Delete app.py

app.py

@@ -1,1676 +0,0 @@
-    
-    # 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_key = f"decision_analysis_{new_hires}_{new_marketing}_{other_expenses}_{growth_impact}"
-            if analysis_key not in st.session_state.insights_cache:
-                analysis = generate_ai_response(f"""
-                You are a financial advisor for startups. A founder asks:
-                "{question}"
-
-                Here's their current financial situation:
-                - Current cash: ${startup_data['cash']}
-                - Monthly burn rate: ${startup_data['burn_rate']}
-                - Monthly revenue: ${startup_data['revenue']}
-                - Monthly growth rate: {startup_data['growth_rate'] * 100}%
-                
-                They're considering these changes:
-                - Adding {decision_params['new_hires']} new engineers (${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.
-                """)
-                st.session_state.insights_cache[analysis_key] = analysis
-            
-            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'>{st.session_state.insights_cache[analysis_key]}</p>", unsafe_allow_html=True)
-            st.markdown("</div>", unsafe_allow_html=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)
-
-# Render Fund Monitoring page
-def render_fund_monitoring():
-    """Render the AI-powered fund monitoring page"""
-    if not st.session_state.current_startup or st.session_state.current_startup not in st.session_state.startups:
-        st.warning("No startup selected. Please upload data or select a sample startup.")
-        render_upload_page()
-        return
-    
-    # Get the selected startup data
-    transactions_df = st.session_state.startups[st.session_state.current_startup]['transactions']
-    
-    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.
-        """)
-        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()
-            
-            # Display dataframe
-            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:
-                suspicious_text = "\n".join([
-                    f"- {row['Date']}: {row['Vendor']} (${row['Amount']:.2f}) - {row['Description']}"
-                    for _, row in suspicious_transactions.head(5).iterrows()
-                ])
-                
-                fraud_analysis = generate_ai_response(f"""
-                You are a financial fraud detection expert. Review these flagged suspicious transactions:
-                
-                {suspicious_text}
-                
-                Provide a detailed analysis:
-                1. Identify concerning patterns in these transactions
-                2. Recommend specific actions to address these issues
-                3. Suggest preventive measures to avoid similar issues in the future
-                
-                Format your response with clear sections and actionable recommendations.
-                """)
-                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.
-            """)
-            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)
-    
-    # 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.
-        """)
-        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? Schedule a session with our AI financial advisor.")
-
-# Render AI Financial Advisor page
-def render_ai_financial_advisor():
-    """Render the AI financial advisor page with voice chat capabilities"""
-    if not st.session_state.current_startup or st.session_state.current_startup not in st.session_state.startups:
-        st.warning("No startup selected. Please upload data or select a sample startup.")
-        render_upload_page()
-        return
-    
-    # Get the selected startup data
-    startup_data = st.session_state.startups[st.session_state.current_startup]['profile']
-    
-    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")
-    
-    # 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 = generate_ai_response(f"""
-        You are a strategic financial advisor for startups. A founder asks:
-        "{question}"
-
-        Here's their current financial situation:
-        - Stage: {startup_data['stage']}
-        - 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}%
-        - Last funding: {startup_data['last_funding']}
-        - Team size: {startup_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.
