feat: change UI appearance

.gitignore

@@ -1,4 +1,3 @@
 .env
 PLAN.md
-__pycache__/
-.streamlit/
+__pycache__/

.streamlit/config.toml

@@ -0,0 +1,26 @@
+[theme]
+primaryColor = "#3498db"
+backgroundColor = "#f9f9f9"
+secondaryBackgroundColor = "#ffffff"
+textColor = "#333333"
+font = "sans serif"
+
+[server]
+enableCORS = false
+enableXsrfProtection = true
+runOnSave = true
+maxUploadSize = 200
+headless = true
+
+[browser]
+gatherUsageStats = false
+serverAddress = "0.0.0.0"
+serverPort = 8501
+
+[runner]
+fastReruns = true
+
+[client]
+showErrorDetails = false
+caching = true
+displayEnabled = true 

.streamlit/custom.py

@@ -0,0 +1,130 @@
+import streamlit as st
+
+def apply_custom_css():
+    """Apply custom CSS to fix UI issues on Huggingface Spaces"""
+    st.markdown("""
+    <style>
+    /* Fix for stacking UI issues */
+    .element-container {
+        overflow-anchor: none !important;
+    }
+    
+    /* Fix for main content area */
+    .stApp {
+        min-height: 100vh;
+        overflow-y: auto;
+    }
+    
+    /* Improve sidebar styling */
+    .css-1d391kg {
+        padding-top: 2rem;
+    }
+    
+    /* Fix for scrolling issues */
+    .main .block-container {
+        max-width: 100%;
+        padding: 1rem;
+    }
+    
+    /* Hide empty elements */
+    .element-container:empty {
+        display: none;
+    }
+    
+    /* Custom tabs styling */
+    .stTabs [data-baseweb="tab-list"] {
+        gap: 2px;
+    }
+    
+    .stTabs [data-baseweb="tab"] {
+        height: 50px;
+        white-space: pre-wrap;
+        background-color: #F0F2F6;
+        border-radius: 4px 4px 0 0;
+        gap: 1px;
+        padding-top: 10px;
+        padding-bottom: 10px;
+    }
+    
+    .stTabs [aria-selected="true"] {
+        background-color: #FFFFFF !important;
+        border-bottom: 2px solid #4CAF50;
+    }
+    
+    /* Chat container styling */
+    [data-testid="stChatMessageContent"] {
+        background-color: #F0F2F6;
+        border-radius: 10px;
+        padding: 8px 12px;
+    }
+    
+    [data-testid="stChatMessage"] [data-testid="stChatMessageContent"] code {
+        background-color: rgba(0,0,0,0.1);
+    }
+    
+    /* Hide duplicate header after page switches */
+    .duplicated-header {
+        display: none;
+    }
+    
+    /* Fix for any layout issues specific to Huggingface */
+    @media screen and (max-width: 1200px) {
+        .row-widget.stButton {
+            width: 100%;
+        }
+    }
+    </style>
+    """, unsafe_allow_html=True)
+
+def apply_custom_js():
+    """Apply custom JavaScript to fix UI issues on Huggingface Spaces"""
+    st.markdown("""
+    <script>
+    // Function to clean up UI by removing duplicated elements
+    function cleanupUI() {
+        // Wait for the page to fully load
+        setTimeout(function() {
+            // Find headers that might be duplicated
+            const headers = document.querySelectorAll('h1, h2');
+            const seen = new Set();
+            
+            headers.forEach((header, index) => {
+                const text = header.innerText;
+                if (seen.has(text) && index > 0) {
+                    // Mark duplicates
+                    header.classList.add('duplicated-header');
+                    header.style.display = 'none';
+                }
+                seen.add(text);
+            });
+            
+            // Fix any scrolling issues
+            document.querySelector('.stApp').scrollTop = 0;
+        }, 1000);
+    }
+    
+    // Run cleanup when DOM is ready
+    document.addEventListener('DOMContentLoaded', cleanupUI);
+    
+    // Also run cleanup when Streamlit's internal events happen
+    const observer = new MutationObserver((mutations) => {
+        cleanupUI();
+    });
+    
+    // Start observing changes in the main container
+    window.addEventListener('load', () => {
+        const mainContainer = document.querySelector('.main');
+        if (mainContainer) {
+            observer.observe(mainContainer, { 
+                childList: true,
+                subtree: true 
+            });
+        }
+    });
+    </script>
+    """, unsafe_allow_html=True)
+
+def init():
+    """Initialize custom CSS and JS"""
+    apply_custom_css()
+    apply_custom_js() 

Home.py

@@ -3,48 +3,69 @@ import streamlit as st
 import os
 from dotenv import load_dotenv
 import google.generativeai as genai
+import sys
+
+# Add project root to path for imports
+sys.path.append(os.path.dirname(__file__))
+
+# Apply custom styling for Huggingface Space compatibility
+try:
+    from .streamlit.custom import init as apply_custom_styling
+    apply_custom_styling()
+except (ImportError, ModuleNotFoundError):
+    # Fallback for when running locally or if custom styling fails
+    pass
 
 # Load environment variables
 load_dotenv()
 
-# Page setup
+# Initialize session state if needed
+if "page_viewed" not in st.session_state:
+    st.session_state.page_viewed = True
+
+# Page setup - set this consistently across all pages
 st.set_page_config(
     page_title="AI Financial Dashboard",
     page_icon="📊",
-    layout="wide"
+    layout="wide",
+    initial_sidebar_state="expanded"
 )
 
