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README.md

@@ -1,12 +1,56 @@
----
-title: Fin Ai
-emoji: 🔥
-colorFrom: indigo
-colorTo: red
-sdk: streamlit
-sdk_version: 1.42.2
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# StartupFinancePilot
+
+StartupFinancePilot is an AI-powered financial assistant for startups that helps founders make better financial decisions and monitor investor fund usage. The application provides four main features:
+
+1. **Financial Dashboard**: Visualize your startup's financial health, runway, and key metrics
+2. **Decision Simulator**: Simulate the impact of business decisions on your startup's runway
+3. **Fund Monitoring**: Detect suspicious transactions and monitor spending patterns
+4. **Financial Advisory**: Get AI-powered financial advice through a conversational interface
+
+## How to Use
+
+This application is deployed on Hugging Face Spaces and can be accessed directly through the web interface. No installation is required.
+
+### API Key Setup
+
+To enable all AI features, you need to add your Google API key to the secrets:
+
+1. Go to the Hugging Face Space settings
+2. Click on "Repository secrets"
+3. Add a new secret named `GOOGLE_API_KEY` with your Google API key
+
+## Features
+
+### Financial Dashboard
+
+- View key financial metrics including cash balance, burn rate, and runway
+- Interactive charts showing runway projections, revenue vs. expenses, and burn rate trends
+- AI-powered financial analysis providing insights and recommendations
+
+### Decision Simulator
+
+- Simulate hiring decisions, marketing budget changes, and other expenses
+- Visualize the impact on runway and financial health
+- Get AI analysis of your decisions with specific recommendations
+
+### Fund Monitoring
+
+- Automatically detect suspicious transactions that might concern investors
+- Track spending patterns across different categories
+- Receive AI analysis of spending anomalies and recommendations
+
+### Financial Advisory
+
+- Ask financial questions through a conversational interface
+- Get strategic guidance on fundraising, spending, and financial planning
+- In the full version, receive voice-generated responses (text-only in this demo)
+
+## Data Privacy
+
+This application uses sample data for demonstration purposes. In a production environment, your financial data would remain private and secure.
+
+## Technologies Used
+
+- Streamlit for the web interface
+- Google's Gemini Pro API for AI capabilities
+-

gitignore.txt

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+# Python
+__pycache__/
+*.py[cod]
+*$py.class
+*.so
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+
+# Environment
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Streamlit
+.streamlit/
+
+# OS specific
+.DS_Store
+.DS_Store?
+._*
+.Spotlight-V100
+.Trashes
+ehthumbs.db
+Thumbs.db
+
+# IDE specific files
+.idea/
+.vscode/
+*.swp
+*.swo

huggingface-config.txt

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+---
+title: StartupFinancePilot
+emoji: 💰
+colorFrom: blue
+colorTo: indigo
+sdk: streamlit
+sdk_version: 1.24.0
+app_file: app.py
+pinned: false
+license: mit
+---
+
+# StartupFinancePilot
+
+AI-powered financial assistant for startups that helps founders make better financial decisions and protect investor funds.
+
+## Features
+
+- **Financial Dashboard**: Visualize runway, financial health, and key metrics
+- **Decision Simulator**: Simulate the impact of business decisions on runway
+- **Fund Monitoring**: Detect suspicious transactions and spending patterns
+- **Financial Advisory**: Get AI-powered advice through conversation
+
+## Setup
+
+1. Add your Google Gemini API key to the Space secrets with the name `GOOGLE_API_KEY`
+2. Launch the application
+
+## Demo Data
+
+The application comes with sample data for TechHealth AI, a fictional seed-stage startup.

