Delete app.py

app.py

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-import streamlit as st
-import pandas as pd
-import numpy as np
-import plotly.express as px
-import plotly.graph_objects as go
-import google.generativeai as genai
-import os
-from datetime import datetime, timedelta
-import base64
-from io import BytesIO
-
-# Initialize page configuration
-st.set_page_config(
-    page_title="StartupFinancePilot",
-    page_icon="💰",
-    layout="wide",
-    initial_sidebar_state="expanded"
-)
-
-# Constants
-DEFAULT_GROWTH_RATE = 0.08  # 8% monthly growth
-DEFAULT_BURN_RATE = 85000   # $85,000 monthly burn
-ENGINEER_SALARY = 10000     # $10,000 monthly cost per engineer ($120K/year)
-DEFAULT_MARKETING_BUDGET = 10000  # $10,000 monthly marketing budget
-
-# Sample data
-@st.cache_data
-def load_sample_data():
-    # TechHealth AI data
-    startup_data = {
-        "name": "TechHealth AI",
-        "stage": "Seed",
-        "founded": "18 months ago",
-        "employees": 12,
-        "last_funding": "$1.2M seed round 10 months ago",
-        "cash": 320000,
-        "burn_rate": 85000,
-        "revenue": 15000,
-        "growth_rate": 0.08
-    }
-    
-    # Cash flow history
-    cash_flow_data = {
-        "Month": [f"Month {i}" for i in range(1, 11)],
-        "Revenue": [8000, 8500, 9200, 10000, 10800, 11700, 12600, 13600, 14700, 15800],
-        "Payroll": [60000, 60000, 62000, 62000, 65000, 65000, 70000, 70000, 75000, 75000],
-        "Marketing": [8000, 9000, 10000, 12000, 15000, 18000, 15000, 12000, 10000, 8000],
-        "Office": [5000, 5000, 5000, 5000, 5000, 5000, 5000, 5000, 5000, 5000],
-        "Software": [3000, 3200, 3500, 3800, 4000, 4200, 4500, 4800, 5000, 5200],
-        "Travel": [2000, 1800, 2500, 3000, 4000, 4500, 3500, 3000, 2500, 2000],
-        "Legal": [1500, 1000, 800, 1200, 800, 2000, 1500, 1000, 3000, 1200],
-        "Misc": [1000, 1200, 1300, 1500, 1700, 1800, 2000, 2200, 2500, 2800]
-    }
-    
-    # Add calculated fields
-    df = pd.DataFrame(cash_flow_data)
-    df["Total_Expenses"] = df[["Payroll", "Marketing", "Office", "Software", "Travel", "Legal", "Misc"]].sum(axis=1)
-    df["Net_Burn"] = df["Total_Expenses"] - df["Revenue"]
-    
-    # Transaction data
-    transactions = pd.DataFrame([
-        {"Date": "2023-11-05", "Category": "Travel", "Vendor": "Caribbean Cruises", "Amount": 8500, "Description": "Team Retreat Planning", "Flag": "Suspicious"},
-        {"Date": "2023-11-12", "Category": "Marketing", "Vendor": "LuxuryGifts Inc", "Amount": 4200, "Description": "Client Appreciation", "Flag": "Suspicious"},
-        {"Date": "2023-11-22", "Category": "Office", "Vendor": "Premium Furniture", "Amount": 12000, "Description": "Office Upgrades", "Flag": "Suspicious"},
-        {"Date": "2023-11-28", "Category": "Consulting", "Vendor": "Strategic Vision LLC", "Amount": 7500, "Description": "Strategy Consulting", "Flag": "Suspicious"},
-        {"Date": "2023-12-05", "Category": "Software", "Vendor": "Personal Apple Store", "Amount": 3200, "Description": "Development Tools", "Flag": "Suspicious"},
-        {"Date": "2023-12-12", "Category": "Legal", "Vendor": "Anderson Brothers", "Amount": 5800, "Description": "Legal Services", "Flag": "Normal"},
-        {"Date": "2023-12-20", "Category": "Payroll", "Vendor": "November Payroll", "Amount": 75000, "Description": "Monthly Payroll", "Flag": "Normal"},
-        {"Date": "2023-12-22", "Category": "Marketing", "Vendor": "Google Ads", "Amount": 8000, "Description": "Ad Campaign", "Flag": "Normal"},
-        {"Date": "2023-12-25", "Category": "Office", "Vendor": "WeWork", "Amount": 5000, "Description": "Monthly Rent", "Flag": "Normal"},
