Delete dashboard-page.py

dashboard-page.py

@@ -1,342 +0,0 @@
-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)