huggingface-setup.py

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+import streamlit as st
+import pandas as pd
+import numpy as np
+import plotly.express as px
+import plotly.graph_objects as go
+import google.generativeai as genai
+import os
+from datetime import datetime, timedelta
+import base64
+from io import BytesIO
+
+# Initialize page configuration
+st.set_page_config(
+    page_title="StartupFinancePilot",
+    page_icon="💰",
+    layout="wide",
+    initial_sidebar_state="expanded"
+)
+
+# Constants
+DEFAULT_GROWTH_RATE = 0.08  # 8% monthly growth
+DEFAULT_BURN_RATE = 85000   # $85,000 monthly burn
+ENGINEER_SALARY = 10000     # $10,000 monthly cost per engineer ($120K/year)
+DEFAULT_MARKETING_BUDGET = 10000  # $10,000 monthly marketing budget
+
+# Sample data
+@st.cache_data
+def load_sample_data():
+    # TechHealth AI data
+    startup_data = {
+        "name": "TechHealth AI",
+        "stage": "Seed",
+        "founded": "18 months ago",
+        "employees": 12,
+        "last_funding": "$1.2M seed round 10 months ago",
+        "cash": 320000,
+        "burn_rate": 85000,
+        "revenue": 15000,
+        "growth_rate": 0.08
+    }
+    
+    # Cash flow history
+    cash_flow_data = {
+        "Month": [f"Month {i}" for i in range(1, 11)],
+        "Revenue": [8000, 8500, 9200, 10000, 10800, 11700, 12600, 13600, 14700, 15800],
+        "Payroll": [60000, 60000, 62000, 62000, 65000, 65000, 70000, 70000, 75000, 75000],
+        "Marketing": [8000, 9000, 10000, 12000, 15000, 18000, 15000, 12000, 10000, 8000],
+        "Office": [5000, 5000, 5000, 5000, 5000, 5000, 5000, 5000, 5000, 5000],
+        "Software": [3000, 3200, 3500, 3800, 4000, 4200, 4500, 4800, 5000, 5200],
+        "Travel": [2000, 1800, 2500, 3000, 4000, 4500, 3500, 3000, 2500, 2000],
+        "Legal": [1500, 1000, 800, 1200, 800, 2000, 1500, 1000, 3000, 1200],
+        "Misc": [1000, 1200, 1300, 1500, 1700, 1800, 2000, 2200, 2500, 2800]
+    }
+    
+    # Add calculated fields
+    df = pd.DataFrame(cash_flow_data)
+    df["Total_Expenses"] = df[["Payroll", "Marketing", "Office", "Software", "Travel", "Legal", "Misc"]].sum(axis=1)
+    df["Net_Burn"] = df["Total_Expenses"] - df["Revenue"]
+    
+    # Transaction data
+    transactions = pd.DataFrame([
+        {"Date": "2023-11-05", "Category": "Travel", "Vendor": "Caribbean Cruises", "Amount": 8500, "Description": "Team Retreat Planning", "Flag": "Suspicious"},
+        {"Date": "2023-11-12", "Category": "Marketing", "Vendor": "LuxuryGifts Inc", "Amount": 4200, "Description": "Client Appreciation", "Flag": "Suspicious"},
+        {"Date": "2023-11-22", "Category": "Office", "Vendor": "Premium Furniture", "Amount": 12000, "Description": "Office Upgrades", "Flag": "Suspicious"},
+        {"Date": "2023-11-28", "Category": "Consulting", "Vendor": "Strategic Vision LLC", "Amount": 7500, "Description": "Strategy Consulting", "Flag": "Suspicious"},
+        {"Date": "2023-12-05", "Category": "Software", "Vendor": "Personal Apple Store", "Amount": 3200, "Description": "Development Tools", "Flag": "Suspicious"},
+        {"Date": "2023-12-12", "Category": "Legal", "Vendor": "Anderson Brothers", "Amount": 5800, "Description": "Legal Services", "Flag": "Normal"},
+        {"Date": "2023-12-20", "Category": "Payroll", "Vendor": "November Payroll", "Amount": 75000, "Description": "Monthly Payroll", "Flag": "Normal"},
+        {"Date": "2023-12-22", "Category": "Marketing", "Vendor": "Google Ads", "Amount": 8000, "Description": "Ad Campaign", "Flag": "Normal"},
+        {"Date": "2023-12-25", "Category": "Office", "Vendor": "WeWork", "Amount": 5000, "Description": "Monthly Rent", "Flag": "Normal"},
+        {"Date": "2023-12-28", "Category": "Software", "Vendor": "AWS", "Amount": 5200, "Description": "Cloud Services", "Flag": "Normal"},
+        {"Date": "2024-01-05", "Category": "Travel", "Vendor": "Delta Airlines", "Amount": 1200, "Description": "Client Meeting Travel", "Flag": "Normal"},
+        {"Date": "2024-01-10", "Category": "Marketing", "Vendor": "Facebook Ads", "Amount": 4500, "Description": "Social Media Campaign", "Flag": "Normal"},
+        {"Date": "2024-01-15", "Category": "Software", "Vendor": "Atlassian", "Amount": 2800, "Description": "Development Tools", "Flag": "Normal"},
+        {"Date": "2024-01-20", "Category": "Payroll", "Vendor": "January Payroll", "Amount": 75000, "Description": "Monthly Payroll", "Flag": "Normal"},
+        {"Date": "2024-01-25", "Category": "Office", "Vendor": "WeWork", "Amount": 5000, "Description": "Monthly Rent", "Flag": "Normal"}
+    ])
+    
+    return startup_data, df, transactions
+
+# Setup Gemini API (with API key from secrets)
+def setup_genai():
+    if 'GOOGLE_API_KEY' in st.secrets:
+        genai.configure(api_key=st.secrets['GOOGLE_API_KEY'])
+        return True
+    else:
+        st.warning("Google API key not found. Please add it to the secrets.")
