Update app.py

app.py

@@ -3,165 +3,187 @@ import yfinance as yf
 import pandas as pd
 from groq import Groq
 import os
+import requests
 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
+# Streamlit App Configuration
+st.set_page_config(
+    page_title="Financial Analysis AI Agent",
+    page_icon="💹",
+    layout="wide"
+)
+
+# Initialize Groq Client
+def get_groq_client():
+    try:
+        # Try to get API key from Hugging Face secrets first
+        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 missing. Please set it in Secrets or .env file.")
+            return None
+        
+        return Groq(api_key=groq_api_key)
+    except Exception as e:
+        st.error(f"Error initializing Groq client: {e}")
+        return None
+
+# Fetch Stock Information
+def get_stock_info(symbol):
+    try:
+        stock = yf.Ticker(symbol)
+        
+        # Fetch key information
+        info = stock.info
+        
+        # Create a structured dictionary of key financial metrics
+        stock_data = {
+            "Company Name": info.get('longName', 'N/A'),
+            "Current Price": f"${info.get('currentPrice', 'N/A'):.2f}",
+            "Market Cap": f"${info.get('marketCap', 'N/A'):,}",
+            "PE Ratio": info.get('trailingPE', 'N/A'),
+            "Dividend Yield": f"{info.get('dividendYield', 'N/A'):.2%}",
+            "52 Week High": f"${info.get('fiftyTwoWeekHigh', 'N/A'):.2f}",
+            "52 Week Low": f"${info.get('fiftyTwoWeekLow', 'N/A'):.2f}",
+            "Sector": info.get('sector', 'N/A'),
+            "Industry": info.get('industry', 'N/A')
         }
-
-    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)
+        return stock_data
+    except Exception as e:
+        st.error(f"Error fetching stock information: {e}")
+        return None
+
+# Fetch News Using DuckDuckGo
+def get_duckduckgo_news(symbol, limit=5):
+    try:
+        with DDGS() as ddgs:
+            # Search for recent news about the stock
+            news_results = list(ddgs.news(f"{symbol} stock recent news", max_results=limit))
+            
+            # Transform results to a consistent format
+            formatted_news = [
+                {
+                    "title": result.get('title', 'N/A'),
+                    "link": result.get('url', ''),
+                    "publisher": result.get('source', 'N/A'),
+                    "source": "DuckDuckGo"
+                } for result in news_results
+            ]
+            
+            return formatted_news
+    except Exception as e:
+        st.warning(f"DuckDuckGo news search error: {e}")
+        return []
+
+# Generate AI Analysis
+def generate_ai_analysis(stock_info, news, query_type):
+    client = get_groq_client()
+    if not client:
+        return "Unable to generate AI analysis due to client initialization error."
+    
+    try:
+        # Prepare context for AI
+        stock_context = "\n".join([f"{k}: {v}" for k, v in stock_info.items()])
         
-        # Step 2: Retrieve News
-        analysis_results['news'] = self.tools['news_search'](symbol)
+        # Prepare news context
+        news_context = "Recent News:\n" + "\n".join([
+            f"- {news['title']} (Source: {news['publisher']})"
+            for news in news
+        ])
         
-        # Step 3: Analyze Market Sentiment
-        analysis_results['market_sentiment'] = self.tools['market_sentiment'](
-            symbol, 
-            analysis_results['news']
-        )
+        # Full context
+        full_context = f"{stock_context}\n\n{news_context}"
         
-        # Step 4: Assess Investment Risk
-        analysis_results['risk_assessment'] = self.tools['risk_assessment'](
-            analysis_results['stock_info']
+        # Generate prompt based on query type
+        if query_type == "Analyst Recommendations":
+            prompt = f"Provide a comprehensive analysis of analyst recommendations for this stock. Consider the following details:\n{full_context}\n\nFocus on: current analyst ratings, price targets, and recent sentiment changes."
+        elif query_type == "Latest News Analysis":
+            prompt = f"Analyze the latest news and its potential impact on the stock. Consider these details:\n{full_context}\n\nProvide insights on how recent news might affect the stock's performance."
+        elif query_type == "Comprehensive Analysis":
+            prompt = f"Provide a holistic analysis of the stock, integrating financial metrics and recent news:\n{full_context}\n\nOffer a balanced perspective on investment potential."
+        else:
+            prompt = f"Generate a detailed financial and news-based analysis:\n{full_context}"
+        
+        # Generate response using Groq
+        response = client.chat.completions.create(
+            model="llama3-70b-8192",
+            messages=[
+                {"role": "system", "content": "You are a professional financial analyst providing nuanced stock insights."},
+                {"role": "user", "content": prompt}
+            ]
         )
         
-        return analysis_results
+        return response.choices[0].message.content
+    except Exception as e:
+        return f"Error generating AI analysis: {e}"
 
-# Streamlit Application
+# Main Streamlit App
 def main():
-    st.title("🤖 Agentic AI Financial Analyst")
-    st.markdown("Advanced autonomous financial intelligence platform")
+    st.title("🚀 Advanced Financial Insight AI")
+    st.markdown("Comprehensive stock analysis with DuckDuckGo news search")
 
-    # API Key Management
-    groq_api_key = st.secrets.get("GROQ_API_KEY") or os.getenv("GROQ_API_KEY")
+    # Sidebar Configuration
+    st.sidebar.header("🔍 Stock Analysis")
     
-    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(
+    stock_symbol = st.sidebar.text_input(
         "Enter Stock Symbol", 
         value="NVDA", 
-        help="Enter a valid stock ticker"
+        help="Enter a valid stock ticker (e.g., AAPL, GOOGL)"
     )
 
-    # 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'])
+    # Analysis Type Selection
+    query_type = st.sidebar.selectbox(
+        "Select Analysis Type",
+        [
+            "Comprehensive Analysis",
+            "Analyst Recommendations",
+            "Latest News Analysis"
+        ]
+    )
 
+    # Generate Analysis Button
+    if st.sidebar.button("Generate Analysis"):
+        with st.spinner("Fetching and analyzing stock data..."):
+            try:
+                # Fetch Stock Information
+                stock_info = get_stock_info(stock_symbol)
+                
+                if stock_info:
+                    # Display Stock Information
+                    st.subheader(f"Financial Snapshot: {stock_symbol}")
+                    info_df = pd.DataFrame.from_dict(stock_info, orient='index', columns=['Value'])
+                    st.table(info_df)
+                
+                # Fetch News via DuckDuckGo
+                real_time_news = get_duckduckgo_news(stock_symbol)
+                
                 # 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.subheader("📰 Latest News")
+                for news in real_time_news:
+                    st.markdown(f"**{news['title']}**")
+                    st.markdown(f"*Source: {news['publisher']}*")
+                    st.markdown(f"[Read more]({news['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'])
-
+                
+                # Generate AI Analysis
+                ai_analysis = generate_ai_analysis(stock_info, real_time_news, query_type)
+                
+                # Display AI Analysis
+                st.subheader("🤖 AI-Powered Insights")
+                st.write(ai_analysis)
+                
             except Exception as e:
-                st.error(f"Analysis failed: {e}")
+                st.error(f"An error occurred: {e}")
 
+    # Disclaimer
+    st.sidebar.markdown("---")
     st.sidebar.warning(
-        "🚨 Disclaimer: AI-generated insights. "
-        "Consult financial professionals for decisions."
+        "🚨 Disclaimer: This is an AI-generated analysis. "
+        "Always consult with a financial advisor before making investment decisions."
     )
 
+# Run the Streamlit app
 if __name__ == "__main__":
     main()