information
        analysis_results['stock_info'] = self.tools['stock_info'](symbol)
        
        # Step 2: Retrieve News
        analysis_results['news'] = self.tools['news_search'](symbol)
        
        # Step 3: Analyze Market Sentiment
        analysis_results['market_sentiment'] = self.tools['market_sentiment'](
            symbol, 
            analysis_results['news']
        )
        
        # Step 4: Assess Investment Risk
        analysis_results['risk_assessment'] = self.tools['risk_assessment'](
            analysis_results['stock_info']
        )
        
        return analysis_results

# Streamlit Application
def main():
    st.title("🤖 Agentic AI Financial Analyst")
    st.markdown("Advanced autonomous financial intelligence platform")

    # API Key Management
    groq_api_key = st.secrets.get("GROQ_API_KEY") or os.getenv("GROQ_API_KEY")
    
    if not groq_api_key:
        st.error("Groq API Key is required!")
        return

    # Initialize Intelligent Agent
    agent = FinancialIntelligentAgent(groq_api_key)

    # Stock Symbol Input
    stock_symbol = st.text_input(
        "Enter Stock Symbol", 
        value="NVDA", 
        help="Enter a valid stock ticker"
    )

    # Analysis Button
    if st.button("Generate Comprehensive Analysis"):
        with st.spinner("Autonomous analysis in progress..."):
            try:
                # Execute Agentic Analysis
                analysis_results = agent.generate_comprehensive_analysis(stock_symbol)

                # Display Stock Information
                st.subheader(f"📊 Stock Overview: {stock_symbol}")
                st.json(analysis_results['stock_info'])

                # Display News
                st.subheader("📰 Recent News")
                for news in analysis_results['news']:
                    st.markdown(f"**{news.get('title', 'N/A')}**")
                    st.markdown(f"[Read more]({news.get('link', '#')})")
                    st.markdown("---")

                # Display Market Sentiment
                st.subheader("📈 Market Sentiment Analysis")
                st.write(analysis_results['market_sentiment'])

                # Display Risk Assessment
                st.subheader("⚖️ Investment Risk Assessment")
                st.write(analysis_results['risk_assessment'])

            except Exception as e:
                st.error(f"Analysis failed: {e}")

    st.sidebar.warning(
        "🚨 Disclaimer: AI-generated insights. "
        "Consult financial professionals for decisions."
    )

if __name__ == "__main__":
    main()