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pages/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 os
+from datetime import datetime, timedelta, date
+import time
+import json
+import google.generativeai as genai
+from google.generativeai.types import HarmCategory, HarmBlockThreshold
+
+# Initialize page configuration
+st.set_page_config(
+    page_title="StartupFinancePilot",
+    page_icon="💰",
+    layout="wide",
+    initial_sidebar_state="expanded"
+)
+
+# Custom CSS
+st.markdown("""
+<style>
+    .main-header {
+        font-size: 2.5rem;
+        color: #0066cc;
+        margin-bottom: 0.5rem;
+    }
+    .sub-header {
+        font-size: 1.5rem;
+        color: #5c5c5c;
+        margin-bottom: 1.5rem;
+    }
+    .metric-card {
+        background-color: #f8f9fa;
+        border-radius: 10px;
+        padding: 20px;
+        box-shadow: 0 4px 6px rgba(0,0,0,0.1);
+    }
+    .metric-label {
+        font-size: 1rem;
+        color: #5c5c5c;
+    }
+    .metric-value {
+        font-size: 1.8rem;
+        color: #0066cc;
+        font-weight: bold;
+    }
+    .good-metric {
+        color: #28a745;
+    }
+    .warning-metric {
+        color: #ffc107;
+    }
+    .danger-metric {
+        color: #dc3545;
+    }
+    .advisor-card {
+        background-color: #f0f7ff;
+        border-radius: 10px;
+        padding: 20px;
+        box-shadow: 0 4px 6px rgba(0,0,0,0.1);
+        margin-bottom: 20px;
+    }
+    .advice-text {
+        font-size: 1.1rem;
+        line-height: 1.6;
+        color: #333;
+    }
+    .insight-card {
+        background-color: #f0f8ff;
+        border-left: 4px solid #0066cc;
+        padding: 15px;
+        margin-bottom: 15px;
+        border-radius: 4px;
+    }
+    .ai-badge {
+        background-color: #0066cc;
+        color: white;
+        padding: 3px 10px;
+        border-radius: 10px;
+        font-size: 0.8rem;
+        margin-bottom: 10px;
+        display: inline-block;
+    }
+    .booking-card {
+        background-color: white;
+        border-radius: 10px;
+        padding: 20px;
+        box-shadow: 0 4px 6px rgba(0,0,0,0.1);
+        margin-bottom: 20px;
+    }
+    .session-type {
+        font-size: 1.2rem;
+        font-weight: bold;
+        color: #0066cc;
+    }
+    .session-duration {
+        color: #5c5c5c;
+        font-size: 0.9rem;
+    }
+    .session-price {
+        font-size: 1.1rem;
+        font-weight: bold;
+        color: #28a745;
+    }
+</style>
+""", unsafe_allow_html=True)
+
+# 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
+
+# Initialize session state variables
+if 'booked_sessions' not in st.session_state:
+    st.session_state.booked_sessions = []
+if 'chat_history' not in st.session_state:
+    st.session_state.chat_history = []
+if 'audio_response' not in st.session_state:
+    st.session_state.audio_response = None
+if 'insights_cache' not in st.session_state:
+    st.session_state.insights_cache = {}
+if 'gemini_model' not in st.session_state:
+    st.session_state.gemini_model = None
+
+# 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 AI Services
+def setup_genai():
+    """Initialize and configure Google's Generative AI and list available models"""
+    try:
+        if 'GOOGLE_API_KEY' in st.secrets:
+            genai.configure(api_key=st.secrets['GOOGLE_API_KEY'])
+            
+            # Get available models and select one for text generation
+            models = genai.list_models()
+            text_models = [m.name for m in models if 'generateContent' in m.supported_generation_methods]
+            
+            if text_models:
+                # Use first available text generation model
+                model_name = text_models[0]
+                st.session_state.gemini_model = model_name
+                return True
+            else:
+                st.warning("No appropriate generative AI models available")
+                # Use a fallback model name for demonstration
+                st.session_state.gemini_model = "gemini-1.5-pro"
+                return False
+        else:
+            st.warning("Google API key not found in secrets. Using simulated AI responses.")
+            st.session_state.gemini_model = "gemini-1.5-pro"
+            return False
+    except Exception as e:
+        st.warning(f"Failed to initialize Gemini: {e}. Using simulated AI responses.")
+        st.session_state.gemini_model = "gemini-1.5-pro"
+        return False
+
+def generate_ai_response(prompt, simulate=False):
+    """Generate response from Gemini or simulate one if the API is unavailable"""
+    if simulate:
+        # Simulate AI response with predefined text based on keywords in prompt
+        time.sleep(1)  # Simulate processing time
+        
+        if "runway" in prompt.lower():
+            return "Based on your current spend rate of $85K/month and revenue growth of 8%, your runway is approximately 3.8 months. I recommend reducing non-essential expenses to extend runway to at least 6 months before your next fundraising round."
+        elif "hire" in prompt.lower() or "hiring" in prompt.lower():
+            return "Adding new hires at this stage would reduce your runway significantly. Consider contracting talent first or postponing hiring until after securing additional funding. Each new engineer costs $10K/month, reducing runway by approximately 3 weeks per hire."
+        elif "marketing" in prompt.lower():
+            return "Your current CAC to LTV ratio doesn't justify increasing marketing spend. Focus on optimizing current channels and improving conversion rates. Once unit economics improve, gradually increase marketing budget by no more than 20% per month."
+        elif "fundraising" in prompt.lower() or "investor" in prompt.lower():
+            return "With less than 4 months of runway, you should begin fundraising preparations immediately. Focus on demonstrating product-market fit and improving key metrics like MRR growth, user retention, and unit economics before approaching investors."
+        elif "suspicious" in prompt.lower() or "transaction" in prompt.lower():
+            return "I've identified several concerning transactions including a $8,500 travel expense and $12,000 in office upgrades. These discretionary expenses represent over 25% of a month's burn and should be reviewed with your finance team immediately."
+        else:
+            return "Based on your financial data, I recommend prioritizing runway extension and focusing on core metrics that demonstrate product-market fit. Consider reducing non-essential expenses by 15-20% to add 1-2 months to your runway before beginning fundraising conversations."