-# Application title
-st.title("📊 AI Financial Dashboard v2.0")
-
-# Display application information
-st.markdown("""
-## Welcome to AI Financial Dashboard
+# Clear any previous runs to avoid stacking UI elements
+if hasattr(st, 'empty'):
+    placeholder = st.empty()
+    with placeholder.container():
+        # Application title
+        st.title("📊 AI Financial Dashboard v2.0")
+        
+        # Display application information
+        st.markdown("""
+        ## Welcome to AI Financial Dashboard
 
-This is an intelligent financial analysis application using AI to help you make smarter investment decisions.
+        This is an intelligent financial analysis application using AI to help you make smarter investment decisions.
 
-### Main Features:
+        ### Main Features:
 
-1. **💬 Chat with AI Financial Analyst**: 
-   - Search for stock information
-   - View price charts
-   - Convert currencies
- 
-2. **📄 In-depth Stock Analysis Report**:
-   - Comprehensive analysis of a specific stock
-   - Data collection from multiple sources
-   - Generate in-depth reports with AI evaluation
- 
-3. **📰 Daily Market Summary Newsletter** (Coming soon):
-   - Compilation of latest financial news
-   - Categorized by topic
-   - Daily market updates
+        1. **💬 Chat with AI Financial Analyst**: 
+           - Search for stock information
+           - View price charts
+           - Convert currencies
+         
+        2. **📄 In-depth Stock Analysis Report**:
+           - Comprehensive analysis of a specific stock
+           - Data collection from multiple sources
+           - Generate in-depth reports with AI evaluation
+         
+        3. **📰 Daily Market Summary Newsletter** (Coming soon):
+           - Compilation of latest financial news
+           - Categorized by topic
+           - Daily market updates
 
-### How to Use:
+        ### How to Use:
 
-Use the navigation bar on the left to switch between different features of the application.
+        Use the navigation bar on the left to switch between different features of the application.
 
-""")
+        """)
 
 # Display API connection status
 st.sidebar.title("Connection Status")

app.py

@@ -0,0 +1,24 @@
+# app.py - Entry point for Huggingface Spaces
+# This file forwards to the main Home.py file
+
+import streamlit as st
+import sys
+import os
+
+# Add the current directory to the path so that imports work correctly
+sys.path.append(os.path.dirname(__file__))
+
+# Import the Home module and run it
+try:
+    # Apply custom styling
+    try:
+        from .streamlit.custom import init as apply_custom_styling
+        apply_custom_styling()
+    except (ImportError, ModuleNotFoundError):
+        pass
+    
+    # Import and run the Home.py module
+    import Home
+except Exception as e:
+    st.error(f"Failed to load application: {str(e)}")
+    st.error("Please check that the application structure is correct and Home.py exists.") 

pages/chat_app.py

@@ -13,7 +13,20 @@ from datetime import datetime
 
 # --- 1. INITIAL CONFIGURATION & STATE INITIALIZATION ---
 load_dotenv()
-st.set_page_config(layout="wide", page_title="AI Financial Dashboard")
+
+# Initialize session state for this page
+if "chat_app_initialized" not in st.session_state:
+    st.session_state.chat_app_initialized = True
+
+# Set page config consistent with other pages
+st.set_page_config(
+    page_title="AI Financial Dashboard",
+    page_icon="📊",
+    layout="wide",
+    initial_sidebar_state="expanded"
+)
+
+# Configure Gemini API
 genai.configure(api_key=os.getenv("GEMINI_API_KEY"))
 
 def initialize_state():
@@ -220,55 +233,58 @@ def render_currency_tab():
         else: st.error(f"Error: {res.get('error', 'Unknown')}")
 
 # --- 6. MAIN APP LAYOUT & CONTROL FLOW ---
-st.title("📈 AI Financial Dashboard")
+# Use container to avoid UI stacking issues
+main_container = st.container()
 
-col1, col2 = st.columns([1, 1])
+with main_container:
+    st.title("📈 AI Financial Dashboard")
 
-with col2:
-    right_column_container = st.container(height=600)
-    with right_column_container:
-        tab_names = ['Stock Watchlist', 'Time Charts', 'Currency Converter']
-        try: default_index = tab_names.index(st.session_state.active_tab)
-        except ValueError: default_index = 0
-        st.session_state.active_tab = tab_names[default_index]
-        
-        tab1, tab2, tab3 = st.tabs(tab_names)
-        with tab1: render_watchlist_tab()
-        with tab2: render_timeseries_tab()
-        with tab3: render_currency_tab()
+    col1, col2 = st.columns([1, 1])
 
-with col1:
-    chat_container = st.container(height=600)
-    with chat_container:
-        for message in st.session_state.chat_history:
-            with st.chat_message(message["role"]):
-                st.markdown(message["parts"])
+    with col2:
+        right_column_container = st.container(height=600)
+        with right_column_container:
+            tab_names = ['Stock Watchlist', 'Time Charts', 'Currency Converter']
+            try: default_index = tab_names.index(st.session_state.active_tab)
+            except ValueError: default_index = 0
+            st.session_state.active_tab = tab_names[default_index]
+            
+            tab1, tab2, tab3 = st.tabs(tab_names)
+            with tab1: render_watchlist_tab()
+            with tab2: render_timeseries_tab()
+            with tab3: render_currency_tab()
 
