Update app.py

app.py

@@ -3,6 +3,8 @@ import yfinance as yf
 import pandas as pd
 from groq import Groq
 import os
+import requests
+from duckduckgo_search import DDGS
 
 # Streamlit App Configuration
 st.set_page_config(
@@ -52,31 +54,62 @@ def get_stock_info(symbol):
         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, query_type):
+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
-        context = "\n".join([f"{k}: {v}" for k, v in stock_info.items()])
+        stock_context = "\n".join([f"{k}: {v}" for k, v in stock_info.items()])
+        
+        # Prepare news context
+        news_context = "Recent News:\n" + "\n".join([
+            f"- {news['title']} (Source: {news['publisher']})"
+            for news in news
+        ])
+        
+        # Full context
+        full_context = f"{stock_context}\n\n{news_context}"
         
         # Generate prompt based on query type
         if query_type == "Analyst Recommendations":
-            prompt = f"Provide a professional analysis of the stock's potential based on these details:\n{context}\nFocus on analyst recommendations and future outlook."
-        elif query_type == "Company Overview":
-            prompt = f"Give a comprehensive overview of the company based on these financial details:\n{context}\nHighlight key strengths and potential challenges."
-        elif query_type == "Investment Potential":
-            prompt = f"Analyze the investment potential of this stock using these financial metrics:\n{context}\nProvide insights on potential growth and risks."
+            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"Provide a detailed financial analysis of the stock using these details:\n{context}"
+            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 detailed stock insights."},
+                {"role": "system", "content": "You are a professional financial analyst providing nuanced stock insights."},
                 {"role": "user", "content": prompt}
             ]
         )
@@ -87,8 +120,8 @@ def generate_ai_analysis(stock_info, query_type):
 
 # Main Streamlit App
 def main():
-    st.title("🚀 Financial Insight AI")
-    st.markdown("Get comprehensive stock analysis powered by AI")
+    st.title("🚀 Advanced Financial Insight AI")
+    st.markdown("Comprehensive stock analysis with DuckDuckGo news search")
 
     # Sidebar Configuration
     st.sidebar.header("🔍 Stock Analysis")
@@ -104,15 +137,15 @@ def main():
     query_type = st.sidebar.selectbox(
         "Select Analysis Type",
         [
-            "Company Overview",
+            "Comprehensive Analysis",
             "Analyst Recommendations",
-            "Investment Potential"
+            "Latest News Analysis"
         ]
     )
 
     # Generate Analysis Button
     if st.sidebar.button("Generate Analysis"):
-        with st.spinner("Analyzing stock data..."):
+        with st.spinner("Fetching and analyzing stock data..."):
             try:
                 # Fetch Stock Information
                 stock_info = get_stock_info(stock_symbol)
@@ -123,8 +156,19 @@ def main():
                     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("📰 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("---")
+                
                 # Generate AI Analysis
-                ai_analysis = generate_ai_analysis(stock_info, query_type)
+                ai_analysis = generate_ai_analysis(stock_info, real_time_news, query_type)
                 
                 # Display AI Analysis
                 st.subheader("🤖 AI-Powered Insights")