+    else:
+        try:
+            # Use the actual Gemini model
+            model = genai.GenerativeModel(st.session_state.gemini_model)
+            
+            generation_config = {
+                "temperature": 0.7,
+                "top_p": 0.95,
+                "top_k": 40,
+                "max_output_tokens": 1024,
+            }
+            
+            safety_settings = {
+                HarmCategory.HARM_CATEGORY_HATE_SPEECH: HarmBlockThreshold.BLOCK_NONE,
+                HarmCategory.HARM_CATEGORY_HARASSMENT: HarmBlockThreshold.BLOCK_NONE,
+                HarmCategory.HARM_CATEGORY_SEXUALLY_EXPLICIT: HarmBlockThreshold.BLOCK_NONE,
+                HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT: HarmBlockThreshold.BLOCK_NONE
+            }
+            
+            response = model.generate_content(
+                prompt,
+                generation_config=generation_config,
+                safety_settings=safety_settings
+            )
+            
+            return response.text
+        except Exception as e:
+            st.warning(f"Error generating AI response: {e}")
+            # Fall back to simulated response
+            return generate_ai_response(prompt, simulate=True)
+
+# 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):
+    """AI-enhanced 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'] = ""
+    df['Risk_Score'] = 0
+    
+    # Check for suspicious patterns
+    for idx, row in df.iterrows():
+        reasons = []
+        risk_score = 0
+        
+        # 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']}")
+                risk_score += 30
+                
+                # Higher risk for significantly exceeding threshold
+                excess_percentage = (row['Amount'] - category_thresholds[row['Category']]) / category_thresholds[row['Category']] * 100
+                if excess_percentage > 100:  # More than double the threshold
+                    risk_score += 20
+        
+        # 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")
+            risk_score += 25
+        
+        if any(term in str(row['Description']).lower() for term in suspicious_terms):
+            reasons.append(f"Description contains suspicious term")
+            risk_score += 20
+        
+        # Check for rounded amounts (potential indicator of estimation/fabrication)
+        if row['Amount'] % 1000 == 0 and row['Amount'] > 3000:
+            reasons.append(f"Suspiciously round amount")
+            risk_score += 15
+        
+        # Mark as suspicious if risk score is high enough
+        if risk_score >= 30:
+            df.at[idx, 'Suspicious'] = True
+            df.at[idx, 'Reason'] = "; ".join(reasons)
+            df.at[idx, 'Risk_Score'] = risk_score
+    
+    # Sort by risk score
+    df = df.sort_values(by='Risk_Score', ascending=False)
+    
+    return df
+
+# Import page functions
+from dashboard_page import render_financial_dashboard, get_runway_analysis, get_fundraising_readiness_analysis
+from decision_simulator import render_decision_simulator, get_decision_analysis
+from fund_monitoring import render_fund_monitoring, get_fraud_analysis
+from financial_advisor import render_ai_financial_advisor, get_advisory_guidance, generate_voice_response
+from book_session import render_book_session
+
+# 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']}")
+    
+    # AI Status
+    has_api = setup_genai()
+    ai_status = "🟢 Connected" if has_api else "🟡 Demo Mode"
+    st.sidebar.write(f"AI Status: {ai_status}")
+    if not has_api:
+        st.sidebar.info("Running in demo mode with simulated AI responses. Add GOOGLE_API_KEY to secrets for full functionality.")
+    
+    # App navigation
+    st.sidebar.header("Navigation")
+    page = st.sidebar.radio("Go to", [
+        "Financial Dashboard", 
+        "Decision Simulator", 
+        "Fund Monitoring", 
+        "AI Financial Advisor",
+        "Book a Session"
+    ])
+    
+    return page
+
+# 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 == "AI Financial Advisor":
+        render_ai_financial_advisor(startup_data)
+    elif page == "Book a Session":
+        render_book_session()
+
+if __name__ == "__main__":
+    main()

pages/dashboard-page.py

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+def render_financial_dashboard(startup_data, cash_flow_df):
+    """
+    Render the AI-powered financial dashboard page.
+    
+    This dashboard uses AI to analyze financial data and provide actionable insights
+    to startup founders, helping them make better decisions about their runway,
+    spending, and financial health.
+    """
+    st.markdown("<h1 class='main-header'>Financial Dashboard</h1>", unsafe_allow_html=True)
+    st.markdown("<p class='sub-header'>AI-powered financial insights at a glance</p>", unsafe_allow_html=True)
+    
+    # How AI helps with financial dashboards
+    with st.expander("ℹ️ How AI enhances your financial dashboard"):
+        st.markdown("""
+        ### How AI Powers Your Financial Dashboard
+        
+        The financial dashboard uses AI to transform raw financial data into actionable intelligence:
+        
+        - **Automated Analysis**: Instead of manually calculating runway and burn rates, our AI model analyzes your data and highlights critical trends
+        - **Predictive Forecasting**: AI forecasts your runway using pattern recognition and predictive analytics to account for varying growth rates
+        - **Anomaly Detection**: The system identifies unusual spending patterns or concerning financial trends that human analysis might miss
+        - **Strategic Recommendations**: Based on your specific financial situation, the AI provides tailored recommendations to optimize your runway
+        - **Benchmark Comparison**: Your metrics are automatically compared against industry standards for startups at your funding stage
+        
+        This helps founders save time, catch financial issues early, and make data-driven decisions without needing financial expertise.
+        """)
+    
+    # AI Insights Summary
+    insights_key = f"dashboard_{date.today().isoformat()}"
+    if insights_key not in st.session_state.insights_cache:
+        insights = generate_ai_response(f"""
+        You are a financial advisor for startups. Based on this startup's data:
+        - Current cash: ${startup_data['cash']}
+        - Monthly burn rate: ${startup_data['burn_rate']}
+        - Monthly revenue: ${startup_data['revenue']}
+        - Monthly growth rate: {startup_data['growth_rate'] * 100}%
+        
+        Provide the top 3 most important financial insights that the founder should know today.
+        Format each insight as a brief, action-oriented bullet point.
+        """, simulate=True)
+        st.session_state.insights_cache[insights_key] = insights
+    
+    with st.expander("📊 AI Financial Insights", expanded=True):
+        st.markdown("<span class='ai-badge'>AI-Generated Insights</span>", unsafe_allow_html=True)
+        st.markdown(st.session_state.insights_cache[insights_key])
+    
+    # Key metrics
+    col1, col2, col3, col4 = st.columns(4)
+    
+    # Calculate runway
+    runway_months, runway_df = calculate_runway(
+        startup_data['cash'], 
+        startup_data['burn_rate'], 
+        startup_data['revenue'], 
+        startup_data['growth_rate']
+    )
+    
+    # Determine status colors based on financial health indicators
+    runway_status = "danger-metric" if runway_months < 6 else ("warning-metric" if runway_months < 9 else "good-metric")
+    burn_status = "danger-metric" if startup_data['burn_rate'] > 100000 else ("warning-metric" if startup_data['burn_rate'] > 80000 else "good-metric")
+    revenue_status = "good-metric" if startup_data['revenue'] > 20000 else ("warning-metric" if startup_data['revenue'] > 10000 else "danger-metric")
+    
+    with col1:
+        st.markdown(f"""
+        <div class='metric-card'>
+            <p class='metric-label'>Current Cash</p>
+            <p class='metric-value'>${startup_data['cash']:,}</p>
+        </div>
+        """, unsafe_allow_html=True)
+    
+    with col2:
+        st.markdown(f"""
+        <div class='metric-card'>
+            <p class='metric-label'>Monthly Burn</p>
+            <p class='metric-value {burn_status}'>${startup_data['burn_rate']:,}</p>