-user_prompt = st.chat_input("Ask AI to control the dashboard...")
-if user_prompt:
-    st.session_state.chat_history.append({"role": "user", "parts": user_prompt})
-    st.rerun()
+    with col1:
+        chat_container = st.container(height=600)
+        with chat_container:
+            for message in st.session_state.chat_history:
+                with st.chat_message(message["role"]):
+                    st.markdown(message["parts"])
 
-if st.session_state.chat_history and st.session_state.chat_history[-1]["role"] == "user":
-    last_user_prompt = st.session_state.chat_history[-1]["parts"]
-    
-    # ***** THIS IS THE CHANGED PART *****
-    with chat_container: 
-        with st.chat_message("model"):
-            with st.spinner("🤖 AI executing command..."):
-                response = st.session_state.chat_session.send_message(last_user_prompt)
-                tool_calls = [part.function_call for part in response.candidates[0].content.parts if part.function_call]
-                while tool_calls:
-                    tool_responses = []
-                    for call in tool_calls:
-                        func_name = call.name; func_args = {k: v for k, v in call.args.items()}
-                        if func_name in AVAILABLE_FUNCTIONS:
-                            tool_result = AVAILABLE_FUNCTIONS[func_name](**func_args)
-                            tool_responses.append(glm.Part(function_response=glm.FunctionResponse(name=func_name, response={'result': tool_result})))
-                        else:
-                            tool_responses.append(glm.Part(function_response=glm.FunctionResponse(name=func_name, response={'error': f"Function '{func_name}' not found."})))
-                    response = st.session_state.chat_session.send_message(glm.Content(parts=tool_responses))
+    user_prompt = st.chat_input("Ask AI to control the dashboard...")
+    if user_prompt:
+        st.session_state.chat_history.append({"role": "user", "parts": user_prompt})
+        st.rerun()
+
+    if st.session_state.chat_history and st.session_state.chat_history[-1]["role"] == "user":
+        last_user_prompt = st.session_state.chat_history[-1]["parts"]
+        
+        with chat_container: 
+            with st.chat_message("model"):
+                with st.spinner("🤖 AI executing command..."):
+                    response = st.session_state.chat_session.send_message(last_user_prompt)
                     tool_calls = [part.function_call for part in response.candidates[0].content.parts if part.function_call]
-                
-                st.session_state.chat_history.append({"role": "model", "parts": response.text})
-                st.rerun()
+                    while tool_calls:
+                        tool_responses = []
+                        for call in tool_calls:
+                            func_name = call.name; func_args = {k: v for k, v in call.args.items()}
+                            if func_name in AVAILABLE_FUNCTIONS:
+                                tool_result = AVAILABLE_FUNCTIONS[func_name](**func_args)
+                                tool_responses.append(glm.Part(function_response=glm.FunctionResponse(name=func_name, response={'result': tool_result})))
+                            else:
+                                tool_responses.append(glm.Part(function_response=glm.FunctionResponse(name=func_name, response={'error': f"Function '{func_name}' not found."})))
+                        response = st.session_state.chat_session.send_message(glm.Content(parts=tool_responses))
+                        tool_calls = [part.function_call for part in response.candidates[0].content.parts if part.function_call]
+                    
+                    st.session_state.chat_history.append({"role": "model", "parts": response.text})
+                    st.rerun()

pages/stock_report.py

@@ -14,353 +14,367 @@ from datetime import datetime
 from modules.analysis_pipeline import run_analysis_pipeline, generate_html_report
 from twelvedata_api import TwelveDataAPI
 
-# Page setup
+# Initialize session state for this page
+if "stock_report_initialized" not in st.session_state:
+    st.session_state.stock_report_initialized = True
+    if "analysis_requested" not in st.session_state:
+        st.session_state.analysis_requested = False
+    if "analysis_complete" not in st.session_state:
+        st.session_state.analysis_complete = False
+
+# Page setup - make sure this is consistent with Home.py
 st.set_page_config(
     page_title="Stock Analysis Report",
     page_icon="📊",
-    layout="wide"
+    layout="wide",
+    initial_sidebar_state="expanded"
 )
 
-# Application title
-st.title("📄 In-depth Stock Analysis Report")
-st.markdown("""
-    This application generates a comprehensive analysis report for a stock symbol, combining data from multiple sources 
-    and using AI to synthesize information, helping you make better investment decisions.
-""")
+# Clear the page for fresh rendering
+main_container = st.container()
 
-# Function to create price chart
-def create_price_chart(price_data, period):
-    """Create price chart from data"""
-    if 'values' not in price_data:
-        return None
-    
-    df = pd.DataFrame(price_data['values'])
-    if df.empty:
-        return None
-    
-    df['datetime'] = pd.to_datetime(df['datetime'])
-    df['close'] = pd.to_numeric(df['close'])
-    
-    # Determine chart title based on time period
-    title_map = {
-        '1_month': 'Stock price over the last month',
-        '3_months': 'Stock price over the last 3 months',
-        '1_year': 'Stock price over the last year'
-    }
-    
-    # Create chart with Altair
-    chart = alt.Chart(df).mark_line().encode(
-        x=alt.X('datetime:T', title='Time'),
-        y=alt.Y('close:Q', title='Closing Price', scale=alt.Scale(zero=False)),
-        tooltip=[
-            alt.Tooltip('datetime:T', title='Date', format='%d/%m/%Y'),
-            alt.Tooltip('close:Q', title='Closing Price', format=',.2f'),
-            alt.Tooltip('volume:Q', title='Volume', format=',.0f')
-        ]
-    ).properties(
-        title=title_map.get(period, f'Stock price ({period})'),
-        height=350
-    ).interactive()
-    
-    return chart
+with main_container:
+    # Application title
+    st.title("📄 In-depth Stock Analysis Report")
+    st.markdown("""
+        This application generates a comprehensive analysis report for a stock symbol, combining data from multiple sources 
+        and using AI to synthesize information, helping you make better investment decisions.
+    """)
 