+        return False
+
+# Financial modeling functions
+def calculate_runway(initial_cash, monthly_burn, monthly_revenue, growth_rate, months=24):
+    """Calculate runway based on current burn rate and revenue growth."""
+    dates = [datetime.now() + timedelta(days=30*i) for i in range(months)]
+    df = pd.DataFrame(index=dates, columns=['Cash', 'Revenue', 'Expenses', 'Net_Burn', 'Cumulative_Cash'])
+    
+    current_cash = initial_cash
+    current_revenue = monthly_revenue
+    df.iloc[0, df.columns.get_loc('Cash')] = current_cash
+    df.iloc[0, df.columns.get_loc('Revenue')] = current_revenue
+    df.iloc[0, df.columns.get_loc('Expenses')] = monthly_burn
+    df.iloc[0, df.columns.get_loc('Net_Burn')] = monthly_burn - current_revenue
+    df.iloc[0, df.columns.get_loc('Cumulative_Cash')] = current_cash
+    
+    runway_months = months
+    for i in range(1, months):
+        current_revenue = current_revenue * (1 + growth_rate)
+        net_burn = monthly_burn - current_revenue
+        current_cash = current_cash - net_burn
+        
+        df.iloc[i, df.columns.get_loc('Cash')] = current_cash
+        df.iloc[i, df.columns.get_loc('Revenue')] = current_revenue
+        df.iloc[i, df.columns.get_loc('Expenses')] = monthly_burn
+        df.iloc[i, df.columns.get_loc('Net_Burn')] = net_burn
+        df.iloc[i, df.columns.get_loc('Cumulative_Cash')] = current_cash
+        
+        if current_cash <= 0:
+            runway_months = i
+            break
+    
+    return runway_months, df
+
+def simulate_decision(initial_cash, monthly_burn, monthly_revenue, growth_rate, 
+                     new_expenses=0, new_hires=0, new_marketing=0, growth_impact=0):
+    """Simulate the impact of a business decision on runway."""
+    # Calculate current runway
+    current_runway, current_df = calculate_runway(initial_cash, monthly_burn, monthly_revenue, growth_rate)
+    
+    # Calculate additional expenses
+    additional_expenses = new_expenses + (new_hires * ENGINEER_SALARY) + new_marketing
+    
+    # Calculate new runway
+    new_runway, new_df = calculate_runway(
+        initial_cash,
+        monthly_burn + additional_expenses,
+        monthly_revenue,
+        growth_rate + growth_impact
+    )
+    
+    return current_runway, new_runway, current_df, new_df
+
+def detect_suspicious_transactions(transactions_df):
+    """Simple rule-based 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'] = ""
+    
+    # Check for suspicious patterns
+    for idx, row in df.iterrows():
+        reasons = []
+        
+        # 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']}")
+        
+        # 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")
+        
+        if any(term in str(row['Description']).lower() for term in suspicious_terms):
+            reasons.append(f"Description contains suspicious term")
+        
+        # Mark as suspicious if any reasons
+        if reasons:
+            df.at[idx, 'Suspicious'] = True
+            df.at[idx, 'Reason'] = "; ".join(reasons)
+    
+    return df
+
+# Gemini prompt functions
+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}%
+
+    Calculate and explain their runway, financial health, and recommendations in a concise paragraph.
+    """
+    
+    model = genai.GenerativeModel('gemini-pro')
+    response = model.generate_content(prompt)
+    
+    return response.text
+
+def get_decision_analysis(question, financial_data):
+    """Get analysis for a specific decision."""
