# 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("

Decision Impact Analysis

", 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("
", unsafe_allow_html=True) st.markdown("AI Decision Analysis", unsafe_allow_html=True) st.markdown(f"

{st.session_state.insights_cache[analysis_key]}

", unsafe_allow_html=True) st.markdown("
", 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"""

Risk Assessment

{risk_level} Risk Decision

This decision would give you {new_runway} months of runway.

""", 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("

Investor Fund Monitoring

", unsafe_allow_html=True) st.markdown("

AI-powered fraud detection and spending analysis

", unsafe_allow_html=True) # How AI helps with fund monitoring with st.expander("ℹī¸ 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("AI-Generated Insights", 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"""

Total Transactions

{total_transactions}

""", 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"""

Flagged Transactions

{suspicious_count} ({flagged_percent:.1f}%)

""", 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"""

Flagged Amount

${suspicious_amount:,.0f} ({amount_percent:.1f}%)

""", 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"""

Average Risk Score

{avg_risk:.1f}/100

""", 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("
", unsafe_allow_html=True) st.markdown("AI Fraud Analysis", unsafe_allow_html=True) st.markdown(f"

{st.session_state.insights_cache[fraud_key]}

", unsafe_allow_html=True) st.markdown("
", 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("
", unsafe_allow_html=True) st.markdown("AI Spending Analysis", unsafe_allow_html=True) st.markdown(st.session_state.insights_cache[spending_key]) st.markdown("
", 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("
", unsafe_allow_html=True) st.markdown("AI Control Recommendations", unsafe_allow_html=True) st.markdown(f"

{st.session_state.insights_cache[controls_key]}

", unsafe_allow_html=True) st.markdown("
", 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("

AI Financial Advisor

", unsafe_allow_html=True) st.markdown("

Get expert financial guidance through our AI-powered advisor

", unsafe_allow_html=True) # How AI helps with financial advisory with st.expander("ℹī¸ 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("
", 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"
You: {message['content']}
", unsafe_allow_html=True) else: st.markdown(f"
Financial Advisor: {message['content']}
", 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("
", unsafe_allow_html=True) # Advanced tools st.subheader("Advanced Financial Tools") tool_cols = st.columns(3) with tool_cols[0]: st.markdown("""

Financial Model Review

Upload your financial model for AI analysis and recommendations.

""", unsafe_allow_html=True) with tool_cols[1]: st.markdown("""

Investor Pitch Review

Get AI feedback on your investor pitch deck and financial projections.

""", unsafe_allow_html=True) with tool_cols[2]: st.markdown("""

Fundraising Strategy

Generate a customized fundraising strategy based on your metrics.

""", 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(""" """, unsafe_allow_html=True) # Create sidebar navigation def create_sidebar(): with st.sidebar: # Title box that works as home button st.markdown("""

💰 StartupFinancePilot

AI-powered financial assistant for startups

""", 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("
", 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("đŸ•ĩī¸ 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("

Upload Your Startup Data

", unsafe_allow_html=True) st.markdown("

Upload CSV files or use sample data to get started

", 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("""

Why Upload Your Data?

By uploading your actual financial data, you'll get:

All data is processed securely and never stored permanently.

""", 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("

Financial Dashboard

", unsafe_allow_html=True) st.markdown("

AI-powered financial insights at a glance

", unsafe_allow_html=True) # How AI helps with financial dashboards with st.expander("ℹī¸ 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("AI-Generated Insights", 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"""

Current Cash

${startup_data['cash']:,}

""", unsafe_allow_html=True) with col2: st.markdown(f"""

Monthly Burn

${startup_data['burn_rate']:,}

""", unsafe_allow_html=True) with col3: st.markdown(f"""

Monthly Revenue

${startup_data['revenue']:,}

""", unsafe_allow_html=True) with col4: st.markdown(f"""

Runway

{runway_months} months

""", 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: ${startup_data['revenue']} - Monthly growth rate: {startup_data['growth_rate'] * 100}% Provide a detailed analysis of their runway and financial health. Include: 1. Exact runway calculation in months 2. Assessment of financial health (critical, concerning, stable, or healthy) 3. Benchmarks compared to similar seed-stage startups 4. Three specific, actionable recommendations to improve runway 5. Key metrics they should focus on Format your response in a structured, easy-to-read format with clear sections and bullet points. """) st.session_state.insights_cache[analysis_key] = analysis st.markdown("AI Financial Analysis", unsafe_allow_html=True) st.markdown(st.session_state.insights_cache[analysis_key]) with tab2: # Revenue vs Expenses chart rev_exp_df = cash_flow_df.copy() fig = px.bar(rev_exp_df, x='Month', y=['Revenue', 'Total_Expenses'], title="Revenue vs. Expenses", barmode='group', labels={'value': 'Amount ($)', '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("

Expense Analysis

", 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("AI Recommendation", 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("
", 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? 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("

Decision Simulator

", unsafe_allow_html=True) st.markdown("

AI-powered analysis of business decisions

", unsafe_allow_html=True) # How AI helps with decision-making with st.expander("ℹī¸ 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"