-# Function to convert analysis results to PDF
-def convert_html_to_pdf(html_content):
-    """Convert HTML to PDF file"""
-    with tempfile.NamedTemporaryFile(suffix='.html', delete=False) as f:
-        f.write(html_content.encode())
-        temp_html = f.name
-    
-    pdf_bytes = weasyprint.HTML(filename=temp_html).write_pdf()
-    
-    # Delete temporary file after use
-    os.unlink(temp_html)
-    
-    return pdf_bytes
+    # Function to create price chart
+    def create_price_chart(price_data, period):
+        """Create price chart from data"""
+        if 'values' not in price_data:
+            return None
+        
+        df = pd.DataFrame(price_data['values'])
+        if df.empty:
+            return None
+        
+        df['datetime'] = pd.to_datetime(df['datetime'])
+        df['close'] = pd.to_numeric(df['close'])
+        
+        # Determine chart title based on time period
+        title_map = {
+            '1_month': 'Stock price over the last month',
+            '3_months': 'Stock price over the last 3 months',
+            '1_year': 'Stock price over the last year'
+        }
+        
+        # Create chart with Altair
+        chart = alt.Chart(df).mark_line().encode(
+            x=alt.X('datetime:T', title='Time'),
+            y=alt.Y('close:Q', title='Closing Price', scale=alt.Scale(zero=False)),
+            tooltip=[
+                alt.Tooltip('datetime:T', title='Date', format='%d/%m/%Y'),
+                alt.Tooltip('close:Q', title='Closing Price', format=',.2f'),
+                alt.Tooltip('volume:Q', title='Volume', format=',.0f')
+            ]
+        ).properties(
+            title=title_map.get(period, f'Stock price ({period})'),
+            height=350
+        ).interactive()
+        
+        return chart
 
-# Function to create PDF download link
-def get_download_link(pdf_bytes, filename):
-    """Create download link for PDF file"""
-    b64 = base64.b64encode(pdf_bytes).decode()
-    href = f'<a href="data:application/pdf;base64,{b64}" download="{filename}">Download Report (PDF)</a>'
-    return href
+    # Function to convert analysis results to PDF
+    def convert_html_to_pdf(html_content):
+        """Convert HTML to PDF file"""
+        with tempfile.NamedTemporaryFile(suffix='.html', delete=False) as f:
+            f.write(html_content.encode())
+            temp_html = f.name
+        
+        pdf_bytes = weasyprint.HTML(filename=temp_html).write_pdf()
+        
+        # Delete temporary file after use
+        os.unlink(temp_html)
+        
+        return pdf_bytes
+
+    # Function to create PDF download link
+    def get_download_link(pdf_bytes, filename):
+        """Create download link for PDF file"""
+        b64 = base64.b64encode(pdf_bytes).decode()
+        href = f'<a href="data:application/pdf;base64,{b64}" download="{filename}">Download Report (PDF)</a>'
+        return href
 