-        """)
-        
-        # 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.rerun()
-    
-    st.markdown("</div>", unsafe_allow_html=True)
-    
-    # Advanced tools
-    st.subheader("Advanced Financial 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;'>
-                <button disabled style="background-color: #E6F3FF; color: #0066cc; border-radius: 5px; padding: 5px 10px; border: none;">Coming Soon</button>
-            </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;'>
-                <button disabled style="background-color: #E6F3FF; color: #0066cc; border-radius: 5px; padding: 5px 10px; border: none;">Coming Soon</button>
-            </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 style="background-color: #E6F3FF; color: #0066cc; border-radius: 5px; padding: 5px 10px; border: none;">Coming Soon</button>
-            </div>
-        </div>
-        """, unsafe_allow_html=True)
-
-# Main function
-def main():
-    # Initialize Gemini API
-    initialize_gemini()
-    
-    # Create sidebar navigation
-    create_sidebar()
-    
-    # Render the correct page based on session state
-    if st.session_state.current_page == 'upload':
-        render_upload_page()
-    elif st.session_state.current_page == 'dashboard':
-        render_financial_dashboard()
-    elif st.session_state.current_page == 'simulator':
-        render_decision_simulator()
-    elif st.session_state.current_page == 'monitoring':
-        render_fund_monitoring()
-    elif st.session_state.current_page == 'advisor':
-        render_ai_financial_advisor()
-
-if __name__ == "__main__":
-    main()
-import streamlit as st
-import pandas as pd
-import numpy as np
-import plotly.express as px
-import plotly.graph_objects as go
-from datetime import datetime, timedelta, date
-import time
-import io
-import base64
-import requests
-import google.generativeai as genai
-
-# 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)
-
-# Initialize session state variables
-if 'startups' not in st.session_state:
-    st.session_state.startups = {}  # Dictionary to store multiple startup data
-if 'current_startup' not in st.session_state:
-    st.session_state.current_startup = None  # Currently selected startup
-if 'current_page' not in st.session_state:
-    st.session_state.current_page = 'upload'  # Default page
-if 'insights_cache' not in st.session_state:
-    st.session_state.insights_cache = {}
-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?"}
-    ]
-
-# Configure Google GenerativeAI (Gemini)
-def initialize_gemini():
-    """Initialize Google's GenerativeAI (Gemini) with API key"""
-    try:
-        # In production, get this from st.secrets or environment variables
-        api_key = st.secrets.get("GEMINI_API_KEY", None)
-        if api_key:
-            genai.configure(api_key=api_key)
-            return True
-        else:
-            st.warning("Gemini API key not found. Using simulated AI responses.")
-            return False
-    except Exception as e:
-        st.error(f"Failed to initialize Gemini AI: {e}")
-        return False
-
-def generate_ai_response(prompt, simulate=True):
-    """Generate text using Google's GenerativeAI (Gemini)"""
-    if simulate:
-        # Return a generic response for simulation
-        return """
-        Based on your financial situation, I recommend focusing on these key areas:
-
-        1. **Extend Your Runway**: With your current burn rate, consider reducing non-essential expenses by 15-20%. Focus particularly on optimizing marketing efficiency while maintaining growth activities.
-
-        2. **Accelerate Revenue Growth**: Your current monthly growth is good, but increasing it would significantly improve your cash position. Consider focusing sales efforts on higher-value customers with shorter sales cycles.
-
-        3. **Prepare for Fundraising**: Begin conversations with existing investors about potential bridge funding. Prepare updated metrics showing clear progress on unit economics and customer acquisition.
-
-        I recommend reviewing your expense categories weekly and tracking your burn rate closely.
-        """
-    else:
-        try:
-            # Initialize Gemini model
-            model = genai.GenerativeModel('gemini-pro')
-            response = model.generate_content(prompt)
-            return response.text
-        except Exception as e:
-            st.error(f"Error generating AI response: {e}")
-            return "Sorry, I couldn't generate a response at this time."
-
-def generate_voice_response(text, simulate=True):
-    """Generate voice response using ElevenLabs API"""
-    if simulate:
-        # Return empty audio data for simulation
-        return None
-    else:
-        try:
-            # Get API key from secrets
-            api_key = st.secrets.get("ELEVENLABS_API_KEY", None)
-            if not api_key:
-                st.warning("ElevenLabs API key not found. Voice response not available.")