-        {"Date": "2023-12-28", "Category": "Software", "Vendor": "AWS", "Amount": 5200, "Description": "Cloud Services", "Flag": "Normal"},
-        {"Date": "2024-01-05", "Category": "Travel", "Vendor": "Delta Airlines", "Amount": 1200, "Description": "Client Meeting Travel", "Flag": "Normal"},
-        {"Date": "2024-01-10", "Category": "Marketing", "Vendor": "Facebook Ads", "Amount": 4500, "Description": "Social Media Campaign", "Flag": "Normal"},
-        {"Date": "2024-01-15", "Category": "Software", "Vendor": "Atlassian", "Amount": 2800, "Description": "Development Tools", "Flag": "Normal"},
-        {"Date": "2024-01-20", "Category": "Payroll", "Vendor": "January Payroll", "Amount": 75000, "Description": "Monthly Payroll", "Flag": "Normal"},
-        {"Date": "2024-01-25", "Category": "Office", "Vendor": "WeWork", "Amount": 5000, "Description": "Monthly Rent", "Flag": "Normal"}
-    ])
-    
-    return startup_data, df, transactions
-
-# Setup Gemini API (with API key from secrets)
-def setup_genai():
-    if 'GOOGLE_API_KEY' in st.secrets:
-        genai.configure(api_key=st.secrets['GOOGLE_API_KEY'])
-        return True
-    else:
-        st.warning("Google API key not found. Please add it to the secrets.")
-        return False
-
-# Financial modeling functions
-def calculate_runway(initial_cash, monthly_burn, monthly_revenue, growth_rate, months=24):
-    """Calculate runway based on current burn rate and revenue growth."""
-    dates = [datetime.now() + timedelta(days=30*i) for i in range(months)]
-    df = pd.DataFrame(index=dates, columns=['Cash', 'Revenue', 'Expenses', 'Net_Burn', 'Cumulative_Cash'])
-    
-    current_cash = initial_cash
-    current_revenue = monthly_revenue
-    df.iloc[0, df.columns.get_loc('Cash')] = current_cash
-    df.iloc[0, df.columns.get_loc('Revenue')] = current_revenue
-    df.iloc[0, df.columns.get_loc('Expenses')] = monthly_burn
-    df.iloc[0, df.columns.get_loc('Net_Burn')] = monthly_burn - current_revenue
-    df.iloc[0, df.columns.get_loc('Cumulative_Cash')] = current_cash
-    
-    runway_months = months
-    for i in range(1, months):
-        current_revenue = current_revenue * (1 + growth_rate)
-        net_burn = monthly_burn - current_revenue
-        current_cash = current_cash - net_burn
-        
-        df.iloc[i, df.columns.get_loc('Cash')] = current_cash
-        df.iloc[i, df.columns.get_loc('Revenue')] = current_revenue
-        df.iloc[i, df.columns.get_loc('Expenses')] = monthly_burn
-        df.iloc[i, df.columns.get_loc('Net_Burn')] = net_burn
-        df.iloc[i, df.columns.get_loc('Cumulative_Cash')] = current_cash
-        
-        if current_cash <= 0:
-            runway_months = i
-            break
-    
-    return runway_months, df
-
-def simulate_decision(initial_cash, monthly_burn, monthly_revenue, growth_rate, 
-                     new_expenses=0, new_hires=0, new_marketing=0, growth_impact=0):
-    """Simulate the impact of a business decision on runway."""
-    # Calculate current runway
-    current_runway, current_df = calculate_runway(initial_cash, monthly_burn, monthly_revenue, growth_rate)
-    
-    # Calculate additional expenses
-    additional_expenses = new_expenses + (new_hires * ENGINEER_SALARY) + new_marketing
-    
-    # Calculate new runway
-    new_runway, new_df = calculate_runway(
-        initial_cash,
-        monthly_burn + additional_expenses,
-        monthly_revenue,
-        growth_rate + growth_impact
-    )
-    
-    return current_runway, new_runway, current_df, new_df
-
-def detect_suspicious_transactions(transactions_df):
-    """Simple rule-based suspicious transaction detection."""