+    prompt = f"""
+    You are a financial advisor for startups. A founder asks:
+    "{question}"
+
+    Here's their current financial situation:
+    - Current cash: ${financial_data['cash']}
+    - Monthly burn rate: ${financial_data['burn_rate']}
+    - Monthly revenue: ${financial_data['revenue']}
+    - Monthly growth rate: {financial_data['growth_rate'] * 100}%
+
+    Analyze how this decision would impact their runway and financial health.
+    Provide specific recommendations in 3-4 concise sentences.
+    """
+    
+    model = genai.GenerativeModel('gemini-pro')
+    response = model.generate_content(prompt)
+    
+    return response.text
+
+def get_fraud_analysis(transactions_df):
+    """Get analysis of potentially fraudulent transactions."""
+    suspicious_df = transactions_df[transactions_df['Suspicious']]
+    
+    if len(suspicious_df) == 0:
+        return "No suspicious transactions detected."
+    
+    transactions_text = suspicious_df.to_string(index=False)
+    
+    prompt = f"""
+    You are a financial advisor specializing in startup spending oversight.
+    Review these flagged transactions:
+
+    {transactions_text}
+
+    Explain why these transactions might concern investors and what actions the startup should take.
+    Keep your response to 4-5 concise sentences focused on the most concerning issues.
+    """
+    
+    model = genai.GenerativeModel('gemini-pro')
+    response = model.generate_content(prompt)
+    
+    return response.text
+
+def get_advisory_guidance(question, financial_data):
+    """Get strategic guidance for a startup question."""
+    prompt = f"""
+    You are a strategic financial advisor for startups. A founder asks:
+    "{question}"
+
+    Here's their current financial situation:
+    - Stage: {financial_data['stage']}
+    - Current cash: ${financial_data['cash']}
+    - Monthly burn rate: ${financial_data['burn_rate']}
+    - Monthly revenue: ${financial_data['revenue']}
+    - Monthly growth rate: {financial_data['growth_rate'] * 100}%
+    - Last funding: {financial_data['last_funding']}
+    - Team size: {financial_data['employees']}
+
+    Provide specific, actionable advice addressing their question in 3-4 concise sentences.
+    Include timelines and metrics where relevant.
+    """
+    
+    model = genai.GenerativeModel('gemini-pro')
+    response = model.generate_content(prompt)
+    
+    return response.text
+
+# UI Components
+def create_sidebar():
+    """Create sidebar with company profile and filters."""
+    st.sidebar.title("StartupFinancePilot")
+    st.sidebar.image("https://img.freepik.com/premium-vector/business-finance-analytics-logo-design-vector-template_67715-552.jpg", width=150)
+    
+    # Company profile
+    startup_data, _, _ = load_sample_data()
+    
+    st.sidebar.header("Company Profile")
+    st.sidebar.write(f"**{startup_data['name']}**")
+    st.sidebar.write(f"Stage: {startup_data['stage']}")
+    st.sidebar.write(f"Founded: {startup_data['founded']}")
+    st.sidebar.write(f"Employees: {startup_data['employees']}")
+    st.sidebar.write(f"Last Funding: {startup_data['last_funding']}")
+    
+    # App navigation
+    st.sidebar.header("Navigation")
+    page = st.sidebar.radio("Go to", ["Financial Dashboard", "Decision Simulator", "Fund Monitoring", "Voice Advisory"])
+    
+    return page
+
+def render_financial_dashboard(startup_data, cash_flow_df):
+    """Render financial dashboard page."""