-                return None
-                
-            # ElevenLabs API endpoint
-            url = "https://api.elevenlabs.io/v1/text-to-speech/21m00Tcm4TlvDq8ikWAM"  # Rachel voice ID
-            
-            # Headers and payload
-            headers = {
-                "Accept": "audio/mpeg",
-                "Content-Type": "application/json",
-                "xi-api-key": api_key
-            }
-            
-            data = {
-                "text": text,
-                "model_id": "eleven_monolingual_v1",
-                "voice_settings": {
-                    "stability": 0.5,
-                    "similarity_boost": 0.5
-                }
-            }
-            
-            # Make the API call
-            response = requests.post(url, json=data, headers=headers)
-            
-            if response.status_code == 200:
-                return response.content
-            else:
-                st.error(f"Error with ElevenLabs API: {response.status_code}")
-                return None
-                
-        except Exception as e:
-            st.error(f"Error generating voice response: {e}")
-            return None
-
-def switch_page(page_name):
-    """Function to switch between pages"""
-    st.session_state.current_page = page_name
-    st.rerun()
-
-# Calculate runway for business decisions
-def calculate_runway(cash, burn_rate, revenue, growth_rate, months=24):
-    """
-    Calculate runway based on cash, burn, revenue and growth
-    Returns runway in months and dataframe with projections
-    """
-    # Create date range
-    current_date = datetime.now()
-    date_range = [current_date + timedelta(days=30*i) for i in range(months)]
-    
-    # Initialize data structures
-    cash_flow = []
-    remaining_cash = cash
-    monthly_revenue = revenue
-    
-    # Calculate cash flow for each month
-    for i in range(months):
-        # Calculate cash flow for this month
-        net_burn = burn_rate - monthly_revenue
-        cash_flow.append(net_burn)
-        
-        # Update remaining cash
-        remaining_cash -= net_burn
-        
-        # Update revenue with growth
-        monthly_revenue *= (1 + growth_rate)
-    
-    # Create dataframe
-    df = pd.DataFrame({
-        'Net_Burn': cash_flow,
-        'Cumulative_Cash': [cash - sum(cash_flow[:i+1]) for i in range(len(cash_flow))]
-    }, index=date_range)
-    
-    # Calculate runway (when cumulative cash goes negative)
-    negative_cash = df[df['Cumulative_Cash'] < 0]
-    if len(negative_cash) > 0:
-        runway_months = (negative_cash.index[0] - current_date).days // 30
-    else:
-        runway_months = months
-    
-    return runway_months, df
-
-# Simulate decisions
-def simulate_decision(cash, burn_rate, revenue, growth_rate, 
-                     additional_expenses, new_hires, marketing_increase, growth_impact):
-    """
-    Simulate the financial impact of a business decision
-    """
-    # Current projection
-    current_runway, current_df = calculate_runway(
-        cash, burn_rate, revenue, growth_rate
-    )
-    
-    # New projection with decision impact
-    new_burn_rate = burn_rate + additional_expenses + (new_hires * ENGINEER_SALARY) + marketing_increase
-    new_growth_rate = growth_rate + growth_impact
-    
-    new_runway, new_df = calculate_runway(
-        cash, new_burn_rate, revenue, new_growth_rate
-    )
-    
-    return current_runway, new_runway, current_df, new_df
-
-# Detect suspicious transactions
-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
-
-# Parse CSV file to dataframe
-def parse_csv_to_df(file):
-    """Parse uploaded CSV file to Pandas DataFrame"""
-    try:
-        df = pd.read_csv(file)
-        return df, None
-    except Exception as e:
-        return None, f"Error parsing CSV: {e}"
-
-# Page config
-st.set_page_config(
-    page_title="StartupFinancePilot",
-    page_icon="💰",
-    layout="wide",
-    initial_sidebar_state="expanded"
-)
-
-# Custom CSS
-st.markdown("""
-<style>
-    #MainMenu {visibility: hidden;}
-    footer {visibility: hidden;}
-    .stDeployButton {display:none;}
-    
-    .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;
-    }
-    
-    /* Style for sidebar buttons */
-    div.stButton > button {
-        width: 100%;
-        padding: 10px 10px;
-        border: none;
-        background-color: #E6F3FF;
-        color: #0066cc;
-        border-radius: 10px;
-        text-align: left;
-        margin: 5px 0;
-        font-weight: bold;
-    }
-    
-    div.stButton > button:hover {
-        background-color: #CCE5FF;
-        color: #004080;
-    }
-    
-    /* Style for title box */
-    .title-box {
-        background: linear-gradient(45deg, #0066cc, #66b3ff);
-        padding: 20px;
-        border-radius: 10px;
-        margin-bottom: 20px;
-        text-align: center;