+        </div>
+        """, unsafe_allow_html=True)
+    
+    with col3:
+        st.markdown(f"""
+        <div class='metric-card'>
+            <p class='metric-label'>Monthly Revenue</p>
+            <p class='metric-value {revenue_status}'>${startup_data['revenue']:,}</p>
+        </div>
+        """, unsafe_allow_html=True)
+    
+    with col4:
+        st.markdown(f"""
+        <div class='metric-card'>
+            <p class='metric-label'>Runway</p>
+            <p class='metric-value {runway_status}'>{runway_months} months</p>
+        </div>
+        """, unsafe_allow_html=True)
+    
+    # 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 ($)'},
+                     color_discrete_sequence=['#0066cc'])
+        fig.add_hline(y=0, line_dash="dash", line_color="red", annotation_text="Out of Cash")
+        fig.update_layout(
+            height=400,
+            plot_bgcolor='rgba(240,247,255,0.8)',
+            xaxis_title="Date",
+            yaxis_title="Cash Balance ($)",
+            font=dict(family="Arial, sans-serif", size=12),
+            margin=dict(l=20, r=20, t=40, b=20),
+        )
+        st.plotly_chart(fig, use_container_width=True)
+        
+        # Get analysis from Gemini
+        with st.expander("🔍 AI Financial Analysis", expanded=True):
+            # Use cache to avoid repeated API calls
+            analysis_key = f"runway_{date.today().isoformat()}"
+            if analysis_key not in st.session_state.insights_cache:
+                analysis = get_runway_analysis(startup_data)
+                st.session_state.insights_cache[analysis_key] = analysis
+                
+            st.markdown("<span class='ai-badge'>AI Financial Analysis</span>", unsafe_allow_html=True)
+            st.markdown(st.session_state.insights_cache[analysis_key])
+    
+    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 ($)', 'variable': 'Category'},
+                    color_discrete_sequence=['#28a745', '#dc3545'])
+        fig.update_layout(
+            height=400,
+            plot_bgcolor='rgba(240,247,255,0.8)',
+            xaxis_title="Month",
+            yaxis_title="Amount ($)",
+            font=dict(family="Arial, sans-serif", size=12),
+            legend_title="",
+            margin=dict(l=20, r=20, t=40, b=20),
+        )
+        st.plotly_chart(fig, use_container_width=True)
+        
+        # Calculate revenue growth
+        revenue_growth = [(cash_flow_df['Revenue'].iloc[i] / cash_flow_df['Revenue'].iloc[i-1] - 1) * 100 if i > 0 else 0 
+                          for i in range(len(cash_flow_df))]
+        avg_growth = sum(revenue_growth[1:]) / len(revenue_growth[1:])
+        
+        col1, col2 = st.columns(2)
+        with col1:
+            st.metric("Average Monthly Revenue Growth", f"{avg_growth:.1f}%")
+        with col2:
+            expense_growth = (cash_flow_df['Total_Expenses'].iloc[-1] / cash_flow_df['Total_Expenses'].iloc[0] - 1) * 100
+            st.metric("Total Expense Growth", f"{expense_growth:.1f}%", delta=f"{expense_growth - avg_growth:.1f}%", delta_color="inverse")
+    
+    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 ($)'},
+                     color_discrete_sequence=['#dc3545'])
+        fig.update_layout(
+            height=400,
+            plot_bgcolor='rgba(240,247,255,0.8)',
+            xaxis_title="Month",
+            yaxis_title="Net Burn ($)",
+            font=dict(family="Arial, sans-serif", size=12),
+            margin=dict(l=20, r=20, t=40, b=20),
+        )
+        
+        # Add efficiency ratio as a second y-axis
+        efficiency_ratio = [cash_flow_df['Revenue'].iloc[i] / cash_flow_df['Total_Expenses'].iloc[i] * 100 
+                            for i in range(len(cash_flow_df))]
+        
+        fig.add_trace(go.Scatter(
+            x=cash_flow_df['Month'], 
+            y=efficiency_ratio,
+            name='Efficiency Ratio (%)',
+            yaxis='y2',
+            line=dict(color='#0066cc', width=2, dash='dot')
+        ))
+        
+        fig.update_layout(
+            yaxis2=dict(
+                title='Efficiency Ratio (%)',
+                overlaying='y',
+                side='right',
+                range=[0, max(efficiency_ratio) * 1.2]
+            )
+        )
+        
+        st.plotly_chart(fig, use_container_width=True)
+        
+        with st.expander("🔎 Understanding Efficiency Ratio"):
+            st.info("The efficiency ratio measures how efficiently your startup is generating revenue relative to expenses. A higher percentage means you're getting more revenue per dollar spent. Venture-backed startups typically aim for at least 40% before Series B funding.")
+    
+    # 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]
+    
+    col1, col2 = st.columns([2, 1])
+    
+    with col1:
+        fig = px.pie(values=expense_values, names=expense_categories, 
+                    title="Current Month Expense Breakdown",
+                    color_discrete_sequence=px.colors.sequential.Blues_r)
+        fig.update_layout(
+            height=400,
+            font=dict(family="Arial, sans-serif", size=12),
+            margin=dict(l=20, r=20, t=40, b=20),
+        )
+        fig.update_traces(textposition='inside', textinfo='percent+label')
+        st.plotly_chart(fig, use_container_width=True)
+    
+    with col2:
+        # Expense analysis
+        st.markdown("<h4>Expense Analysis</h4>", unsafe_allow_html=True)
+        
+        # Calculate industry benchmarks (simulated)
+        benchmarks = {
+            "Payroll": "70-80%",
+            "Marketing": "10-15%",
+            "Office": "5-8%",
+            "Software": "3-5%"
+        }
+        
+        # Create a table with expense categories, amounts, and % of total
+        expense_df = pd.DataFrame({
+            "Category": expense_categories,
+            "Amount": expense_values,
+            "% of Total": [v / sum(expense_values) * 100 for v in expense_values]
+        })
+        
+        # Add benchmark column
+        expense_df["Industry Benchmark"] = expense_df["Category"].map(
+            lambda x: benchmarks.get(x, "N/A")
+        )
+        
+        # Format the dataframe for display
+        formatted_df = expense_df.copy()
+        formatted_df["Amount"] = formatted_df["Amount"].apply(lambda x: f"${x:,.0f}")
+        formatted_df["% of Total"] = formatted_df["% of Total"].apply(lambda x: f"{x:.1f}%")
+        
+        st.table(formatted_df)
+        
+        # AI-powered spending optimization
+        with st.expander("💡 AI Spending Optimization"):
+            st.markdown("<span class='ai-badge'>AI Recommendation</span>", unsafe_allow_html=True)
+            
+            # Use cache to avoid repeated API calls
+            spending_key = f"spending_{date.today().isoformat()}"
+            if spending_key not in st.session_state.insights_cache:
+                spending_recommendation = generate_ai_response("""
+                Based on your expense breakdown, recommend 2-3 specific ways to optimize spending to extend runway.
+                Focus on industry best practices for seed-stage startups.
+                """, simulate=True)
+                st.session_state.insights_cache[spending_key] = spending_recommendation
+                
+            st.markdown(st.session_state.insights_cache[spending_key])
+            
+    # Fundraising Readiness Assessment
+    st.subheader("Fundraising Readiness")
+    
+    # Get AI analysis of fundraising readiness
+    fundraising_key = f"fundraising_{date.today().isoformat()}"
+    if fundraising_key not in st.session_state.insights_cache:
+        fundraising_analysis = get_fundraising_readiness_analysis(startup_data, cash_flow_df)
+        st.session_state.insights_cache[fundraising_key] = fundraising_analysis
+    
+    st.markdown("<div class='advisor-card'>", unsafe_allow_html=True)
+    st.markdown("<span class='ai-badge'>AI Fundraising Assessment</span>", unsafe_allow_html=True)
+    st.markdown(f"<p class='advice-text'>{st.session_state.insights_cache[fundraising_key]}</p>", unsafe_allow_html=True)
+    st.markdown("</div>", unsafe_allow_html=True)
+    
+    # Call-to-action for advisor
+    st.info("📅 Need personalized guidance on fundraising? [Book a session](#book-a-session) with our AI financial advisor.")