-# List of popular stock symbols and information
-def load_stock_symbols():
-    """Load stock symbols from cache or create new cache"""
-    cache_file = "static/stock_symbols_cache.json"
-    
-    # Check if cache exists
-    if os.path.exists(cache_file):
+    # List of popular stock symbols and information
+    @st.cache_data(ttl=3600)
+    def load_stock_symbols():
+        """Load stock symbols from cache or create new cache"""
+        cache_file = "static/stock_symbols_cache.json"
+        
+        # Check if cache exists
+        if os.path.exists(cache_file):
+            try:
+                with open(cache_file, 'r') as f:
+                    return json.load(f)
+            except Exception as e:
+                print(f"Error loading cache: {e}")
+        
+        # Default list if cache doesn't exist or fails to load
+        default_symbols = [
+            {"symbol": "AAPL", "name": "Apple Inc."},
+            {"symbol": "MSFT", "name": "Microsoft Corporation"},
+            {"symbol": "GOOGL", "name": "Alphabet Inc."},
+            {"symbol": "AMZN", "name": "Amazon.com Inc."},
+            {"symbol": "TSLA", "name": "Tesla, Inc."},
+            {"symbol": "META", "name": "Meta Platforms, Inc."},
+            {"symbol": "NVDA", "name": "NVIDIA Corporation"},
+            {"symbol": "JPM", "name": "JPMorgan Chase & Co."},
+            {"symbol": "V", "name": "Visa Inc."},
+            {"symbol": "JNJ", "name": "Johnson & Johnson"},
+            {"symbol": "WMT", "name": "Walmart Inc."},
+            {"symbol": "MA", "name": "Mastercard Incorporated"},
+            {"symbol": "PG", "name": "Procter & Gamble Co."},
+            {"symbol": "UNH", "name": "UnitedHealth Group Inc."},
+            {"symbol": "HD", "name": "Home Depot Inc."},
+            {"symbol": "BAC", "name": "Bank of America Corp."},
+            {"symbol": "XOM", "name": "Exxon Mobil Corporation"},
+            {"symbol": "DIS", "name": "Walt Disney Co."},
+            {"symbol": "CSCO", "name": "Cisco Systems, Inc."},
+            {"symbol": "VZ", "name": "Verizon Communications Inc."},
+            {"symbol": "ADBE", "name": "Adobe Inc."},
+            {"symbol": "NFLX", "name": "Netflix, Inc."},
+            {"symbol": "CMCSA", "name": "Comcast Corporation"},
+            {"symbol": "PFE", "name": "Pfizer Inc."},
+            {"symbol": "KO", "name": "Coca-Cola Company"},
+            {"symbol": "INTC", "name": "Intel Corporation"},
+            {"symbol": "PYPL", "name": "PayPal Holdings, Inc."},
+            {"symbol": "T", "name": "AT&T Inc."},
+            {"symbol": "PEP", "name": "PepsiCo, Inc."},
+            {"symbol": "MRK", "name": "Merck & Co., Inc."}
+        ]
+        
+        # Try to fetch more comprehensive list if API key is available
         try:
-            with open(cache_file, 'r') as f:
-                return json.load(f)
+            from dotenv import load_dotenv
+            load_dotenv()
+            api_key = os.getenv("TWELVEDATA_API_KEY")
+            if api_key:
+                td_api = TwelveDataAPI(api_key)
+                stocks_data = td_api.get_all_stocks(exchange="NASDAQ")
+                if stocks_data and 'data' in stocks_data:
+                    # Convert to format we need and take first 1000 stocks
+                    symbols = [{"symbol": stock["symbol"], "name": stock.get("name", "Unknown")} 
+                              for stock in stocks_data['data']]
+                    
+                    # Save to cache
+                    os.makedirs(os.path.dirname(cache_file), exist_ok=True)
+                    with open(cache_file, 'w') as f:
+                        json.dump(symbols, f)
+                    
+                    return symbols
         except Exception as e:
-            print(f"Error loading cache: {e}")
-    
-    # Default list if cache doesn't exist or fails to load
-    default_symbols = [
-        {"symbol": "AAPL", "name": "Apple Inc."},
-        {"symbol": "MSFT", "name": "Microsoft Corporation"},
-        {"symbol": "GOOGL", "name": "Alphabet Inc."},
-        {"symbol": "AMZN", "name": "Amazon.com Inc."},
-        {"symbol": "TSLA", "name": "Tesla, Inc."},
-        {"symbol": "META", "name": "Meta Platforms, Inc."},
-        {"symbol": "NVDA", "name": "NVIDIA Corporation"},
-        {"symbol": "JPM", "name": "JPMorgan Chase & Co."},
-        {"symbol": "V", "name": "Visa Inc."},
-        {"symbol": "JNJ", "name": "Johnson & Johnson"},
-        {"symbol": "WMT", "name": "Walmart Inc."},
-        {"symbol": "MA", "name": "Mastercard Incorporated"},
-        {"symbol": "PG", "name": "Procter & Gamble Co."},
-        {"symbol": "UNH", "name": "UnitedHealth Group Inc."},
-        {"symbol": "HD", "name": "Home Depot Inc."},
-        {"symbol": "BAC", "name": "Bank of America Corp."},
-        {"symbol": "XOM", "name": "Exxon Mobil Corporation"},
-        {"symbol": "DIS", "name": "Walt Disney Co."},
-        {"symbol": "CSCO", "name": "Cisco Systems, Inc."},
-        {"symbol": "VZ", "name": "Verizon Communications Inc."},
-        {"symbol": "ADBE", "name": "Adobe Inc."},
-        {"symbol": "NFLX", "name": "Netflix, Inc."},
-        {"symbol": "CMCSA", "name": "Comcast Corporation"},
-        {"symbol": "PFE", "name": "Pfizer Inc."},
-        {"symbol": "KO", "name": "Coca-Cola Company"},
-        {"symbol": "INTC", "name": "Intel Corporation"},
-        {"symbol": "PYPL", "name": "PayPal Holdings, Inc."},
-        {"symbol": "T", "name": "AT&T Inc."},
-        {"symbol": "PEP", "name": "PepsiCo, Inc."},
-        {"symbol": "MRK", "name": "Merck & Co., Inc."}
-    ]
-    
-    # Try to fetch more comprehensive list if API key is available
-    try:
-        from dotenv import load_dotenv
-        load_dotenv()
-        api_key = os.getenv("TWELVEDATA_API_KEY")
-        if api_key:
-            td_api = TwelveDataAPI(api_key)
-            stocks_data = td_api.get_all_stocks(exchange="NASDAQ")
-            if stocks_data and 'data' in stocks_data:
-                # Convert to format we need and take first 1000 stocks
-                symbols = [{"symbol": stock["symbol"], "name": stock.get("name", "Unknown")} 
-                          for stock in stocks_data['data']]
-                
-                # Save to cache
-                os.makedirs(os.path.dirname(cache_file), exist_ok=True)
-                with open(cache_file, 'w') as f:
-                    json.dump(symbols, f)
-                
-                return symbols
-    except Exception as e:
-        print(f"Error fetching stock symbols from API: {e}")
-    
-    # If everything fails, return default list
-    return default_symbols
+            print(f"Error fetching stock symbols from API: {e}")
+        
+        # If everything fails, return default list
+        return default_symbols
 