-        color: white;
-        cursor: pointer;
-    }
-    
-    .ai-badge {
-        display: inline-block;
-        background-color: #0066cc;
-        color: white;
-        border-radius: 4px;
-        padding: 2px 6px;
-        font-size: 0.7rem;
-        font-weight: bold;
-        margin-bottom: 8px;
-    }
-    
-    .insight-card, .advisor-card {
-        background-color: #f8f9fa;
-        border-radius: 10px;
-        padding: 15px;
-        margin-bottom: 20px;
-        box-shadow: 0 2px 4px rgba(0,0,0,0.05);
-    }
-    
-    .advice-text {
-        margin-top: 10px;
-        line-height: 1.6;
-    }
-</style>
-""", unsafe_allow_html=True)
-
-# Create sidebar navigation
-def create_sidebar():
-    with st.sidebar:
-        # Title box that works as home button
-        st.markdown("""
-            <div class="title-box">
-                <h1>💰 StartupFinancePilot</h1>
-                <p>AI-powered financial assistant for startups</p>
-            </div>
-        """, unsafe_allow_html=True)
-
-        # Startup selector (if there are startups in the session state)
-        if st.session_state.startups:
-            st.subheader("Selected Startup")
-            startup_names = list(st.session_state.startups.keys())
-            selected_startup = st.selectbox(
-                "Choose Startup", 
-                startup_names,
-                index=startup_names.index(st.session_state.current_startup) if st.session_state.current_startup in startup_names else 0
-            )
-            st.session_state.current_startup = selected_startup
-            
-            # Show basic startup info
-            if selected_startup in st.session_state.startups:
-                startup_data = st.session_state.startups[selected_startup]['profile']
-                st.markdown(f"""
-                **Stage:** {startup_data['stage']}  
-                **Cash:** ${startup_data['cash']:,}  
-                **Monthly Burn:** ${startup_data['burn_rate']:,}  
-                **Monthly Revenue:** ${startup_data['revenue']:,}  
-                """)
-
-        st.markdown("<hr>", unsafe_allow_html=True)  # Divider
-        
-        # Upload data button at the top
-        if st.button("📤 Upload Startup Data", use_container_width=True):
-            switch_page('upload')
-
-        # Navigation buttons
-        if st.button("📊 Financial Dashboard", use_container_width=True):
-            switch_page('dashboard')
-            
-        if st.button("🔮 Decision Simulator", use_container_width=True):
-            switch_page('simulator')
-            
-        if st.button("🕵️ Fund Monitoring", use_container_width=True):
-            switch_page('monitoring')
-            
-        if st.button("🤖 AI Financial Advisor", use_container_width=True):
-            switch_page('advisor')
-
-# Upload and process financial data files
-def render_upload_page():
-    """Render the upload page for startup data"""
-    st.markdown("<h1 class='main-header'>Upload Your Startup Data</h1>", unsafe_allow_html=True)
-    st.markdown("<p class='sub-header'>Upload CSV files or use sample data to get started</p>", unsafe_allow_html=True)
-    
-    with st.expander("Upload Instructions", expanded=False):
-        st.markdown("""
-        ### How to Upload Your Startup Data
-        
-        You can upload three types of files:
-        
-        1. **Company Profile** - A CSV with basic information about your startup including:
-           - name, stage, founded, employees, last_funding, cash, burn_rate, revenue, growth_rate
-        
-        2. **Cash Flow Data** - A CSV with monthly cash flow data with columns:
-           - Month, Revenue, Payroll, Marketing, Office, Software, Travel, Legal, Misc
-        
-        3. **Transaction Data** - A CSV with transaction details:
-           - Date, Category, Vendor, Amount, Description, Flag
-           
-        If you don't have these files ready, you can use our sample data.
-        """)
-    
-    col1, col2 = st.columns(2)
-    
-    with col1:
-        startup_name = st.text_input("Startup Name", value="My Startup")
-        
-        profile_file = st.file_uploader("Upload Company Profile (CSV)", type=['csv'])
-        cash_flow_file = st.file_uploader("Upload Cash Flow Data (CSV)", type=['csv'])
-        transactions_file = st.file_uploader("Upload Transactions Data (CSV)", type=['csv'])
-    
-    with col2:
-        st.markdown("""
-        <div style="background-color: #f0f7ff; padding: 15px; border-radius: 10px; height: 90%;">
-            <h4>Why Upload Your Data?</h4>
-            <p>By uploading your actual financial data, you'll get:</p>
-            <ul>
-                <li>Personalized AI insights tailored to your startup</li>
-                <li>Accurate runway projections based on your real spending patterns</li>