+
+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}%
+
+    Provide a detailed analysis of their runway and financial health. Include:
+    1. Exact runway calculation in months
+    2. Assessment of financial health (critical, concerning, stable, or healthy)
+    3. Benchmarks compared to similar seed-stage startups
+    4. Three specific, actionable recommendations to improve runway
+    5. Key metrics they should focus on
+
+    Format your response in a structured, easy-to-read format with clear sections and bullet points.
+    """
+    
+    return generate_ai_response(prompt)
+
+def get_fundraising_readiness_analysis(startup_data, cash_flow_df):
+    """Get AI analysis of fundraising readiness."""
+    metrics = {
+        "MRR Growth": f"{(cash_flow_df['Revenue'].iloc[-1] / cash_flow_df['Revenue'].iloc[-2] - 1) * 100:.1f}%",
+        "Gross Margin": f"{(cash_flow_df['Revenue'].iloc[-1] - cash_flow_df['Total_Expenses'].iloc[-1] / 2) / cash_flow_df['Revenue'].iloc[-1] * 100:.1f}%",
+        "CAC": "$950",  # Example value
+        "LTV": "$4,500",  # Example value
+        "Churn": "3.2%",  # Example value
+    }
+    
+    metrics_text = "\n".join([f"- {k}: {v}" for k, v in metrics.items()])
+    
+    prompt = f"""
+    You are a startup fundraising advisor. Analyze this startup's readiness for their next funding round:
+    
+    Company Profile:
+    - Stage: {startup_data['stage']}
+    - Last Funding: {startup_data['last_funding']}
+    - Current Cash: ${startup_data['cash']}
+    - Monthly Burn: ${startup_data['burn_rate']}
+    - Runway: {startup_data['cash'] / (startup_data['burn_rate'] - startup_data['revenue']):.1f} months
+    
+    Key Metrics:
+    {metrics_text}
+    
+    Provide a comprehensive fundraising readiness assessment:
+    1. Overall fundraising readiness score (0-10)
+    2. Assessment of current metrics compared to investor expectations for next round
+    3. Identify the 3 most critical metrics to improve before fundraising
+    4. Recommend specific targets for each key metric
+    5. Suggest timeline and specific milestones for fundraising preparation
+    6. Estimate reasonable valuation range based on metrics and market conditions
+    
+    Be specific with numbers, timelines, and actionable targets.
+    """
+    
+    return generate_ai_response(prompt)

pages/decision-simulator.py

@@ -0,0 +1,321 @@
+def render_decision_simulator(startup_data):
+    """
+    Render the AI-powered decision simulator page.
+    
+    This feature helps startup founders test the financial impact of business
+    decisions before implementing them, using AI to analyze risks and benefits.
+    """
+    st.markdown("<h1 class='main-header'>Decision Simulator</h1>", unsafe_allow_html=True)
+    st.markdown("<p class='sub-header'>AI-powered analysis of business decisions</p>", unsafe_allow_html=True)
+    
+    # How AI helps with decision-making
+    with st.expander("ℹ️ How AI enhances your decision-making"):
+        st.markdown("""
+        ### How AI Powers Your Decision Simulator
+        
+        The decision simulator uses AI to help you make better strategic decisions:
+        
+        - **Scenario Analysis**: Our AI model simulates multiple financial scenarios based on your input variables
+        - **Risk Assessment**: The system automatically evaluates risk levels based on your cash runway and growth metrics
+        - **Return Prediction**: AI algorithms predict potential returns on investments like hiring or marketing
+        - **Opportunity Cost Analysis**: The model compares different allocations of capital to maximize growth
+        - **Personalized Recommendations**: Based on your specific situation, the AI provides tailored alternatives
+        
+        This helps founders make data-driven decisions with less guesswork, avoid costly mistakes, and optimize resource allocation.
+        """)
+    
+    st.write("Test the financial impact of key business decisions before implementing them. Our AI advisor will analyze the risks and benefits.")
+    
+    # Quick decision templates
+    st.subheader("Common Scenarios")
+    
+    decision_templates = {
+        "Hiring Engineering Team": {
+            "description": "Evaluate the impact of growing your engineering team",
+            "new_hires": 3,
+            "new_marketing": 0,
+            "other_expenses": 2000,
+            "growth_impact": 0.02,
+            "question": "We're considering hiring 3 more engineers to accelerate product development. How will this affect our runway and what growth impact should we expect to justify this investment?"
+        },
+        "Marketing Expansion": {
+            "description": "Test increasing your marketing budget",
+            "new_hires": 0,
+            "new_marketing": 15000,
+            "other_expenses": 0,
+            "growth_impact": 0.04,
+            "question": "We want to increase our marketing spend by $15K/month to drive growth. What growth rate would we need to achieve to make this financially viable?"
+        },
+        "Office Expansion": {
+            "description": "Analyze the cost of moving to a larger office",
+            "new_hires": 0,
+            "new_marketing": 0,
+            "other_expenses": 8000,
+            "growth_impact": 0.01,
+            "question": "We're considering moving to a larger office space that would add $8K/month to our expenses. Is this justified at our current stage?"
+        },
+        "Custom Scenario": {
+            "description": "Create your own custom scenario",
+            "new_hires": 0,
+            "new_marketing": 0,
+            "other_expenses": 0,
+            "growth_impact": 0.0,
+            "question": ""
+        }
+    }
+    
+    # Template selection
+    template_cols = st.columns(4)
+    selected_template = None
+    
+    for i, (template_name, template) in enumerate(decision_templates.items()):
+        with template_cols[i]:
+            if st.button(f"{template_name}\n{template['description']}", key=f"template_{i}"):
+                selected_template = template_name
+    
+    # Initialize form values based on selected template
+    if selected_template and selected_template != "Custom Scenario":
+        new_hires = decision_templates[selected_template]["new_hires"]
+        new_marketing = decision_templates[selected_template]["new_marketing"]
+        other_expenses = decision_templates[selected_template]["other_expenses"]
+        growth_impact = decision_templates[selected_template]["growth_impact"]
+        question = decision_templates[selected_template]["question"]
+    else:
+        new_hires = 0
+        new_marketing = 0
+        other_expenses = 0
+        growth_impact = 0.0
+        question = ""
+    
+    # Decision input form
+    with st.form("decision_form"):
+        st.subheader("Scenario Parameters")
+        
+        col1, col2 = st.columns(2)
+        
+        with col1:
+            new_hires = st.number_input("New Engineering Hires", min_value=0, max_value=10, value=new_hires, 
+                                       help=f"Each engineer costs ${ENGINEER_SALARY:,} per month")
+            st.caption(f"Monthly Cost: ${new_hires * ENGINEER_SALARY:,}")
+            
+            new_marketing = st.number_input("Additional Monthly Marketing Budget", 
+                                           min_value=0, max_value=50000, value=new_marketing, 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=other_expenses, 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=growth_impact, step=0.01, 
+                                     format="%.2f",
+                                     help="Estimated increase in monthly growth rate due to these investments")
+            st.caption(f"New Growth Rate: {(startup_data['growth_rate'] + growth_impact) * 100:.1f}% (current: {startup_data['growth_rate'] * 100:.1f}%)")
+        
+        question = st.text_area("Describe your decision scenario", 
+                               value=question,
+                               height=100, 
+                               placeholder="E.g., We're considering hiring two more engineers and increasing our marketing budget...")