-# Load stock symbols
-STOCK_SYMBOLS = load_stock_symbols()
+    # Load stock symbols
+    STOCK_SYMBOLS = load_stock_symbols()
 
-# Function to format stock options for display
-def format_stock_option(stock):
-    return f"{stock['symbol']} - {stock['name']}"
+    # Function to format stock options for display
+    def format_stock_option(stock):
+        return f"{stock['symbol']} - {stock['name']}"
 
-# Create interface
-col1, col2 = st.columns([3, 1])
+    # Create interface
+    col1, col2 = st.columns([3, 1])
 
-# Information input section
-with col2:
-    st.subheader("Enter Information")
-    
-    # Create a list of formatted options and a mapping back to symbols
-    stock_options = [format_stock_option(stock) for stock in STOCK_SYMBOLS]
-    
-    # Use selectbox with search functionality
-    selected_stock = st.selectbox(
-        "Select a stock symbol",
-        options=stock_options,
-        index=0 if stock_options else None,
-        placeholder="Search for a stock symbol...",
-    )
-    
-    # Extract symbol from selection
-    if selected_stock:
-        stock_symbol = selected_stock.split(" - ")[0]
-    else:
-        stock_symbol = ""
-    
-    if st.button("Generate Report", use_container_width=True, type="primary"):
-        if not stock_symbol:
-            st.error("Please select a stock symbol to continue.")
-        else:
-            # Save stock symbol to session state to maintain between runs
-            st.session_state.stock_symbol = stock_symbol
-            st.session_state.analysis_requested = True
-            st.rerun()
-    
-    # PDF report generation section - moved from tab1
-    if "analysis_complete" in st.session_state and st.session_state.analysis_complete:
-        st.divider()
-        st.subheader("PDF Report")
+    # Information input section
+    with col2:
+        st.subheader("Enter Information")
         
-        # Get results from session state
-        analysis_results = st.session_state.analysis_results
+        # Create a list of formatted options and a mapping back to symbols
+        stock_options = [format_stock_option(stock) for stock in STOCK_SYMBOLS]
         
-        # Create static directory if it doesn't exist
-        os.makedirs("static", exist_ok=True)
+        # Use selectbox with search functionality
+        selected_stock = st.selectbox(
+            "Select a stock symbol",
+            options=stock_options,
+            index=0 if stock_options else None,
+            placeholder="Search for a stock symbol...",
+        )
         
-        # Create PDF filename and path
-        filename = f"Report_{analysis_results['symbol']}_{datetime.now().strftime('%d%m%Y')}.pdf"
-        pdf_path = os.path.join("static", filename)
-        
-        # Display information
-        st.markdown("Get a complete PDF report with price charts:")
+        # Extract symbol from selection
+        if selected_stock:
+            stock_symbol = selected_stock.split(" - ")[0]
+        else:
+            stock_symbol = ""
         
-        # Import PDF generation function
-        from modules.analysis_pipeline import generate_pdf_report
+        if st.button("Generate Report", use_container_width=True, type="primary"):
+            if not stock_symbol:
+                st.error("Please select a stock symbol to continue.")
+            else:
+                # Save stock symbol to session state to maintain between runs
+                st.session_state.stock_symbol = stock_symbol
+                st.session_state.analysis_requested = True
+                st.rerun()
         
-        # Generate and download PDF button (combined)
-        if st.button("📊 Generate & Download PDF Report", use_container_width=True, key="pdf_btn", type="primary"):
-            # Check if file doesn't exist or needs to be recreated
-            if not os.path.exists(pdf_path):
-                with st.spinner("Creating PDF report with charts..."):
-                    generate_pdf_report(analysis_results, pdf_path)
-                
-                if not os.path.exists(pdf_path):
-                    st.error("Failed to create PDF report.")
-                    st.stop()
+        # PDF report generation section
+        if "analysis_complete" in st.session_state and st.session_state.analysis_complete:
+            st.divider()
+            st.subheader("PDF Report")
             
-            # Read PDF file for download
-            with open(pdf_path, "rb") as pdf_file:
-                pdf_bytes = pdf_file.read()
+            # Get results from session state
+            analysis_results = st.session_state.analysis_results
             
-            # Display success message and download widget
-            st.success("PDF report generated successfully!")
+            # Create static directory if it doesn't exist
+            os.makedirs("static", exist_ok=True)
             