-                <li>Custom recommendations to optimize your burn rate</li>
-                <li>More realistic decision simulations</li>
-            </ul>
-            <p>All data is processed securely and never stored permanently.</p>
-        </div>
-        """, unsafe_allow_html=True)
-    
-    # Process the files if uploaded
-    if st.button("Process Data"):
-        # Initialize with default values
-        startup_data = {
-            "name": startup_name,
-            "stage": "Seed",
-            "founded": "12 months ago",
-            "employees": 5,
-            "last_funding": "Not specified",
-            "cash": 100000,
-            "burn_rate": 20000,
-            "revenue": 5000,
-            "growth_rate": 0.05
-        }
-        
-        cash_flow_df = None
-        transactions_df = None
-        
-        # Parse company profile
-        if profile_file:
-            try:
-                profile_df, error = parse_csv_to_df(profile_file)
-                if error:
-                    st.error(error)
-                else:
-                    # Get the first row as a dictionary
-                    if len(profile_df) > 0:
-                        startup_data.update(profile_df.iloc[0].to_dict())
-                        st.success(f"Successfully loaded company profile for {startup_data['name']}")
-            except Exception as e:
-                st.error(f"Error processing company profile: {e}")
-        
-        # Parse cash flow data
-        if cash_flow_file:
-            cash_flow_df, error = parse_csv_to_df(cash_flow_file)
-            if error:
-                st.error(error)
-            else:
-                # Add calculated fields if not present
-                if "Total_Expenses" not in cash_flow_df.columns:
-                    expense_columns = [col for col in cash_flow_df.columns if col not in ["Month", "Revenue", "Total_Expenses", "Net_Burn"]]
-                    cash_flow_df["Total_Expenses"] = cash_flow_df[expense_columns].sum(axis=1)
-                
-                if "Net_Burn" not in cash_flow_df.columns:
-                    cash_flow_df["Net_Burn"] = cash_flow_df["Total_Expenses"] - cash_flow_df["Revenue"]
-                
-                st.success("Successfully loaded cash flow data")
-        
-        # Parse transactions data
-        if transactions_file:
-            transactions_df, error = parse_csv_to_df(transactions_file)
-            if error:
-                st.error(error)
-            else:
-                # Ensure transactions data has required columns
-                required_columns = ["Date", "Category", "Vendor", "Amount", "Description"]
-                if all(col in transactions_df.columns for col in required_columns):
-                    if "Flag" not in transactions_df.columns:
-                        transactions_df["Flag"] = "Normal"  # Default flag
-                    
-                    st.success("Successfully loaded transactions data")
-                else:
-                    st.error("Transactions file is missing required columns")
-        
-        # If any files were processed, save the data to session state
-        if profile_file or cash_flow_file or transactions_file:
-            # Create a sample cash flow dataframe if none was uploaded
-            if cash_flow_df is None:
-                cash_flow_df = create_sample_cash_flow(startup_data)
-            
-            # Create a sample transactions dataframe if none was uploaded
-            if transactions_df is None:
-                transactions_df = create_sample_transactions(startup_data)
-            
-            # Store in session state
-            st.session_state.startups[startup_data['name']] = {
-                'profile': startup_data,
-                'cash_flow': cash_flow_df,
-                'transactions': transactions_df
-            }
-            
-            # Set as current startup
-            st.session_state.current_startup = startup_data['name']
-            
-            st.success(f"Successfully added {startup_data['name']} to your startups")
-            st.info("You can now analyze this startup's data in the dashboard")
-            
-            # Redirect to dashboard
-            switch_page('dashboard')
-    
-    # Sample data options
-    st.subheader("Or Use Sample Data")
-    
-    sample_col1, sample_col2 = st.columns(2)
-    
-    with sample_col1:
-        if st.button("Use TechHealth AI Sample"):
-            # Load sample data (function would generate or load from file)
-            load_sample_data("TechHealth AI")            
-            st.success("Successfully loaded TechHealth AI sample data")
-            # Redirect to dashboard
-            switch_page('dashboard')
-    
-    with sample_col2:
-        if st.button("Use GreenTech Innovations Sample"):