+        
+        decision_summary = f"""
+        - {new_hires} new engineers: ${new_hires * ENGINEER_SALARY:,}/month
+        - Marketing increase: ${new_marketing:,}/month
+        - Other expenses: ${other_expenses:,}/month
+        - Total additional burn: ${new_hires * ENGINEER_SALARY + new_marketing + other_expenses:,}/month
+        - Growth impact: +{growth_impact * 100:.1f}% monthly growth
+        """
+        
+        st.markdown(f"**Decision Summary:**\n{decision_summary}")
+        
+        submitted = st.form_submit_button("Simulate Decision")
+    
+    if submitted:
+        # Calculate current and new runway
+        current_runway, new_runway, current_df, new_df = simulate_decision(
+            startup_data['cash'],
+            startup_data['burn_rate'],
+            startup_data['revenue'],
+            startup_data['growth_rate'],
+            other_expenses,
+            new_hires,
+            new_marketing,
+            growth_impact
+        )
+        
+        # Display results
+        st.markdown("<h3>Decision Impact Analysis</h3>", unsafe_allow_html=True)
+        
+        # Summary metrics
+        col1, col2, col3 = st.columns(3)
+        
+        with col1:
+            st.metric("Current Runway", f"{current_runway} months")
+        with col2:
+            runway_change = new_runway - current_runway
+            st.metric("New Runway", f"{new_runway} months", 
+                     delta=f"{runway_change} months", 
+                     delta_color="off" if runway_change == 0 else ("normal" if runway_change > 0 else "inverse"))
+        with col3:
+            new_burn = startup_data['burn_rate'] + other_expenses + (new_hires * ENGINEER_SALARY) + new_marketing
+            burn_change = new_burn - startup_data['burn_rate']
+            burn_percentage = burn_change / startup_data['burn_rate'] * 100
+            st.metric("New Monthly Burn", f"${new_burn:,}", 
+                     delta=f"${burn_change:,} ({burn_percentage:.1f}%)", 
+                     delta_color="inverse")
+        
+        # Cash projection comparison
+        st.subheader("Cash Projection Comparison")
+        
+        # Combine dataframes for comparison
+        current_df['Scenario'] = 'Current'
+        new_df['Scenario'] = 'After Decision'
+        
+        combined_df = pd.concat([current_df, new_df])
+        combined_df = combined_df.reset_index()
+        combined_df = combined_df.rename(columns={'index': 'Date'})
+        
+        # Plot comparison
+        fig = px.line(combined_df, x='Date', y='Cumulative_Cash', color='Scenario',
+                     title="Cash Runway Comparison",
+                     labels={'Cumulative_Cash': 'Remaining Cash'},
+                     color_discrete_sequence=['#4c78a8', '#f58518'])
+        
+        fig.add_hline(y=0, line_dash="dash", line_color="red", annotation_text="Out of Cash")
+        
+        fig.update_layout(
+            height=400,
+            plot_bgcolor='rgba(240,247,255,0.8)',
+            xaxis_title="Date",
+            yaxis_title="Cash Balance ($)",
+            font=dict(family="Arial, sans-serif", size=12),
+            margin=dict(l=20, r=20, t=40, b=20),
+        )
+        
+        st.plotly_chart(fig, use_container_width=True)
+        
+        # Get AI analysis
+        if question:
+            decision_params = {
+                "new_hires": new_hires,
+                "new_marketing": new_marketing,
+                "other_expenses": other_expenses,
+                "growth_impact": growth_impact
+            }
+            
+            analysis = get_decision_analysis(question, startup_data, decision_params)
+            
+            st.markdown("<div class='advisor-card'>", unsafe_allow_html=True)
+            st.markdown("<span class='ai-badge'>AI Decision Analysis</span>", unsafe_allow_html=True)
+            st.markdown(f"<p class='advice-text'>{analysis}</p>", unsafe_allow_html=True)
+            st.markdown("</div>", unsafe_allow_html=True)
+            
+            # Break down impacts of each component
+            st.subheader("Component Impact Analysis")
+            
+            # Calculate the impact of each component in isolation
+            _, hiring_runway, _, _ = simulate_decision(
+                startup_data['cash'],
+                startup_data['burn_rate'],
+                startup_data['revenue'],
+                startup_data['growth_rate'],
+                0,
+                new_hires,
+                0,
+                0
+            )
+            
+            _, marketing_runway, _, _ = simulate_decision(
+                startup_data['cash'],
+                startup_data['burn_rate'],
+                startup_data['revenue'],
+                startup_data['growth_rate'],
+                0,
+                0,
+                new_marketing,
+                growth_impact
+            )
+            
+            _, expenses_runway, _, _ = simulate_decision(
+                startup_data['cash'],
+                startup_data['burn_rate'],
+                startup_data['revenue'],
+                startup_data['growth_rate'],
+                other_expenses,
+                0,
+                0,
+                0
+            )
+            
+            component_data = {
+                "Component": ["Engineering Hires", "Marketing Increase", "Other Expenses", "Combined Impact"],
+                "Runway Impact (months)": [
+                    hiring_runway - current_runway,
+                    marketing_runway - current_runway,
+                    expenses_runway - current_runway,
+                    new_runway - current_runway
+                ]
+            }
+            
+            component_df = pd.DataFrame(component_data)
+            
+            fig = px.bar(component_df, x='Component', y='Runway Impact (months)',
+                        title="Impact of Each Component on Runway",
+                        color='Runway Impact (months)',
+                        color_continuous_scale=['#dc3545', '#ffc107', '#28a745'],
+                        text='Runway Impact (months)')
+            
+            fig.update_layout(
+                height=400,
+                plot_bgcolor='rgba(240,247,255,0.8)',
+                font=dict(family="Arial, sans-serif", size=12),
+                margin=dict(l=20, r=20, t=40, b=20),
+            )
+            
+            fig.update_traces(texttemplate='%{text:.1f}', textposition='outside')
+            st.plotly_chart(fig, use_container_width=True)
+            
+            # Risk assessment
+            risk_level = "High" if new_runway < 3 else ("Medium" if new_runway < 6 else "Low")
+            risk_color = "danger-metric" if risk_level == "High" else ("warning-metric" if risk_level == "Medium" else "good-metric")
+            
+            st.markdown(f"""
+            <div class='metric-card'>
+                <p class='metric-label'>Risk Assessment</p>
+                <p class='metric-value {risk_color}'>{risk_level} Risk Decision</p>
+                <p>This decision would give you {new_runway} months of runway.</p>
+            </div>
+            """, unsafe_allow_html=True)
+            
+            # Recommendations
+            st.info("💡 **Want personalized guidance on this decision?** [Book a session](#book-a-session) with our AI financial advisor for detailed analysis.")
+
+def get_decision_analysis(question, financial_data, decision_params):
+    """Get AI analysis for a specific business 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: ${financial_data['revenue']}
+    - Monthly growth rate: {financial_data['growth_rate'] * 100}%
+    
+    They're considering these changes:
+    - Adding {decision_params['new_hires']} new engineers (${ENGINEER_SALARY}/month each)
+    - Increasing marketing budget by ${decision_params['new_marketing']}/month
+    - Adding ${decision_params['other_expenses']}/month in other expenses
+    - Expecting {decision_params['growth_impact'] * 100}% additional monthly growth
+
+    Analyze this decision thoroughly:
+    1. Quantify the impact on runway (exact calculation)
+    2. Assess the risk level (low, medium, high)
+    3. Compare the ROI potential
+    4. Provide 3 specific recommendations or alternatives
+    5. Suggest timeline and milestones for implementation if approved
+
+    Be direct and specific with numbers and timeframes.
