Update app.py

app.py

@@ -1,6 +1,6 @@
 import streamlit as st
-import requests
 import yfinance as yf
+import pandas as pd
 from groq import Groq
 import os
 
@@ -14,10 +14,13 @@ st.set_page_config(
 # 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}")
@@ -31,23 +34,20 @@ def get_stock_info(symbol):
         # Fetch key information
         info = stock.info
         
-        # Fetch analyst recommendations
-        recommendations = stock.recommendations
-        
-        # Fetch recent news
-        news = stock.news[:5]  # Get last 5 news items
-        
-        return {
-            "basic_info": {
-                "Company Name": info.get('longName', 'N/A'),
-                "Sector": info.get('sector', 'N/A'),
-                "Industry": info.get('industry', 'N/A'),
-                "Market Cap": f"${info.get('marketCap', 'N/A'):,}",
-                "Current Price": f"${info.get('currentPrice', 'N/A'):.2f}"
-            },
-            "recommendations": recommendations,
-            "news": news
+        # 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')
         }
+        
+        return stock_data
     except Exception as e:
         st.error(f"Error fetching stock information: {e}")
         return None
@@ -60,28 +60,23 @@ def generate_ai_analysis(stock_info, query_type):
     
     try:
         # Prepare context for AI
-        context = f"""Stock Information:
-Company: {stock_info['basic_info'].get('Company Name', 'N/A')}
-Sector: {stock_info['basic_info'].get('Sector', 'N/A')}
-Market Cap: {stock_info['basic_info'].get('Market Cap', 'N/A')}
-Current Price: {stock_info['basic_info'].get('Current Price', 'N/A')}
-        """
+        context = "\n".join([f"{k}: {v}" for k, v in stock_info.items()])
         
         # Generate prompt based on query type
         if query_type == "Analyst Recommendations":
-            prompt = f"{context}\n\nProvide a detailed analysis of the recent analyst recommendations for this stock."
-        elif query_type == "Latest Company News":
-            prompt = f"{context}\n\nSummarize the key points from the latest company news and their potential impact on the stock."
-        elif query_type == "Stock Fundamentals":
-            prompt = f"{context}\n\nProvide an in-depth analysis of the stock's fundamental financial health."
+            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."
         else:
-            prompt = f"{context}\n\nGenerate a comprehensive overview of the stock, including its market position, recent performance, and potential outlook."
+            prompt = f"Provide a detailed financial analysis of the stock using these details:\n{context}"
         
         # Generate response using Groq
         response = client.chat.completions.create(
             model="llama3-70b-8192",
             messages=[
-                {"role": "system", "content": "You are a financial analyst providing professional stock insights."},
+                {"role": "system", "content": "You are a professional financial analyst providing detailed stock insights."},
                 {"role": "user", "content": prompt}
             ]
         )
@@ -90,60 +85,59 @@ Current Price: {stock_info['basic_info'].get('Current Price', 'N/A')}
     except Exception as e:
         return f"Error generating AI analysis: {e}"
 
-# Streamlit App Main Function
+# Main Streamlit App
 def main():
-    st.title("🤖 Financial Analysis AI Agent")
-    st.write("Get comprehensive financial insights using AI-powered analysis!")
+    st.title("🚀 Financial Insight AI")
+    st.markdown("Get comprehensive stock analysis powered by AI")
 
-    # Sidebar for configuration
-    st.sidebar.header("🔧 Query Configuration")
+    # Sidebar Configuration
+    st.sidebar.header("🔍 Stock Analysis")
     
     # Stock Symbol Input
     stock_symbol = st.sidebar.text_input(
         "Enter Stock Symbol", 
         value="NVDA", 
-        help="Enter a valid stock ticker symbol"
+        help="Enter a valid stock ticker (e.g., AAPL, GOOGL)"
     )
 
-    # Query Type Selection
+    # Analysis Type Selection
     query_type = st.sidebar.selectbox(
         "Select Analysis Type",
         [
-            "Comprehensive Financial Overview",
+            "Company Overview",
             "Analyst Recommendations",
-            "Latest Company News",
-            "Stock Fundamentals"
+            "Investment Potential"
         ]
     )
 
-    # Submit Button
+    # Generate Analysis Button
     if st.sidebar.button("Generate Analysis"):
-        with st.spinner("Analyzing financial data..."):
+        with st.spinner("Analyzing stock data..."):
             try:
                 # Fetch Stock Information
                 stock_info = get_stock_info(stock_symbol)
                 
                 if stock_info:
-                    # Display Basic Stock Information
-                    st.subheader(f"Basic Information for {stock_symbol}")
-                    info_df = pd.DataFrame.from_dict(stock_info['basic_info'], orient='index', columns=['Value'])
+                    # 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)
                 
                 # Generate AI Analysis
                 ai_analysis = generate_ai_analysis(stock_info, query_type)
                 
                 # Display AI Analysis
-                st.subheader("AI-Powered Analysis")
+                st.subheader("🤖 AI-Powered Insights")
                 st.write(ai_analysis)
                 
             except Exception as e:
                 st.error(f"An error occurred: {e}")
 
-    # Footer
+    # Disclaimer
     st.sidebar.markdown("---")
-    st.sidebar.info(
-        "💡 Tip: Use this tool to get quick financial insights. "
-        "Always verify important financial decisions independently."
+    st.sidebar.warning(
+        "🚨 Disclaimer: This is an AI-generated analysis. "
+        "Always consult with a financial advisor before making investment decisions."
     )
 
 # Run the Streamlit app