-    df = transactions_df.copy()
-    
-    # Define thresholds for each category
-    category_thresholds = {
-        "Travel": 3000,
-        "Marketing": 10000,
-        "Office": 7000,
-        "Software": 6000,
-        "Consulting": 5000,
-        "Legal": 6000
-    }
-    
-    # Define suspicious terms
-    suspicious_terms = ['luxury', 'cruise', 'premium', 'personal', 'gift']
-    
-    # Add suspicious column
-    df['Suspicious'] = False
-    df['Reason'] = ""
-    
-    # Check for suspicious patterns
-    for idx, row in df.iterrows():
-        reasons = []
-        
-        # Check if amount exceeds category threshold
-        if row['Category'] in category_thresholds:
-            if row['Amount'] > category_thresholds[row['Category']]:
-                reasons.append(f"Amount exceeds typical spending for {row['Category']}")
-        
-        # Check for suspicious vendors or descriptions
-        if any(term in str(row['Vendor']).lower() for term in suspicious_terms):
-            reasons.append(f"Vendor name contains suspicious term")
-        
-        if any(term in str(row['Description']).lower() for term in suspicious_terms):
-            reasons.append(f"Description contains suspicious term")
-        
-        # Mark as suspicious if any reasons
-        if reasons:
-            df.at[idx, 'Suspicious'] = True
-            df.at[idx, 'Reason'] = "; ".join(reasons)
-    
-    return df
-
-# Gemini prompt functions
-def get_runway_analysis(financial_data):
-    """Get runway analysis using Gemini."""
-    prompt = f"""
-    You are a financial advisor for startups. Analyze this startup's financial data:
-    - Current cash: ${financial_data['cash']}
-    - Monthly burn rate: ${financial_data['burn_rate']}
-    - Monthly revenue: ${financial_data['revenue']}
-    - Monthly growth rate: {financial_data['growth_rate'] * 100}%
-
-    Calculate and explain their runway, financial health, and recommendations in a concise paragraph.
-    """
-    
-    model = genai.GenerativeModel('gemini')
-    response = model.generate_content(prompt)
-    
-    return response.text
-
-def get_decision_analysis(question, financial_data):
-    """Get analysis for a specific decision."""
-    prompt = f"""
-    You are a financial advisor for startups. A founder asks:
-    "{question}"
-
-    Here's their current financial situation:
-    - Current cash: ${financial_data['cash']}
-    - Monthly burn rate: ${financial_data['burn_rate']}
-    - Monthly revenue: ${financial_data['revenue']}
-    - Monthly growth rate: {financial_data['growth_rate'] * 100}%
-
-    Analyze how this decision would impact their runway and financial health.
-    Provide specific recommendations in 3-4 concise sentences.
-    """
-    
-    model = genai.GenerativeModel('gemini')
-    response = model.generate_content(prompt)
-    
-    return response.text
-
-def get_fraud_analysis(transactions_df):
-    """Get analysis of potentially fraudulent transactions."""
-    suspicious_df = transactions_df[transactions_df['Suspicious']]
-    
-    if len(suspicious_df) == 0:
-        return "No suspicious transactions detected."
-    
-    transactions_text = suspicious_df.to_string(index=False)
-    
-    prompt = f"""
-    You are a financial advisor specializing in startup spending oversight.
-    Review these flagged transactions:
-
-    {transactions_text}
-
-    Explain why these transactions might concern investors and what actions the startup should take.
-    Keep your response to 4-5 concise sentences focused on the most concerning issues.
-    """
-    
-    model = genai.GenerativeModel('gemini')
-    response = model.generate_content(prompt)
-    
-    return response.text
-
-def get_advisory_guidance(question, financial_data):
-    """Get strategic guidance for a startup question."""
-    prompt = f"""
-    You are a strategic financial advisor for startups. A founder asks:
-    "{question}"
-
-    Here's their current financial situation:
-    - Stage: {financial_data['stage']}
-    - Current cash: ${financial_data['cash']}
-    - Monthly burn rate: ${financial_data['burn_rate']}
-    - Monthly revenue: ${financial_data['revenue']}
-    - Monthly growth rate: {financial_data['growth_rate'] * 100}%
-    - Last funding: {financial_data['last_funding']}
-    - Team size: {financial_data['employees']}
-
-    Provide specific, actionable advice addressing their question in 3-4 concise sentences.
-    Include timelines and metrics where relevant.