+    st.title("Financial Dashboard")
+    
+    # Key metrics
+    col1, col2, col3, col4 = st.columns(4)
+    
+    # Calculate runway
+    runway_months, runway_df = calculate_runway(
+        startup_data['cash'], 
+        startup_data['burn_rate'], 
+        startup_data['revenue'], 
+        startup_data['growth_rate']
+    )
+    
+    with col1:
+        st.metric("Current Cash", f"${startup_data['cash']:,}")
+    with col2:
+        st.metric("Monthly Burn", f"${startup_data['burn_rate']:,}")
+    with col3:
+        st.metric("Monthly Revenue", f"${startup_data['revenue']:,}")
+    with col4:
+        st.metric("Runway", f"{runway_months} months")
+    
+    # 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'})
+        fig.add_hline(y=0, line_dash="dash", line_color="red")
+        fig.update_layout(height=400)
+        st.plotly_chart(fig, use_container_width=True)
+        
+        # Get analysis from Gemini
+        if setup_genai():
+            with st.expander("AI Financial Analysis"):
+                analysis = get_runway_analysis(startup_data)
+                st.write(analysis)
+    
+    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 ($)', 'variable': 'Category'})
+        fig.update_layout(height=400)
+        st.plotly_chart(fig, use_container_width=True)
+    
+    with tab3:
+        # Burn rate trend
+        fig = px.line(cash_flow_df, x='Month', y='Net_Burn',
+                     title="Monthly Net Burn Trend",
+                     labels={'Net_Burn': 'Net Burn ($)'})
+        fig.update_layout(height=400)
+        st.plotly_chart(fig, use_container_width=True)
+    
+    # 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]
+    
+    fig = px.pie(values=expense_values, names=expense_categories, 
+                title="Current Month Expense Breakdown")
+    fig.update_layout(height=400)
+    st.plotly_chart(fig, use_container_width=True)
+
+def render_decision_simulator(startup_data):
+    """Render decision simulator page."""
+    st.title("Decision Simulator")
+    
+    st.write("Simulate the impact of business decisions on your startup's financial runway.")
+    
+    # Decision input form
+    with st.form("decision_form"):
+        st.subheader("Enter Decision Parameters")
+        
+        col1, col2 = st.columns(2)
+        
+        with col1:
+            new_hires = st.number_input("New Engineering Hires", min_value=0, max_value=10, value=0, 
+                                         help=f"Each engineer costs ${ENGINEER_SALARY:,} per month")
+            new_marketing = st.number_input("Additional Monthly Marketing Budget", min_value=0, max_value=50000, value=0, 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=0, 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=0.0, step=0.01, 
+                                     format="%.2f",
+                                     help="Estimated increase in monthly growth rate due to these investments")
+        
+        question = st.text_area("Describe your decision scenario", height=100, 
+                               placeholder="E.g., We're considering hiring two more engineers and increasing our marketing budget...")
+        
+        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.subheader("Decision Impact")
+        
+        # Summary metrics
+        col1, col2, col3 = st.columns(3)
+        
+        with col1:
+            st.metric("Current Runway", f"{current_runway} months")
+        with col2:
+            st.metric("New Runway", f"{new_runway} months", delta=new_runway-current_runway)
+        with col3:
+            new_burn = startup_data['burn_rate'] + other_expenses + (new_hires * ENGINEER_SALARY) + new_marketing
+            st.metric("New Monthly Burn", f"${new_burn:,}", delta=new_burn-startup_data['burn_rate'], 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'})
+        fig.add_hline(y=0, line_dash="dash", line_color="red")
+        fig.update_layout(height=400)
+        st.plotly_chart(fig, use_container_width=True)
+        
+        # Get AI analysis
+        if setup_genai() and question:
+            st.subheader("AI Analysis")
+            analysis = get_decision_analysis(question, startup_data)
+            st.success(analysis)
+
+def render_fund_monitoring(transactions_df):
+    """Render fund monitoring page."""
+    st.title("Investor Fund Monitoring")
+    
+    st.write("Monitor your startup's spending to maintain investor trust and ensure proper fund usage.")
+    
+    # Process transactions to detect suspicious ones
+    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 = st.columns(3)
+    
+    with col1:
+        st.metric("Total Transactions", total_transactions)
+    with col2:
+        st.metric("Flagged Transactions", suspicious_count, 
+                 delta=f"{suspicious_count/total_transactions:.1%}" if total_transactions > 0 else "0%")
+    with col3:
+        st.metric("Flagged Amount", f"${suspicious_amount:,}", 
+                delta=f"{suspicious_amount/total_amount:.1%}" if total_amount > 0 else "0%")
+    
+    # Tabs for different views
+    tab1, tab2 = st.tabs(["Flagged Transactions", "All Transactions"])
+    
+    with tab1:
+        if suspicious_count > 0:
+            st.dataframe(suspicious_transactions[['Date', 'Category', 'Vendor', 'Amount', 'Description', 'Reason']], 
+                        use_container_width=True)
+            
+            # Get AI analysis of suspicious transactions
+            if setup_genai():
+                st.subheader("AI Fraud Analysis")
+                analysis = get_fraud_analysis(suspicious_transactions)
+                st.warning(analysis)
+        else:
+            st.success("No suspicious transactions detected.")