-            st.download_button(
-                label="⬇️ Download Report",
-                data=pdf_bytes,
-                file_name=filename,
-                mime="application/pdf",
-                use_container_width=True,
-                key="download_pdf_btn"
-            )
-
-# Report display section
-with col1:
-    # Check if there's an analysis request
-    if "analysis_requested" in st.session_state and st.session_state.analysis_requested:
-        symbol = st.session_state.stock_symbol
-        
-        with st.spinner(f"🔍 Collecting data and analyzing {symbol} stock... (this may take a few minutes)"):
-            try:
-                # Run analysis
-                analysis_results = asyncio.run(run_analysis_pipeline(symbol))
+            # Create PDF filename and path
+            filename = f"Report_{analysis_results['symbol']}_{datetime.now().strftime('%d%m%Y')}.pdf"
+            pdf_path = os.path.join("static", filename)
+            
+            # Display information
+            st.markdown("Get a complete PDF report with price charts:")
+            
+            # Import PDF generation function
+            from modules.analysis_pipeline import generate_pdf_report
+            
+            # Generate and download PDF button (combined)
+            if st.button("📊 Generate & Download PDF Report", use_container_width=True, key="pdf_btn", type="primary"):
+                # Check if file doesn't exist or needs to be recreated
+                if not os.path.exists(pdf_path):
+                    with st.spinner("Creating PDF report with charts..."):
+                        generate_pdf_report(analysis_results, pdf_path)
+                    
+                    if not os.path.exists(pdf_path):
+                        st.error("Failed to create PDF report.")
+                        st.stop()
                 
-                # Save results to session state
-                st.session_state.analysis_results = analysis_results
-                st.session_state.analysis_complete = True
-                st.session_state.analysis_requested = False
+                # Read PDF file for download
+                with open(pdf_path, "rb") as pdf_file:
+                    pdf_bytes = pdf_file.read()
                 
-                # Automatically rerun to display results
-                st.rerun()
-            except Exception as e:
-                st.error(f"An error occurred during analysis: {str(e)}")
-                st.session_state.analysis_requested = False
-    
-    # Check if analysis is complete
-    if "analysis_complete" in st.session_state and st.session_state.analysis_complete:
-        # Get results from session state
-        analysis_results = st.session_state.analysis_results
-        
-        # Create tabs to display content
-        tab1, tab2, tab3, tab4, tab5 = st.tabs([
-            "📋 Overview", 
-            "💰 Financial Health", 
-            "📰 News & Sentiment", 
-            "👨‍💼 Market Analysis",
-            "📊 Price Charts"
-        ])
-        
-        with tab1:
-            # Display basic company information
-            overview = analysis_results.get('overview', {})
-            if overview:
-                col1, col2 = st.columns([1, 1])
-                with col1:
-                    st.subheader(f"{analysis_results['symbol']} - {overview.get('Name', 'N/A')}")
-                    st.write(f"**Industry:** {overview.get('Industry', 'N/A')}")
-                    st.write(f"**Sector:** {overview.get('Sector', 'N/A')}")
-                with col2:
-                    st.write(f"**Market Cap:** {overview.get('MarketCapitalization', 'N/A')}")
-                    st.write(f"**P/E Ratio:** {overview.get('PERatio', 'N/A')}")
-                    st.write(f"**Dividend Yield:** {overview.get('DividendYield', 'N/A')}%")
+                # Display success message and download widget
+                st.success("PDF report generated successfully!")
+                
+                st.download_button(
+                    label="⬇️ Download Report",
+                    data=pdf_bytes,
+                    file_name=filename,
+                    mime="application/pdf",
+                    use_container_width=True,
+                    key="download_pdf_btn"
+                )
+
+    # Report display section
+    with col1:
+        # Check if there's an analysis request
+        if "analysis_requested" in st.session_state and st.session_state.analysis_requested:
+            symbol = st.session_state.stock_symbol
             
-            # Display summary
-            st.markdown("### Summary & Recommendation")
-            st.markdown(analysis_results['analysis']['summary'])
+            with st.spinner(f"🔍 Collecting data and analyzing {symbol} stock... (this may take a few minutes)"):
+                try:
+                    # Run analysis
+                    analysis_results = asyncio.run(run_analysis_pipeline(symbol))
+                    
+                    # Save results to session state
+                    st.session_state.analysis_results = analysis_results
+                    st.session_state.analysis_complete = True
+                    st.session_state.analysis_requested = False
+                    
+                    # Automatically rerun to display results
+                    st.rerun()
+                except Exception as e:
+                    st.error(f"An error occurred during analysis: {str(e)}")
+                    st.session_state.analysis_requested = False
         
-        with tab2:
-            st.markdown("### Financial Health Analysis")
-            st.markdown(analysis_results['analysis']['financial_health'])
-        
-        with tab3:
-            st.markdown("### News & Market Sentiment Analysis")
-            st.markdown(analysis_results['analysis']['news_sentiment'])
-        
-        with tab4:
-            st.markdown("### Market Analysis")
-            st.markdown(analysis_results['analysis']['expert_opinion'])
-        
-        with tab5:
-            st.markdown("### Stock Price Charts")
+        # Check if analysis is complete
+        if "analysis_complete" in st.session_state and st.session_state.analysis_complete:
+            # Get results from session state
+            analysis_results = st.session_state.analysis_results
+            
+            # Create tabs to display content
+            tab1, tab2, tab3, tab4, tab5 = st.tabs([
+                "📋 Overview", 
+                "💰 Financial Health", 
+                "📰 News & Sentiment", 
+                "👨‍💼 Market Analysis",
+                "📊 Price Charts"
+            ])
+            
+            with tab1:
+                # Display basic company information
+                overview = analysis_results.get('overview', {})
+                if overview:
+                    col1, col2 = st.columns([1, 1])
+                    with col1:
+                        st.subheader(f"{analysis_results['symbol']} - {overview.get('Name', 'N/A')}")
+                        st.write(f"**Industry:** {overview.get('Industry', 'N/A')}")
+                        st.write(f"**Sector:** {overview.get('Sector', 'N/A')}")
+                    with col2:
+                        st.write(f"**Market Cap:** {overview.get('MarketCapitalization', 'N/A')}")
+                        st.write(f"**P/E Ratio:** {overview.get('PERatio', 'N/A')}")
+                        st.write(f"**Dividend Yield:** {overview.get('DividendYield', 'N/A')}%")
+                
+                # Display summary
+                st.markdown("### Summary & Recommendation")
+                st.markdown(analysis_results['analysis']['summary'])
+            
+            with tab2:
+                st.markdown("### Financial Health Analysis")
+                st.markdown(analysis_results['analysis']['financial_health'])
+            
+            with tab3:
+                st.markdown("### News & Market Sentiment Analysis")
+                st.markdown(analysis_results['analysis']['news_sentiment'])
             