-    """
-    
-    model = genai.GenerativeModel('gemini')
-    response = model.generate_content(prompt)
-    
-    return response.text
-
-# UI Components
-def create_sidebar():
-    """Create sidebar with company profile and filters."""
-    st.sidebar.title("StartupFinancePilot")
-    st.sidebar.image("https://img.freepik.com/premium-vector/business-finance-analytics-logo-design-vector-template_67715-552.jpg", width=150)
-    
-    # Company profile
-    startup_data, _, _ = load_sample_data()
-    
-    st.sidebar.header("Company Profile")
-    st.sidebar.write(f"**{startup_data['name']}**")
-    st.sidebar.write(f"Stage: {startup_data['stage']}")
-    st.sidebar.write(f"Founded: {startup_data['founded']}")
-    st.sidebar.write(f"Employees: {startup_data['employees']}")
-    st.sidebar.write(f"Last Funding: {startup_data['last_funding']}")
-    
-    # App navigation
-    st.sidebar.header("Navigation")
-    page = st.sidebar.radio("Go to", ["Financial Dashboard", "Decision Simulator", "Fund Monitoring", "Voice Advisory"])
-    
-    return page
-
-def render_financial_dashboard(startup_data, cash_flow_df):
-    """Render financial dashboard page."""
-    st.title("Financial Dashboard")
-    
-    # Key metrics
-    col1, col2, col3, col4 = st.columns(4)
-    
-    # Calculate runway
-    runway_months, runway_df = calculate_runway(
-        startup_data['cash'], 
-        startup_data['burn_rate'], 
-        startup_data['revenue'], 
-        startup_data['growth_rate']
-    )
-    
-    with col1:
-        st.metric("Current Cash", f"${startup_data['cash']:,}")
-    with col2:
-        st.metric("Monthly Burn", f"${startup_data['burn_rate']:,}")
-    with col3:
-        st.metric("Monthly Revenue", f"${startup_data['revenue']:,}")
-    with col4:
-        st.metric("Runway", f"{runway_months} months")
-    
-    # Financial charts
-    st.subheader("Financial Overview")
-    
-    tab1, tab2, tab3 = st.tabs(["Runway Projection", "Revenue vs. Expenses", "Burn Rate Trend"])
-    
-    with tab1:
-        # Runway projection chart
-        fig = px.line(runway_df.reset_index(), x='index', y='Cumulative_Cash', 
-                     title="Cash Runway Projection",
-                     labels={'index': 'Date', 'Cumulative_Cash': 'Remaining Cash'})
-        fig.add_hline(y=0, line_dash="dash", line_color="red")
-        fig.update_layout(height=400)
-        st.plotly_chart(fig, use_container_width=True)
-        
-        # Get analysis from Gemini
-        if setup_genai():
-            with st.expander("AI Financial Analysis"):
-                analysis = get_runway_analysis(startup_data)
-                st.write(analysis)
-    
-    with tab2:
-        # Revenue vs Expenses chart
-        rev_exp_df = cash_flow_df.copy()
-        fig = px.bar(rev_exp_df, x='Month', y=['Revenue', 'Total_Expenses'],
-                    title="Revenue vs. Expenses",
-                    barmode='group',
-                    labels={'value': 'Amount ($)', 'variable': 'Category'})
-        fig.update_layout(height=400)
-        st.plotly_chart(fig, use_container_width=True)
-    
-    with tab3:
-        # Burn rate trend
-        fig = px.line(cash_flow_df, x='Month', y='Net_Burn',
-                     title="Monthly Net Burn Trend",
-                     labels={'Net_Burn': 'Net Burn ($)'})
-        fig.update_layout(height=400)
-        st.plotly_chart(fig, use_container_width=True)
-    
-    # Expense breakdown
-    st.subheader("Expense Breakdown")
-    
-    # Last month expenses
-    last_month = cash_flow_df.iloc[-1]
-    expense_categories = ['Payroll', 'Marketing', 'Office', 'Software', 'Travel', 'Legal', 'Misc']
-    expense_values = [last_month[cat] for cat in expense_categories]
-    
-    fig = px.pie(values=expense_values, names=expense_categories, 
-                title="Current Month Expense Breakdown")
-    fig.update_layout(height=400)
-    st.plotly_chart(fig, use_container_width=True)
-
-def render_decision_simulator(startup_data):
-    """Render decision simulator page."""
-    st.title("Decision Simulator")
-    
-    st.write("Simulate the impact of business decisions on your startup's financial runway.")