+    
+    with tab2:
+        st.dataframe(processed_df[['Date', 'Category', 'Vendor', 'Amount', 'Description', 'Suspicious']], 
+                    use_container_width=True)
+    
+    # Spending patterns
+    st.subheader("Spending Patterns")
+    
+    # Category breakdown
+    category_spending = processed_df.groupby('Category')['Amount'].sum().reset_index()
+    
+    fig = px.bar(category_spending, x='Category', y='Amount',
+                title="Spending by Category",
+                labels={'Amount': 'Total Spent ($)'})
+    fig.update_layout(height=400)
+    st.plotly_chart(fig, use_container_width=True)
+    
+    # Time series of spending
+    processed_df['Date'] = pd.to_datetime(processed_df['Date'])
+    processed_df['Week'] = processed_df['Date'].dt.isocalendar().week
+    weekly_spending = processed_df.groupby(['Week', 'Category'])['Amount'].sum().reset_index()
+    
+    fig = px.line(weekly_spending, x='Week', y='Amount', color='Category',
+                title="Weekly Spending Trends",
+                labels={'Amount': 'Amount Spent ($)'})
+    fig.update_layout(height=400)
+    st.plotly_chart(fig, use_container_width=True)
+
+def render_voice_advisory(startup_data):
+    """Render voice advisory page."""
+    st.title("Financial Advisory")
+    
+    st.write("Get strategic financial guidance from our AI advisor.")
+    
+    # Common financial questions
+    st.subheader("Select a Question or Ask Your Own")
+    
+    common_questions = [
+        "Select a question...",
+        "How much runway do we have at our current burn rate?",
+        "If we hire two more engineers, how will it affect our runway?",
+        "Should we increase our marketing spend given our current growth rate?",
+        "Is our current burn rate sustainable given our revenue growth?",
+        "Should we consider raising a bridge round in the next 3 months?",
+        "What's our projected cash position at the end of next quarter?",
+        "Are there any concerning spending patterns in our recent expenses?",
+        "What metrics should we focus on improving before our next fundraise?"
+    ]
+    
+    selected_question = st.selectbox("Common Questions", common_questions)
+    
+    custom_question = st.text_area("Or ask your own question", height=100,
+                                 placeholder="Enter your financial question here...")
+    
+    question = custom_question if custom_question else (selected_question if selected_question != common_questions[0] else "")
+    
+    if st.button("Get Advice") and question and setup_genai():
+        with st.spinner("Generating advisory response..."):
+            advice = get_advisory_guidance(question, startup_data)
+            
+            # Display advice
+            st.subheader("Financial Advice")
+            st.info(advice)
+            
+            # Note about voice generation
+            st.write("In the full version, this advice would be delivered as a voice response using ElevenLabs.")
+            
+            # Add a sample conversation
+            with st.expander("View sample conversation"):
+                st.write("**You:** " + question)
+                st.write("**Financial Advisor:** " + advice)
+
+# Main application
+def main():
+    # Load sample data
+    startup_data, cash_flow_df, transactions_df = load_sample_data()
+    
+    # Create sidebar and get selected page
+    page = create_sidebar()
+    
+    # Render selected page
+    if page == "Financial Dashboard":
+        render_financial_dashboard(startup_data, cash_flow_df)
+    elif page == "Decision Simulator":
+        render_decision_simulator(startup_data)
+    elif page == "Fund Monitoring":
+        render_fund_monitoring(transactions_df)
+    elif page == "Voice Advisory":
+        render_voice_advisory(startup_data)
+
+if __name__ == "__main__":
+    main()

requirements-huggingface.txt

@@ -0,0 +1,5 @@
+streamlit==1.24.0
+google-generativeai==0.3.2
+pandas==2.0.0
+numpy==1.24.3
+plotly==5.14.1

sample-data.py