-            # Display charts from price data
-            price_data = analysis_results.get('price_data', {})
-            if price_data:
-                period_tabs = st.tabs(['1 Month', '3 Months', '1 Year'])
+            with tab4:
+                st.markdown("### Market Analysis")
+                st.markdown(analysis_results['analysis']['expert_opinion'])
+            
+            with tab5:
+                st.markdown("### Stock Price Charts")
                 
-                periods = ['1_month', '3_months', '1_year']
-                for i, period in enumerate(periods):
-                    with period_tabs[i]:
-                        if period in price_data:
-                            chart = create_price_chart(price_data[period], period)
-                            if chart:
-                                st.altair_chart(chart, use_container_width=True)
+                # Display charts from price data
+                price_data = analysis_results.get('price_data', {})
+                if price_data:
+                    period_tabs = st.tabs(['1 Month', '3 Months', '1 Year'])
+                    
+                    periods = ['1_month', '3_months', '1_year']
+                    for i, period in enumerate(periods):
+                        with period_tabs[i]:
+                            if period in price_data:
+                                chart = create_price_chart(price_data[period], period)
+                                if chart:
+                                    st.altair_chart(chart, use_container_width=True)
+                                else:
+                                    st.info(f"Insufficient data to display chart for {period} timeframe.")
                             else:
-                                st.info(f"Insufficient data to display chart for {period} timeframe.")
-                        else:
-                            st.info(f"No chart data available for {period} timeframe.")
-            else:
-                st.info("No price chart data available for this stock.")
-    else:
-        # Display instructions when no analysis is present
-        st.info("👈 Enter a stock symbol and click 'Generate Report' to begin.")
-        st.markdown("""
-        ### About Stock Analysis Reports
-        
-        The stock analysis report includes the following information:
-        
-        1. **Overview & Investment Recommendation**: Summary of the company and general investment potential assessment.
-        2. **Financial Health Analysis**: Evaluation of financial metrics, revenue growth, and profitability.
-        3. **News & Market Sentiment Analysis**: Summary of notable news related to the company.
-        4. **Market Analysis**: Analysis of current stock performance and market trends.
-        5. **Price Charts**: Stock price charts for various timeframes.
-        
-        Reports are generated based on data from multiple sources and analyzed by AI.
-        """)
-        
-        # Display popular stock symbols
-        st.markdown("### Popular Stock Symbols")
-        
-        # Display list of popular stock symbols in grid
-        # Only take first 12 to avoid cluttering the interface
-        display_stocks = STOCK_SYMBOLS[:12]
-        
-        # Create grid with 4 columns
-        cols = st.columns(4)
-        for i, stock in enumerate(display_stocks):
-            col = cols[i % 4]
-            if col.button(f"{stock['symbol']} - {stock['name']}", key=f"pop_stock_{i}", use_container_width=True):
-                st.session_state.stock_symbol = stock['symbol']
-                st.session_state.analysis_requested = True
-                st.rerun() 
+                                st.info(f"No chart data available for {period} timeframe.")
+                else:
+                    st.info("No price chart data available for this stock.")
+        else:
+            # Display instructions when no analysis is present
+            st.info("👈 Enter a stock symbol and click 'Generate Report' to begin.")
+            st.markdown("""
+            ### About Stock Analysis Reports
+            
+            The stock analysis report includes the following information:
+            
+            1. **Overview & Investment Recommendation**: Summary of the company and general investment potential assessment.
+            2. **Financial Health Analysis**: Evaluation of financial metrics, revenue growth, and profitability.
+            3. **News & Market Sentiment Analysis**: Summary of notable news related to the company.
+            4. **Market Analysis**: Analysis of current stock performance and market trends.
+            5. **Price Charts**: Stock price charts for various timeframes.
+            
+            Reports are generated based on data from multiple sources and analyzed by AI.
+            """)
+            
+            # Display popular stock symbols
+            st.markdown("### Popular Stock Symbols")
+            
+            # Display list of popular stock symbols in grid
+            # Only take first 12 to avoid cluttering the interface
+            display_stocks = STOCK_SYMBOLS[:12]
+            
+            # Create grid with 4 columns
+            cols = st.columns(4)
+            for i, stock in enumerate(display_stocks):
+                col = cols[i % 4]
+                if col.button(f"{stock['symbol']} - {stock['name']}", key=f"pop_stock_{i}", use_container_width=True):
+                    st.session_state.stock_symbol = stock['symbol']
+                    st.session_state.analysis_requested = True
+                    st.rerun()