import streamlit as st import plotly.express as px import plotly.graph_objs as go import pandas as pd from datetime import date # Import local utilities from ..utils.ai_helpers import generate_ai_response from ..utils.data_processing import calculate_runway 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. Args: startup_data (dict): Dictionary containing startup financial profile cash_flow_df (pd.DataFrame): DataFrame with monthly cash flow details """ st.markdown("

Financial Dashboard

", unsafe_allow_html=True) st.markdown("

AI-powered financial insights at a glance

", unsafe_allow_html=True) # Initialize insights cache if not exists if 'insights_cache' not in st.session_state: st.session_state.insights_cache = {} # AI Insights Explanation with st.expander("â„šī¸ 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**: Our AI model analyzes your data and highlights critical trends - **Predictive Forecasting**: AI forecasts your runway using advanced analytics - **Anomaly Detection**: Identifies unusual spending patterns or concerning financial trends - **Strategic Recommendations**: Provides tailored recommendations to optimize your runway - **Benchmark Comparison**: Compares your metrics against industry standards This helps founders make data-driven decisions quickly and confidently. """) # Generate AI Insights insights_key = f"dashboard_{date.today().isoformat()}" if insights_key not in st.session_state.insights_cache: try: 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: ${startup_data['revenue']:,} - Monthly growth rate: {startup_data['growth_rate'] * 100:.2f}% Provide the top 3 most important financial insights that the founder should know today. Format each insight as a brief, action-oriented bullet point. """) st.session_state.insights_cache[insights_key] = insights except Exception as e: st.session_state.insights_cache[insights_key] = f"Error generating insights: {str(e)}" # Display AI Insights with st.expander("📊 AI Financial Insights", expanded=True): st.markdown("AI-Generated Insights", unsafe_allow_html=True) st.markdown(st.session_state.insights_cache[insights_key]) # Key Metrics Section col1, col2, col3, col4 = st.columns(4) # Calculate Runway try: runway_months, runway_df = calculate_runway( startup_data['cash'], startup_data['burn_rate'], startup_data['revenue'], startup_data['growth_rate'] ) except Exception as e: st.error(f"Error calculating runway: {e}") runway_months = 0 runway_df = pd.DataFrame() # Placeholder # Determine Metric Status Colors runway_status = ( "danger-metric" if runway_months < 6 else "warning-metric" if runway_months < 9 else "good-metric" ) burn_status = ( "danger-metric" if startup_data['burn_rate'] > 100000 else "warning-metric" if startup_data['burn_rate'] > 80000 else "good-metric" ) revenue_status = ( "good-metric" if startup_data['revenue'] > 20000 else "warning-metric" if startup_data['revenue'] > 10000 else "danger-metric" ) # Display Key Metrics metrics_display = [ ("Current Cash", f"${startup_data['cash']:,}", None), ("Monthly Burn", f"${startup_data['burn_rate']:,}", burn_status), ("Monthly Revenue", f"${startup_data['revenue']:,}", revenue_status), ("Runway", f"{runway_months} months", runway_status) ] for i, (label, value, status) in enumerate(metrics_display): with [col1, col2, col3, col4][i]: status_class = f"metric-value {status}" if status else "metric-value" st.markdown(f"""

{label}

{value}

""", unsafe_allow_html=True) # Financial Overview Tabs st.subheader("Financial Overview") tab1, tab2, tab3 = st.tabs([ "Runway Projection", "Revenue vs. Expenses", "Burn Rate Trend" ]) with tab1: # Runway Projection Chart if not runway_df.empty: fig = px.line( runway_df.reset_index(), x='index', y='Cumulative_Cash', title="Cash Runway Projection", labels={'index': 'Month', 'Cumulative_Cash': 'Remaining Cash ($)'}, color_discrete_sequence=['#0066cc'] ) fig.add_hline(y=0, line_dash="dash", line_color="red", annotation_text="Out of Cash") fig.update_layout( height=400, plot_bgcolor='rgba(240,247,255,0.8)', xaxis_title="Month", yaxis_title="Cash Balance ($)", 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) # Runway Analysis with st.expander("🔍 AI Runway Analysis", expanded=True): runway_key = f"runway_{date.today().isoformat()}" if runway_key not in st.session_state.insights_cache: try: runway_analysis = get_runway_analysis(startup_data) st.session_state.insights_cache[runway_key] = runway_analysis except Exception as e: st.session_state.insights_cache[runway_key] = f"Error generating runway analysis: {str(e)}" st.markdown("AI Financial Analysis", unsafe_allow_html=True) st.markdown(st.session_state.insights_cache[runway_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) # Revenue Growth Calculations try: 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" ) except Exception as e: st.error(f"Error calculating growth metrics: {e}") with tab3: # Burn Rate Trend Chart 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), ) # Efficiency Ratio Calculation try: 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." ) except Exception as e: st.error(f"Error calculating efficiency ratio: {e}") # 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: try: fundraising_analysis = get_fundraising_readiness_analysis(startup_data, cash_flow_df) st.session_state.insights_cache[fundraising_key] = fundraising_analysis except Exception as e: st.session_state.insights_cache[fundraising_key] = f"Error generating fundraising analysis: {str(e)}" st.markdown("
", unsafe_allow_html=True) st.markdown("AI Fundraising Assessment", unsafe_allow_html=True) st.markdown( f"

{st.session_state.insights_cache[fundraising_key]}

", unsafe_allow_html=True ) st.markdown("
", 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): """ Generate runway analysis using AI Args: financial_data (dict): Startup financial data Returns: str: AI-generated runway analysis """ 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:.2f}% 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): """ Generate fundraising readiness analysis using AI Args: startup_data (dict): Startup financial profile cash_flow_df (pd.DataFrame): Monthly cash flow data Returns: str: AI-generated fundraising readiness analysis """ # Calculate key metrics with error handling try: mrr_growth = ( cash_flow_df['Revenue'].iloc[-1] / cash_flow_df['Revenue'].iloc[-2] - 1 ) * 100 except Exception: mrr_growth = 0 try: gross_margin = ( cash_flow_df['Revenue'].iloc[-1] - cash_flow_df['Total_Expenses'].iloc[-1] / 2 ) / cash_flow_df['Revenue'].iloc[-1] * 100 except Exception: gross_margin = 0 # Predefined metrics with example values metrics = { "MRR Growth": f"{mrr_growth:.1f}%", "Gross Margin": f"{gross_margin:.1f}%", "CAC": "$950", # Customer Acquisition Cost "LTV": "$4,500", # Lifetime Value "Churn": "3.2%", } # Convert metrics to formatted text metrics_text = "\n".join([f"- {k}: {v}" for k, v in metrics.items()]) # Calculate runway try: runway = startup_data['cash'] / (startup_data['burn_rate'] - startup_data['revenue']) except Exception: runway = 0 # Prepare prompt for AI analysis 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.get('last_funding', 'N/A')} - Current Cash: ${startup_data['cash']:,} - Monthly Burn: ${startup_data['burn_rate']:,} - Runway: {runway:.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)