@@ -0,0 +1,70 @@
+import pandas as pd
+import os
+
+# Create data directory if it doesn't exist
+os.makedirs('data', exist_ok=True)
+
+# TechHealth AI data
+company_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
+}
+
+# Save company data
+pd.DataFrame([company_data]).to_csv('data/startup_data.csv', index=False)
+
+# Cash flow history
+cash_flow_data = {
+    "Month": [f"Month {i}" for i in range(1, 11)],
+    "Revenue": [8000, 8500, 9200, 10000, 10800, 11700, 12600, 13600, 14700, 15800],
+    "Payroll": [60000, 60000, 62000, 62000, 65000, 65000, 70000, 70000, 75000, 75000],
+    "Marketing": [8000, 9000, 10000, 12000, 15000, 18000, 15000, 12000, 10000, 8000],
+    "Office": [5000, 5000, 5000, 5000, 5000, 5000, 5000, 5000, 5000, 5000],
+    "Software": [3000, 3200, 3500, 3800, 4000, 4200, 4500, 4800, 5000, 5200],
+    "Travel": [2000, 1800, 2500, 3000, 4000, 4500, 3500, 3000, 2500, 2000],
+    "Legal": [1500, 1000, 800, 1200, 800, 2000, 1500, 1000, 3000, 1200],
+    "Misc": [1000, 1200, 1300, 1500, 1700, 1800, 2000, 2200, 2500, 2800]
+}
+
+# Add calculated fields
+df = pd.DataFrame(cash_flow_data)
+df["Total_Expenses"] = df[["Payroll", "Marketing", "Office", "Software", "Travel", "Legal", "Misc"]].sum(axis=1)
+df["Net_Burn"] = df["Total_Expenses"] - df["Revenue"]
+
+# Save cash flow data
+df.to_csv('data/projections.csv', index=False)
+
+# Transaction data
+transactions = pd.DataFrame([
+    {"Date": "2023-11-05", "Category": "Travel", "Vendor": "Caribbean Cruises", "Amount": 8500, "Description": "Team Retreat Planning", "Flag": "Suspicious"},
+    {"Date": "2023-11-12", "Category": "Marketing", "Vendor": "LuxuryGifts Inc", "Amount": 4200, "Description": "Client Appreciation", "Flag": "Suspicious"},
+    {"Date": "2023-11-22", "Category": "Office", "Vendor": "Premium Furniture", "Amount": 12000, "Description": "Office Upgrades", "Flag": "Suspicious"},
+    {"Date": "2023-11-28", "Category": "Consulting", "Vendor": "Strategic Vision LLC", "Amount": 7500, "Description": "Strategy Consulting", "Flag": "Suspicious"},
+    {"Date": "2023-12-05", "Category": "Software", "Vendor": "Personal Apple Store", "Amount": 3200, "Description": "Development Tools", "Flag": "Suspicious"},
+    {"Date": "2023-12-12", "Category": "Legal", "Vendor": "Anderson Brothers", "Amount": 5800, "Description": "Legal Services", "Flag": "Normal"},
+    {"Date": "2023-12-20", "Category": "Payroll", "Vendor": "November Payroll", "Amount": 75000, "Description": "Monthly Payroll", "Flag": "Normal"},
+    {"Date": "2023-12-22", "Category": "Marketing", "Vendor": "Google Ads", "Amount": 8000, "Description": "Ad Campaign", "Flag": "Normal"},
+    {"Date": "2023-12-25", "Category": "Office", "Vendor": "WeWork", "Amount": 5000, "Description": "Monthly Rent", "Flag": "Normal"},
+    {"Date": "2023-12-28", "Category": "Software", "Vendor": "AWS", "Amount": 5200, "Description": "Cloud Services", "Flag": "Normal"},
+    {"Date": "2024-01-05", "Category": "Travel", "Vendor": "Delta Airlines", "Amount": 1200, "Description": "Client Meeting Travel", "Flag": "Normal"},
+    {"Date": "2024-01-10", "Category": "Marketing", "Vendor": "Facebook Ads", "Amount": 4500, "Description": "Social Media Campaign", "Flag": "Normal"},
+    {"Date": "2024-01-15", "Category": "Software", "Vendor": "Atlassian", "Amount": 2800, "Description": "Development Tools", "Flag": "Normal"},
+    {"Date": "2024-01-20", "Category": "Payroll", "Vendor": "January Payroll", "Amount": 75000, "Description": "Monthly Payroll", "Flag": "Normal"},
+    {"Date": "2024-01-25", "Category": "Office", "Vendor": "WeWork", "Amount": 5000, "Description": "Monthly Rent", "Flag": "Normal"}
+])
+
+# Save transactions data
+transactions.to_csv('data/transactions.csv', index=False)
+
+# Create a separate file for suspicious transactions for easier analysis
+suspicious = transactions[transactions['Flag'] == 'Suspicious']
+suspicious.to_csv('data/suspicious.csv', index=False)
+
+print("Sample data files have been created in the 'data' directory.")