-            # Load another sample (function would generate or load from file)
-            load_sample_data("GreenTech Innovations")            
-            st.success("Successfully loaded GreenTech Innovations sample data")
-            # Redirect to dashboard
-            switch_page('dashboard')
-
-def create_sample_cash_flow(startup_data):
-    """Create a sample cash flow dataframe for a startup"""
-    cash_flow_data = {
-        "Month": [f"Month {i}" for i in range(1, 7)],
-        "Revenue": [startup_data['revenue'] * (1 + startup_data['growth_rate'])**i for i in range(6)],
-        "Payroll": [startup_data['burn_rate'] * 0.7] * 6,
-        "Marketing": [startup_data['burn_rate'] * 0.15] * 6,
-        "Office": [startup_data['burn_rate'] * 0.05] * 6,
-        "Software": [startup_data['burn_rate'] * 0.03] * 6,
-        "Travel": [startup_data['burn_rate'] * 0.02] * 6,
-        "Legal": [startup_data['burn_rate'] * 0.01] * 6,
-        "Misc": [startup_data['burn_rate'] * 0.04] * 6
-    }
-    cash_flow_df = pd.DataFrame(cash_flow_data)
-    cash_flow_df["Total_Expenses"] = cash_flow_df[["Payroll", "Marketing", "Office", "Software", "Travel", "Legal", "Misc"]].sum(axis=1)
-    cash_flow_df["Net_Burn"] = cash_flow_df["Total_Expenses"] - cash_flow_df["Revenue"]
-    return cash_flow_df
-
-def create_sample_transactions(startup_data):
-    """Create sample transaction data for a startup"""
-    transactions_data = {
-        "Date": [(datetime.now() - timedelta(days=i*5)).strftime("%Y-%m-%d") for i in range(10)],
-        "Category": ["Payroll", "Marketing", "Office", "Software", "Travel", "Legal", "Misc", "Payroll", "Marketing", "Office"],
-        "Vendor": ["Payroll Provider", "Facebook Ads", "Office Rent", "AWS", "Travel Agency", "Legal Firm", "Miscellaneous", "Payroll Provider", "Google Ads", "Office Supplies"],
-        "Amount": [startup_data['burn_rate'] * 0.7, startup_data['burn_rate'] * 0.15, startup_data['burn_rate'] * 0.05, startup_data['burn_rate'] * 0.03, startup_data['burn_rate'] * 0.02, startup_data['burn_rate'] * 0.01, startup_data['burn_rate'] * 0.04, startup_data['burn_rate'] * 0.7, startup_data['burn_rate'] * 0.15, startup_data['burn_rate'] * 0.05],
-        "Description": ["Monthly Payroll", "Ad Campaign", "Monthly Rent", "Cloud Services", "Business Travel", "Legal Services", "Miscellaneous Expenses", "Monthly Payroll", "Ad Campaign", "Office Supplies"],
-        "Flag": ["Normal", "Normal", "Normal", "Normal", "Normal", "Normal", "Normal", "Normal", "Normal", "Normal"]
-    }
-    return pd.DataFrame(transactions_data)
-
-def load_sample_data(sample_name):
-    """Load sample data for demonstration"""
-    if sample_name == "TechHealth AI":
-        # Create TechHealth AI sample
-        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
-        }
-    else:
-        # Create GreenTech Innovations sample
-        startup_data = {
-            "name": "GreenTech Innovations",
-            "stage": "Series A",
-            "founded": "3 years ago",
-            "employees": 25,
-            "last_funding": "$4.5M Series A 8 months ago",
-            "cash": 2800000,
-            "burn_rate": 220000,
-            "revenue": 75000,
-            "growth_rate": 0.12
-        }
-    
-    # Generate cash flow and transaction data
-    cash_flow_df = create_sample_cash_flow(startup_data)
-    transactions_df = create_sample_transactions(startup_data)
-    
-    # Add some suspicious transactions for the sample
-    if sample_name == "TechHealth AI":
-        suspicious_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"}
-        ])
-        transactions_df = pd.concat([suspicious_transactions, transactions_df], ignore_index=True)
-    
-    # Store in session state
-    st.session_state.startups[startup_data['name']] = {
-        'profile': startup_data,
-        'cash_flow': cash_flow_df,
-        'transactions': transactions_df
-    }
-    
-    # Set as current startup
-    st.session_state.current_startup = startup_data['name']
-
-# Render Financial Dashboard
-def render_financial_dashboard():
-    """Render the AI-powered financial dashboard page"""
-    if not st.session_state.current_startup or st.session_state.current_startup not in st.session_state.startups:
-        st.warning("No startup selected. Please upload data or select a sample startup.")
-        render_upload_page()
-        return
-    
-    # Get the selected startup data
-    startup_data = st.session_state.startups[st.session_state.current_startup]['profile']
-    cash_flow_df = st.session_state.startups[st.session_state.current_startup]['cash_flow']
-    
-    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: ${startup_data['revenue']}
-        - Monthly growth rate: {startup_data['growth_rate'] * 100}%
-        
-        Provide the top 3 most important financial insights that the founder should know today.