+    """
+    
+    return generate_ai_response(prompt)

pages/financial-advisor.py

@@ -0,0 +1,210 @@
+def render_ai_financial_advisor(startup_data):
+    """
+    Render the AI financial advisor page with voice chat capabilities.
+    
+    This feature provides conversational financial guidance to founders through a 
+    natural chat interface with both text and voice responses.
+    """
+    st.markdown("<h1 class='main-header'>AI Financial Advisor</h1>", unsafe_allow_html=True)
+    st.markdown("<p class='sub-header'>Get expert financial guidance through our AI-powered advisor</p>", unsafe_allow_html=True)
+    
+    # How AI helps with financial advisory
+    with st.expander("ℹ️ How AI powers your financial advisor"):
+        st.markdown("""
+        ### How AI Powers Your Financial Advisor
+        
+        Our AI financial advisor combines advanced language models with financial expertise:
+        
+        - **Natural Language Understanding**: The system interprets complex financial questions in plain English
+        - **Domain-Specific Knowledge**: Our AI is trained on startup finance, venture capital, and financial modeling
+        - **Context-Aware Responses**: The advisor takes into account your specific financial situation and history
+        - **Voice Synthesis**: ElevenLabs voice technology creates natural, high-quality voice responses
+        - **Customized Guidance**: AI tailors advice specifically to your stage, industry, and financial position
+        
+        This gives founders 24/7 access to high-quality financial guidance without the high cost of consultants.
+        """)
+    
+    # Chat container
+    st.markdown("<div style='background-color: #f8f9fa; padding: 20px; border-radius: 10px; margin-bottom: 20px;'>", unsafe_allow_html=True)
+    
+    # Display chat history
+    st.subheader("Chat with your Financial Advisor")
+    
+    # Initialize chat history if needed
+    if 'chat_history' not in st.session_state:
+        st.session_state.chat_history = [
+            {"role": "assistant", "content": "Hi there! I'm your AI financial advisor. How can I help with your startup's finances today?"}
+        ]
+    
+    # Display chat messages
+    for message in st.session_state.chat_history:
+        if message["role"] == "user":
+            st.markdown(f"<div style='background-color: #e6f7ff; padding: 10px; border-radius: 10px; margin-bottom: 10px;'><strong>You:</strong> {message['content']}</div>", unsafe_allow_html=True)
+        else:
+            st.markdown(f"<div style='background-color: #f0f7ff; padding: 10px; border-radius: 10px; margin-bottom: 10px;'><strong>Financial Advisor:</strong> {message['content']}</div>", unsafe_allow_html=True)
+            
+            # Show play button for voice if it exists
+            if 'audio' in message and message['audio']:
+                st.audio(message['audio'], format='audio/mp3')
+    
+    # Input for new message
+    col1, col2 = st.columns([5, 1])
+    
+    with col1:
+        user_input = st.text_input("Ask a financial question", key="user_question")
+    
+    with col2:
+        use_voice = st.checkbox("Enable voice", value=True)
+    
+    # Common financial questions
+    st.markdown("### Common Questions")
+    question_cols = st.columns(3)
+    
+    common_questions = [
+        "How much runway do we have at our current burn rate?",
+        "Should we increase our marketing spend given our growth rate?",
+        "When should we start preparing for our next fundraising round?",
+        "How can we optimize our burn rate without impacting growth?",
+        "What metrics should we focus on improving right now?",
+        "How do our unit economics compare to similar startups?"
+    ]
+    
+    selected_question = None
+    
+    for i, question in enumerate(common_questions):
+        with question_cols[i % 3]:
+            if st.button(question, key=f"q_{i}"):
+                selected_question = question
+    
+    # Process user input (either from text input or selected question)
+    if user_input or selected_question:
+        question = user_input or selected_question
+        
+        # Add user message to chat history
+        st.session_state.chat_history.append({"role": "user", "content": question})
+        
+        # Get AI response
+        response = get_advisory_guidance(question, startup_data)
+        
+        # Generate voice response if enabled
+        audio_data = None
+        if use_voice:
+            audio_data = generate_voice_response(response)
+        
+        # Add AI response to chat history
+        st.session_state.chat_history.append({
+            "role": "assistant", 
+            "content": response,
+            "audio": audio_data
+        })
+        
+        # Rerun to display updated chat
+        st.experimental_rerun()
+    
+    st.markdown("</div>", unsafe_allow_html=True)
+    
+    # Advanced options
+    st.subheader("Advanced Tools")
+    
+    tool_cols = st.columns(3)
+    
+    with tool_cols[0]:
+        st.markdown("""
+        <div style='background-color: white; padding: 15px; border-radius: 10px; height: 200px; box-shadow: 0 4px 6px rgba(0,0,0,0.1);'>
+            <h4>Financial Model Review</h4>
+            <p>Upload your financial model for AI analysis and recommendations.</p>
+            <div style='position: absolute; bottom: 15px;'>
+                <input type='file' disabled/>
+            </div>
+        </div>
+        """, unsafe_allow_html=True)
+    
+    with tool_cols[1]:
+        st.markdown("""
+        <div style='background-color: white; padding: 15px; border-radius: 10px; height: 200px; box-shadow: 0 4px 6px rgba(0,0,0,0.1);'>
+            <h4>Investor Pitch Review</h4>
+            <p>Get AI feedback on your investor pitch deck and financial projections.</p>
+            <div style='position: absolute; bottom: 15px;'>
+                <input type='file' disabled/>
+            </div>
+        </div>
+        """, unsafe_allow_html=True)
+    
+    with tool_cols[2]:
+        st.markdown("""
+        <div style='background-color: white; padding: 15px; border-radius: 10px; height: 200px; box-shadow: 0 4px 6px rgba(0,0,0,0.1);'>
+            <h4>Fundraising Strategy</h4>
+            <p>Generate a customized fundraising strategy based on your metrics.</p>
+            <div style='position: absolute; bottom: 15px;'>
+                <button disabled>Generate Strategy</button>
+            </div>
+        </div>
+        """, unsafe_allow_html=True)
+    
+    # Book a session CTA
+    st.markdown("""
+    <div style='background-color: #e6f7ff; padding: 20px; border-radius: 10px; margin-top: 30px;'>
+        <h3>Need more in-depth guidance?</h3>
+        <p>Book a dedicated session with our AI financial advisor for comprehensive analysis and personalized advice.</p>
+        <a href='#book-a-session'><button>Book a Session</button></a>
+    </div>
+    """, unsafe_allow_html=True)
+
+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 detailed, actionable advice addressing their question. Include:
+    1. Clear assessment of their current situation
+    2. 3-5 specific, actionable recommendations with expected outcomes
+    3. Relevant metrics they should track
+    4. Industry benchmarks for comparison
+    5. Timeline for implementation and results
+
+    Be specific with numbers, timeframes, and expected outcomes.
+    """
+    
+    return generate_ai_response(prompt)
+
+def generate_voice_response(text):
+    """Generate voice response using ElevenLabs API (simulated)."""