-    
-    # Decision input form
-    with st.form("decision_form"):
-        st.subheader("Enter Decision Parameters")
-        
-        col1, col2 = st.columns(2)
-        
-        with col1:
-            new_hires = st.number_input("New Engineering Hires", min_value=0, max_value=10, value=0, 
-                                         help=f"Each engineer costs ${ENGINEER_SALARY:,} per month")
-            new_marketing = st.number_input("Additional Monthly Marketing Budget", min_value=0, max_value=50000, value=0, step=1000,
-                                           help="Additional marketing spend per month")
-        
-        with col2:
-            other_expenses = st.number_input("Other Additional Monthly Expenses", min_value=0, max_value=50000, value=0, step=1000,
-                                            help="Any other additional monthly expenses")
-            growth_impact = st.slider("Estimated Impact on Monthly Growth Rate", min_value=0.0, max_value=0.10, value=0.0, step=0.01, 
-                                     format="%.2f",
-                                     help="Estimated increase in monthly growth rate due to these investments")
-        
-        question = st.text_area("Describe your decision scenario", height=100, 
-                               placeholder="E.g., We're considering hiring two more engineers and increasing our marketing budget...")
-        
-        submitted = st.form_submit_button("Simulate Decision")
-    
-    if submitted:
-        # Calculate current and new runway
-        current_runway, new_runway, current_df, new_df = simulate_decision(
-            startup_data['cash'],
-            startup_data['burn_rate'],
-            startup_data['revenue'],
-            startup_data['growth_rate'],
-            other_expenses,
-            new_hires,
-            new_marketing,
-            growth_impact
-        )
-        
-        # Display results
-        st.subheader("Decision Impact")
-        
-        # Summary metrics
-        col1, col2, col3 = st.columns(3)
-        
-        with col1:
-            st.metric("Current Runway", f"{current_runway} months")
-        with col2:
-            st.metric("New Runway", f"{new_runway} months", delta=new_runway-current_runway)
-        with col3:
-            new_burn = startup_data['burn_rate'] + other_expenses + (new_hires * ENGINEER_SALARY) + new_marketing
-            st.metric("New Monthly Burn", f"${new_burn:,}", delta=new_burn-startup_data['burn_rate'], delta_color="inverse")
-        
-        # Cash projection comparison
-        st.subheader("Cash Projection Comparison")
-        
-        # Combine dataframes for comparison
-        current_df['Scenario'] = 'Current'
-        new_df['Scenario'] = 'After Decision'
-        
-        combined_df = pd.concat([current_df, new_df])
-        combined_df = combined_df.reset_index()
-        combined_df = combined_df.rename(columns={'index': 'Date'})
-        
-        # Plot comparison
-        fig = px.line(combined_df, x='Date', y='Cumulative_Cash', color='Scenario',
-                     title="Cash Runway Comparison",
-                     labels={'Cumulative_Cash': 'Remaining Cash'})
-        fig.add_hline(y=0, line_dash="dash", line_color="red")
-        fig.update_layout(height=400)
-        st.plotly_chart(fig, use_container_width=True)
-        
-        # Get AI analysis
-        if setup_genai() and question:
-            st.subheader("AI Analysis")
-            analysis = get_decision_analysis(question, startup_data)
-            st.success(analysis)
-
-def render_fund_monitoring(transactions_df):
-    """Render fund monitoring page."""
-    st.title("Investor Fund Monitoring")
-    
-    st.write("Monitor your startup's spending to maintain investor trust and ensure proper fund usage.")
-    
-    # Process transactions to detect suspicious ones
-    processed_df = detect_suspicious_transactions(transactions_df)
-    
-    # Summary metrics
-    total_transactions = len(processed_df)
-    suspicious_transactions = processed_df[processed_df['Suspicious']].copy()
-    suspicious_count = len(suspicious_transactions)
-    suspicious_amount = suspicious_transactions['Amount'].sum()
-    total_amount = processed_df['Amount'].sum()
-    
-    col1, col2, col3 = st.columns(3)
-    
-    with col1:
-        st.metric("Total Transactions", total_transactions)
-    with col2:
-        st.metric("Flagged Transactions", suspicious_count, 
-                 delta=f"{suspicious_count/total_transactions:.1%}" if total_transactions > 0 else "0%")
-    with col3:
-        st.metric("Flagged Amount", f"${suspicious_amount:,}", 
-                delta=f"{suspicious_amount/total_amount:.1%}" if total_amount > 0 else "0%")
-    
-    # Tabs for different views
-    tab1, tab2 = st.tabs(["Flagged Transactions", "All Transactions"])
-    
-    with tab1:
-        if suspicious_count > 0:
-            st.dataframe(suspicious_transactions[['Date', 'Category', 'Vendor', 'Amount', 'Description', 'Reason']], 
-                        use_container_width=True)
-            
-            # Get AI analysis of suspicious transactions
-            if setup_genai():
-                st.subheader("AI Fraud Analysis")
-                analysis = get_fraud_analysis(suspicious_transactions)
-                st.warning(analysis)
-        else:
-            st.success("No suspicious transactions detected.")