-        Format each insight as a brief, action-oriented bullet point.
-        """)
-        st.session_state.insights_cache[insights_key] = insights
-    
-    with st.expander("📊 AI Financial Insights", expanded=True):
-        st.markdown("<span class='ai-badge'>AI-Generated Insights</span>", unsafe_allow_html=True)
-        st.markdown(st.session_state.insights_cache[insights_key])
-    
-    # Key metrics
-    col1, col2, col3, col4 = st.columns(4)
-    
-    # Calculate runway
-    runway_months, runway_df = calculate_runway(
-        startup_data['cash'], 
-        startup_data['burn_rate'], 
-        startup_data['revenue'], 
-        startup_data['growth_rate']
-    )
-    
-    # Determine status colors based on financial health indicators
-    runway_status = "danger-metric" if runway_months < 6 else ("warning-metric" if runway_months < 9 else "good-metric")
-    burn_status = "danger-metric" if startup_data['burn_rate'] > 100000 else ("warning-metric" if startup_data['burn_rate'] > 80000 else "good-metric")
-    revenue_status = "good-metric" if startup_data['revenue'] > 20000 else ("warning-metric" if startup_data['revenue'] > 10000 else "danger-metric")
-    
-    with col1:
-        st.markdown(f"""
-        <div class='metric-card'>
-            <p class='metric-label'>Current Cash</p>
-            <p class='metric-value'>${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 AI
-        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 = generate_ai_response(f"""
-                You are a financial advisor for startups. Analyze this startup's financial data:
-                - Current cash: ${startup_data['cash']}
-                - Monthly burn rate: ${startup_data['burn_rate']}
-                - Monthly revenue: ${startup_data['revenue']}
-                - Monthly growth rate: {startup_data['growth_rate'] * 100}%
-
-                Provide a detailed analysis of their runway and financial health. Include:
-                1. Exact runway calculation in months
-                2. Assessment of financial health (critical, concerning, stable, or healthy)
-                3. Benchmarks compared to similar seed-stage startups
-                4. Three specific, actionable recommendations to improve runway
-                5. Key metrics they should focus on
-
-                Format your response in a structured, easy-to-read format with clear sections and bullet points.
-                """)
-                st.session_state.insights_cache[analysis_key] = analysis
-                
-            st.markdown("<span class='ai-badge'>AI Financial Analysis</span>", unsafe_allow_html=True)
-            st.markdown(st.session_state.insights_cache[analysis_key])
-    
-    with tab2:
-        # Revenue vs Expenses chart
-        rev_exp_df = cash_flow_df.copy()
-        fig = px.bar(rev_exp_df, x='Month', y=['Revenue', 'Total_Expenses'],
-                    title="Revenue vs. Expenses",
-                    barmode='group',
-                    labels={'value': 'Amount ($)', '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.
-                """)
-                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:
-        # Calculate metrics for assessment
-        runway_calc = startup_data['cash'] / (startup_data['burn_rate'] - startup_data['revenue'])
-        
-        # Calculate some example metrics
-        try:
-            mrr_growth = (cash_flow_df['Revenue'].iloc[-1] / cash_flow_df['Revenue'].iloc[-2] - 1) * 100
-            gross_margin = (cash_flow_df['Revenue'].iloc[-1] - cash_flow_df['Total_Expenses'].iloc[-1] / 2) / cash_flow_df['Revenue'].iloc[-1] * 100
-        except:
-            mrr_growth = 5.0
-            gross_margin = 60.0
-            
-        metrics = {
-            "MRR Growth": f"{mrr_growth:.1f}%",
-            "Gross Margin": f"{gross_margin:.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()])
-        
-        fundraising_analysis = generate_ai_response(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: {runway_calc:.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.
-        """)
-        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? Schedule a session with our AI financial advisor to get detailed recommendations.")
-
-# Render Decision Simulator page
-def render_decision_simulator():
-    """Render the AI-powered decision simulator page"""
-    if not st.session_state.current_startup or st.session_state.current_startup not in st.session_state.startups:
-        st.warning("No startup selected. Please upload data or select a sample startup.")
-        render_upload_page()
-        return
-    
-    # Get the selected startup data
-    startup_data = st.session_state.startups[st.session_state.current_startup]['profile']
-    
-    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"