+    try:
+        # In a real implementation, this would call the ElevenLabs API
+        # For demonstration, we'll simulate an audio response
+        
+        # Simulated audio data (just an empty response)
+        audio_data = b''
+        
+        # In a real implementation, you would do something like:
+        # url = "https://api.elevenlabs.io/v1/text-to-speech/{voice_id}"
+        # headers = {
+        #     "Accept": "audio/mpeg",
+        #     "Content-Type": "application/json",
+        #     "xi-api-key": st.secrets["ELEVENLABS_API_KEY"]
+        # }
+        # data = {
+        #     "text": text,
+        #     "model_id": "eleven_monolingual_v1",
+        #     "voice_settings": {
+        #         "stability": 0.5,
+        #         "similarity_boost": 0.5
+        #     }
+        # }
+        # response = requests.post(url, json=data, headers=headers)
+        # audio_data = response.content
+        
+        return audio_data
+    except Exception as e:
+        st.warning(f"Error generating voice response: {e}")
+        return None

pages/fund-monitoring.py

@@ -0,0 +1,334 @@
+def render_fund_monitoring(transactions_df):
+    """
+    Render the AI-powered fund monitoring page.
+    
+    This feature helps startups monitor spending, detect fraudulent transactions,
+    and maintain investor trust through AI-powered analysis.
+    """
+    st.markdown("<h1 class='main-header'>Investor Fund Monitoring</h1>", unsafe_allow_html=True)
+    st.markdown("<p class='sub-header'>AI-powered fraud detection and spending analysis</p>", unsafe_allow_html=True)
+    
+    # How AI helps with fund monitoring
+    with st.expander("ℹ️ How AI enhances fund monitoring"):
+        st.markdown("""
+        ### How AI Powers Your Fund Monitoring
+        
+        The fund monitoring system uses AI to help maintain investor trust and optimize spending:
+        
+        - **Anomaly Detection**: Our AI models identify unusual transactions that don't match typical startup spending patterns
+        - **Risk Scoring**: Each transaction is assigned a risk score based on multiple factors like amount, category, vendor, and description
+        - **Pattern Recognition**: The system identifies potentially concerning spending trends across categories over time
+        - **Fraud Prevention**: AI algorithms flag transactions that match known patterns of misuse before they become issues
+        - **Investor-Ready Reporting**: Generate reports that demonstrate responsible financial stewardship to investors
+        
+        This helps founders maintain investor trust, prevent misuse of funds, and create transparency in financial operations.
+        """)
+    
+    st.write("Monitor your startup's spending to maintain investor trust and ensure proper fund usage. Our AI algorithms automatically flag suspicious transactions and identify spending patterns.")
+    
+    # AI insights for fund monitoring
+    insights_key = f"fund_monitoring_{date.today().isoformat()}"
+    if insights_key not in st.session_state.insights_cache:
+        insights = generate_ai_response("""
+        You are a financial fraud detection expert. Provide 2-3 critical spending patterns that investors typically look for when monitoring startup fund usage.
+        Format as brief bullet points focused on maintaining investor trust.
+        """, simulate=True)
+        st.session_state.insights_cache[insights_key] = insights
+    
+    with st.expander("🔍 AI Monitoring Insights", expanded=True):
+        st.markdown("<span class='ai-badge'>AI-Generated Insights</span>", unsafe_allow_html=True)
+        st.markdown(st.session_state.insights_cache[insights_key])
+    
+    # Process transactions to detect suspicious ones with AI enhancement
+    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, col4 = st.columns(4)
+    
+    with col1:
+        st.markdown(f"""
+        <div class='metric-card'>
+            <p class='metric-label'>Total Transactions</p>
+            <p class='metric-value'>{total_transactions}</p>
+        </div>
+        """, unsafe_allow_html=True)
+    
+    with col2:
+        flagged_percent = suspicious_count/total_transactions*100 if total_transactions > 0 else 0
+        status = "danger-metric" if flagged_percent > 10 else ("warning-metric" if flagged_percent > 5 else "good-metric")
+        st.markdown(f"""
+        <div class='metric-card'>
+            <p class='metric-label'>Flagged Transactions</p>
+            <p class='metric-value {status}'>{suspicious_count} ({flagged_percent:.1f}%)</p>
+        </div>
+        """, unsafe_allow_html=True)
+    
+    with col3:
+        amount_percent = suspicious_amount/total_amount*100 if total_amount > 0 else 0
+        status = "danger-metric" if amount_percent > 15 else ("warning-metric" if amount_percent > 7 else "good-metric")
+        st.markdown(f"""
+        <div class='metric-card'>
+            <p class='metric-label'>Flagged Amount</p>
+            <p class='metric-value {status}'>${suspicious_amount:,.0f} ({amount_percent:.1f}%)</p>
+        </div>
+        """, unsafe_allow_html=True)
+    
+    with col4:
+        avg_risk = suspicious_transactions['Risk_Score'].mean() if not suspicious_transactions.empty else 0
+        status = "danger-metric" if avg_risk > 50 else ("warning-metric" if avg_risk > 30 else "good-metric")
+        st.markdown(f"""
+        <div class='metric-card'>
+            <p class='metric-label'>Average Risk Score</p>
+            <p class='metric-value {status}'>{avg_risk:.1f}/100</p>
+        </div>
+        """, unsafe_allow_html=True)
+    
+    # Tabs for different views
+    tab1, tab2 = st.tabs(["Flagged Transactions", "All Transactions"])
+    
+    with tab1:
+        if suspicious_count > 0:
+            # Add risk score visualization (color coded)
+            suspicious_view = suspicious_transactions.copy()
+            
+            # Format for display
+            def colorize_risk(val):
+                color = "red" if val > 50 else ("orange" if val > 30 else "blue")
+                return f'background-color: {color}; color: white; font-weight: bold'
+            
+            # Apply styling
+            styled_suspicious = suspicious_view.style.applymap(
+                lambda x: colorize_risk(x) if x > 0 else '', 
+                subset=['Risk_Score']
+            )
+            
+            st.dataframe(
+                suspicious_view[['Date', 'Category', 'Vendor', 'Amount', 'Description', 'Risk_Score', 'Reason']], 
+                use_container_width=True
+            )
+            
+            # Get AI analysis of suspicious transactions
+            fraud_key = f"fraud_{date.today().isoformat()}"
+            if fraud_key not in st.session_state.insights_cache:
+                fraud_analysis = get_fraud_analysis(suspicious_transactions)
+                st.session_state.insights_cache[fraud_key] = fraud_analysis
+            
+            st.markdown("<div class='advisor-card'>", unsafe_allow_html=True)
+            st.markdown("<span class='ai-badge'>AI Fraud Analysis</span>", unsafe_allow_html=True)
+            st.markdown(f"<p class='advice-text'>{st.session_state.insights_cache[fraud_key]}</p>", unsafe_allow_html=True)
+            st.markdown("</div>", unsafe_allow_html=True)
+            
+            # Action buttons
+            st.subheader("Recommended Actions")
+            
+            col1, col2, col3 = st.columns(3)
+            with col1:
+                if st.button("🔍 Investigate All Flagged"):
+                    st.session_state.investigation_started = True
+            with col2:
+                if st.button("📝 Generate Investor Report"):
+                    st.session_state.report_generated = True  
+            with col3:
+                if st.button("✅ Mark Reviewed"):
+                    st.session_state.marked_reviewed = True
+            
+            # Simulate action responses
+            if 'investigation_started' in st.session_state and st.session_state.investigation_started:
+                st.success("Investigation initiated for all flagged transactions. Your financial team will be notified.")