-    
-    with tab2:
-        st.dataframe(processed_df[['Date', 'Category', 'Vendor', 'Amount', 'Description', 'Suspicious']], 
-                    use_container_width=True)
-    
-    # Spending patterns
-    st.subheader("Spending Patterns")
-    
-    # Category breakdown
-    category_spending = processed_df.groupby('Category')['Amount'].sum().reset_index()
-    
-    fig = px.bar(category_spending, x='Category', y='Amount',
-                title="Spending by Category",
-                labels={'Amount': 'Total Spent ($)'})
-    fig.update_layout(height=400)
-    st.plotly_chart(fig, use_container_width=True)
-    
-    # Time series of spending
-    processed_df['Date'] = pd.to_datetime(processed_df['Date'])
-    processed_df['Week'] = processed_df['Date'].dt.isocalendar().week
-    weekly_spending = processed_df.groupby(['Week', 'Category'])['Amount'].sum().reset_index()
-    
-    fig = px.line(weekly_spending, x='Week', y='Amount', color='Category',
-                title="Weekly Spending Trends",
-                labels={'Amount': 'Amount Spent ($)'})
-    fig.update_layout(height=400)
-    st.plotly_chart(fig, use_container_width=True)
-
-def render_voice_advisory(startup_data):
-    """Render voice advisory page."""
-    st.title("Financial Advisory")
-    
-    st.write("Get strategic financial guidance from our AI advisor.")
-    
-    # Common financial questions
-    st.subheader("Select a Question or Ask Your Own")
-    
-    common_questions = [
-        "Select a question...",
-        "How much runway do we have at our current burn rate?",
-        "If we hire two more engineers, how will it affect our runway?",
-        "Should we increase our marketing spend given our current growth rate?",
-        "Is our current burn rate sustainable given our revenue growth?",
-        "Should we consider raising a bridge round in the next 3 months?",
-        "What's our projected cash position at the end of next quarter?",
-        "Are there any concerning spending patterns in our recent expenses?",
-        "What metrics should we focus on improving before our next fundraise?"
-    ]
-    
-    selected_question = st.selectbox("Common Questions", common_questions)
-    
-    custom_question = st.text_area("Or ask your own question", height=100,
-                                 placeholder="Enter your financial question here...")
-    
-    question = custom_question if custom_question else (selected_question if selected_question != common_questions[0] else "")
-    
-    if st.button("Get Advice") and question and setup_genai():
-        with st.spinner("Generating advisory response..."):
-            advice = get_advisory_guidance(question, startup_data)
-            
-            # Display advice
-            st.subheader("Financial Advice")
-            st.info(advice)
-            
-            # Note about voice generation
-            st.write("In the full version, this advice would be delivered as a voice response using ElevenLabs.")
-            
-            # Add a sample conversation
-            with st.expander("View sample conversation"):
-                st.write("**You:** " + question)
-                st.write("**Financial Advisor:** " + advice)
-
-# Main application
-def main():
-    # Load sample data
-    startup_data, cash_flow_df, transactions_df = load_sample_data()
-    
-    # Create sidebar and get selected page
-    page = create_sidebar()
-    
-    # Render selected page
-    if page == "Financial Dashboard":
-        render_financial_dashboard(startup_data, cash_flow_df)
-    elif page == "Decision Simulator":
-        render_decision_simulator(startup_data)
-    elif page == "Fund Monitoring":
-        render_fund_monitoring(transactions_df)
-    elif page == "Voice Advisory":
-        render_voice_advisory(startup_data)
-
-if __name__ == "__main__":
-    main()