Update app.py

app.py

@@ -1,41 +1,30 @@
 import streamlit as st
 import yfinance as yf
 import pandas as pd
+import plotly.graph_objs as plt
 from groq import Groq
 import os
-import requests
+from dotenv import load_dotenv
 from duckduckgo_search import DDGS
 
-# Streamlit App Configuratio
+# Load environment variables from .env file
+load_dotenv()
+
+# Streamlit App Configuration
 st.set_page_config(
     page_title="Financial Analysis AI Agent",
     page_icon="💹",
-    layout="centered"
+    layout="wide"
 )
 
-# Custom CSS for center alignment
-st.markdown("""
-    <style>
-    .reportview-container .main .block-container {
-        text-align: center;
-    }
-    .stTextInput > div > div > input {
-        text-align: center;
-    }
-    .stSelectbox > div > div > div {
-        text-align: center;
-    }
-    </style>
-""", unsafe_allow_html=True)
-
 # 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")
+        # Try to get API key from environment variables
+        groq_api_key = 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.")
+            st.error("Groq API Key is missing. Please set GROQ_API_KEY in your .env file.")
             return None
         
         return Groq(api_key=groq_api_key)
@@ -69,6 +58,74 @@ def get_stock_info(symbol):
         st.error(f"Error fetching stock information: {e}")
         return None
 
+# Fetch Historical Stock Data and Calculate Volatility
+def get_stock_volatility(symbol, period='1y'):
+    try:
+        # Download historical stock data
+        stock_data = yf.download(symbol, period=period)
+        
+        if stock_data.empty:
+            st.error(f"No historical data found for {symbol}")
+            return None
+        
+        # Calculate daily returns
+        stock_data['Daily Returns'] = stock_data['Close'].pct_change()
+        
+        # Calculate rolling volatility (30-day standard deviation of returns)
+        stock_data['Rolling Volatility'] = stock_data['Daily Returns'].rolling(window=30).std() * (252 ** 0.5)  # Annualized
+        
+        return stock_data
+    except Exception as e:
+        st.error(f"Error fetching historical stock data: {e}")
+        return None
+
+# Create Volatility Visualization
+def create_volatility_chart(stock_data):
+    try:
+        # Create figure with two y-axes
+        fig = plt.Figure()
+        
+        # Price line
+        fig.add_trace(
+            plt.Scatter(
+                x=stock_data.index, 
+                y=stock_data['Close'], 
+                name='Stock Price', 
+                line=dict(color='blue'),
+                yaxis='y1'
+            )
+        )
+        
+        # Volatility line
+        fig.add_trace(
+            plt.Scatter(
+                x=stock_data.index, 
+                y=stock_data['Rolling Volatility'], 
+                name='30-Day Volatility', 
+                line=dict(color='red'),
+                yaxis='y2'
+            )
+        )
+        
+        # Layout configuration
+        fig.update_layout(
+            title=f'Stock Price and Volatility',
+            xaxis_title='Date',
+            yaxis_title='Stock Price',
+            yaxis2=dict(
+                title='Annualized Volatility',
+                overlaying='y',
+                side='right'
+            ),
+            height=600,
+            legend=dict(x=0, y=1.1, orientation='h')
+        )
+        
+        return fig
+    except Exception as e:
+        st.error(f"Error creating volatility chart: {e}")
+        return None
+
 # Fetch News Using DuckDuckGo
 def get_duckduckgo_news(symbol, limit=5):
     try:
@@ -138,68 +195,74 @@ def main():
     st.title("🚀 Advanced Financial Insight AI")
     st.markdown("Comprehensive stock analysis with DuckDuckGo news search")
 
-    # Center columns for inputs
-    col1, col2, col3 = st.columns([1,2,1])
+    # Sidebar Configuration
+    st.sidebar.header("🔍 Stock Analysis")
     
-    with col2:
-        # Stock Symbol Input
-        stock_symbol = st.text_input(
-            "Enter Stock Symbol", 
-            value="NVDA", 
-            help="Enter a valid stock ticker (e.g., AAPL, GOOGL)"
-        )
+    # Stock Symbol Input
+    stock_symbol = st.sidebar.text_input(
+        "Enter Stock Symbol", 
+        value="NVDA", 
+        help="Enter a valid stock ticker (e.g., AAPL, GOOGL)"
+    )
 
-        # Analysis Type Selection
-        query_type = st.selectbox(
-            "Select Analysis Type",
-            [
-                "Comprehensive Analysis",
-                "Analyst Recommendations",
-                "Latest News Analysis"
-            ]
-        )
+    # Analysis Type Selection
+    query_type = st.sidebar.selectbox(
+        "Select Analysis Type",
+        [
+            "Comprehensive Analysis",
+            "Analyst Recommendations",
+            "Latest News Analysis"
+        ]
+    )
 
-        # Generate Analysis Button
-        if st.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("📰 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, real_time_news, query_type)
-                    
-                    # Display AI Analysis
-                    st.subheader("🤖 AI-Powered Insights")
-                    st.write(ai_analysis)
-                    
-                except Exception as e:
-                    st.error(f"An error occurred: {e}")
+    # 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 and Display Volatility Chart
+                volatility_data = get_stock_volatility(stock_symbol)
+                if volatility_data is not None:
+                    # Create and Display Volatility Chart
+                    st.subheader("📊 Stock Price and Volatility")
+                    volatility_chart = create_volatility_chart(volatility_data)
+                    st.plotly_chart(volatility_chart, use_container_width=True)
+                
+                # 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, real_time_news, query_type)
+                
+                # Display AI Analysis
+                st.subheader("🤖 AI-Powered Insights")
+                st.write(ai_analysis)
+                
+            except Exception as e:
+                st.error(f"An error occurred: {e}")
 
     # Disclaimer
-    st.markdown("<div style='text-align: center; margin-top: 20px;'>", unsafe_allow_html=True)
-    st.warning(
+    st.sidebar.markdown("---")
+    st.sidebar.warning(
         "🚨 Disclaimer: This is an AI-generated analysis. "
         "Always consult with a financial advisor before making investment decisions."
     )
-    st.markdown("</div>", unsafe_allow_html=True)
 
 # Run the Streamlit app
 if __name__ == "__main__":