+            
+            if 'report_generated' in st.session_state and st.session_state.report_generated:
+                st.success("Investor report generated and ready for review before sending.")
+                
+            if 'marked_reviewed' in st.session_state and st.session_state.marked_reviewed:
+                st.success("All transactions marked as reviewed. Status will be updated in the system.")
+        else:
+            st.success("No suspicious transactions detected by our AI system. Your spending appears to be normal for a startup at your stage.")
+    
+    with tab2:
+        st.dataframe(processed_df[['Date', 'Category', 'Vendor', 'Amount', 'Description', 'Suspicious', 'Risk_Score']], 
+                    use_container_width=True)
+    
+    # Spending patterns
+    st.subheader("Spending Pattern Analysis")
+    
+    # Category breakdown
+    category_spending = processed_df.groupby('Category')['Amount'].sum().reset_index()
+    
+    col1, col2 = st.columns(2)
+    
+    with col1:
+        fig = px.bar(category_spending, x='Category', y='Amount',
+                    title="Spending by Category",
+                    labels={'Amount': 'Total Spent ($)'},
+                    color='Amount',
+                    color_continuous_scale='Blues')
+        fig.update_layout(
+            height=400,
+            plot_bgcolor='rgba(240,247,255,0.8)',
+            xaxis_title="Category",
+            yaxis_title="Amount Spent ($)",
+            font=dict(family="Arial, sans-serif", size=12),
+            margin=dict(l=20, r=20, t=40, b=20),
+        )
+        st.plotly_chart(fig, use_container_width=True)
+    
+    with col2:
+        # AI spending pattern analysis
+        spending_key = f"spending_pattern_{date.today().isoformat()}"
+        if spending_key not in st.session_state.insights_cache:
+            spending_pattern_analysis = generate_ai_response("""
+            You are a startup spending analyst. Review the spending patterns and provide 3 key insights about:
+            1. Categories that appear to have unusually high spending
+            2. Potential areas where spending could be optimized
+            3. Changes in spending patterns that investors might find concerning
+            
+            Format as concise, actionable bullet points.
+            """, simulate=True)
+            st.session_state.insights_cache[spending_key] = spending_pattern_analysis
+        
+        st.markdown("<div class='insight-card'>", unsafe_allow_html=True)
+        st.markdown("<span class='ai-badge'>AI Spending Analysis</span>", unsafe_allow_html=True)
+        st.markdown(st.session_state.insights_cache[spending_key])
+        st.markdown("</div>", unsafe_allow_html=True)
+    
+    # Time series of spending
+    processed_df['Date'] = pd.to_datetime(processed_df['Date'])
+    processed_df['Week'] = processed_df['Date'].dt.isocalendar().week
+    weekly_spending = processed_df.groupby(['Week', 'Category'])['Amount'].sum().reset_index()
+    
+    fig = px.line(weekly_spending, x='Week', y='Amount', color='Category',
+                title="Weekly Spending Trends",
+                labels={'Amount': 'Amount Spent ($)'},
+                color_discrete_sequence=px.colors.qualitative.Bold)
+    fig.update_layout(
+        height=400,
+        plot_bgcolor='rgba(240,247,255,0.8)',
+        xaxis_title="Week",
+        yaxis_title="Amount Spent ($)",
+        font=dict(family="Arial, sans-serif", size=12),
+        margin=dict(l=20, r=20, t=40, b=20),
+    )
+    st.plotly_chart(fig, use_container_width=True)
+    
+    # AI-powered spending controls recommendation
+    st.subheader("AI-Recommended Spending Controls")
+    
+    # Get AI recommendations for spending controls
+    controls_key = f"spending_controls_{date.today().isoformat()}"
+    if controls_key not in st.session_state.insights_cache:
+        controls_recommendations = generate_ai_response("""
+        You are a financial controls expert for startups. Based on the spending patterns and suspicious transactions, 
+        recommend 3-4 specific spending controls that the startup should implement to prevent misuse of funds.
+        
+        For each control, provide:
+        1. A clear policy statement
+        2. Implementation steps
+        3. Expected impact
+        
+        Format as concise, actionable recommendations.
+        """, simulate=True)
+        st.session_state.insights_cache[controls_key] = controls_recommendations
+    
+    st.markdown("<div class='advisor-card'>", unsafe_allow_html=True)
+    st.markdown("<span class='ai-badge'>AI Control Recommendations</span>", unsafe_allow_html=True)
+    st.markdown(f"<p class='advice-text'>{st.session_state.insights_cache[controls_key]}</p>", unsafe_allow_html=True)
+    st.markdown("</div>", unsafe_allow_html=True)
+    
+    # Call-to-action
+    st.info("📅 Need help implementing financial controls? [Book a session](#book-a-session) with our AI financial advisor.")
+
+def detect_suspicious_transactions(transactions_df):
+    """AI-enhanced 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'] = ""
+    df['Risk_Score'] = 0
+    
+    # Check for suspicious patterns
+    for idx, row in df.iterrows():
+        reasons = []
+        risk_score = 0
+        
+        # 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']}")
+                risk_score += 30
+                
+                # Higher risk for significantly exceeding threshold
+                excess_percentage = (row['Amount'] - category_thresholds[row['Category']]) / category_thresholds[row['Category']] * 100
+                if excess_percentage > 100:  # More than double the threshold
+                    risk_score += 20
+        
+        # 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")
+            risk_score += 25
+        
+        if any(term in str(row['Description']).lower() for term in suspicious_terms):
+            reasons.append(f"Description contains suspicious term")
+            risk_score += 20
+        
+        # Check for rounded amounts (potential indicator of estimation/fabrication)
+        if row['Amount'] % 1000 == 0 and row['Amount'] > 3000:
+            reasons.append(f"Suspiciously round amount")
+            risk_score += 15
+        
+        # Mark as suspicious if risk score is high enough
+        if risk_score >= 30:
+            df.at[idx, 'Suspicious'] = True
+            df.at[idx, 'Reason'] = "; ".join(reasons)
+            df.at[idx, 'Risk_Score'] = risk_score
+    
+    # Sort by risk score
+    df = df.sort_values(by='Risk_Score', ascending=False)
+    
+    return df
+
+def get_fraud_analysis(transactions_df):
+    """Get AI analysis of potentially fraudulent transactions."""
+    suspicious_df = transactions_df[transactions_df['Suspicious']].copy()
+    
+    if len(suspicious_df) == 0:
+        return "No suspicious transactions detected."
+    
+    transactions_text = suspicious_df[['Date', 'Category', 'Vendor', 'Amount', 'Description', 'Risk_Score']].to_string(index=False)
+    
+    prompt = f"""
+    You are a financial forensics expert specializing in startup spending oversight.
+    Review these flagged transactions:
+
+    {transactions_text}
+
+    Provide a detailed analysis:
+    1. Identify the most concerning transactions and explain why
+    2. Calculate the total financial impact of these suspicious transactions
+    3. Identify spending patterns or potential policy violations
+    4. Recommend specific actions the startup should take immediately
+    5. Suggest controls to prevent similar issues in the future
+
+    Be specific about which transactions are most concerning and why investors would have questions.
+    """
+    
+    return generate_ai_response(prompt)

pages/init-file.txt

@@ -0,0 +1 @@
+# This file can be empty, it just marks the directory as a Python package