Upload main-app (1).py

main-app (1).py

@@ -0,0 +1,508 @@
+import streamlit as st
+import pandas as pd
+import numpy as np
+import os
+from datetime import datetime, timedelta, date
+import time
+import io
+import base64
+
+# Import your existing pages (make sure they're in the same directory or in a 'pages' directory)
+# If you have these in separate files, you'll need to adjust the imports
+# For example:
+# from pages.dashboard import render_financial_dashboard
+# from pages.decision_simulator import render_decision_simulator
+# from pages.fund_monitoring import render_fund_monitoring
+# from pages.advisor import render_ai_financial_advisor
+
+# 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 = {}
+
+def switch_page(page_name):
+    """Function to switch between pages"""
+    st.session_state.current_page = page_name
+    st.rerun()
+
+# Page config
+st.set_page_config(
+    page_title="StartupFinancePilot",
+    page_icon="💰",
+    layout="wide",
+    initial_sidebar_state="expanded"
+)
+
+# Custom CSS
+st.markdown("""
+<style>
+    #MainMenu {visibility: hidden;}
+    footer {visibility: hidden;}
+    .stDeployButton {display:none;}
+    
+    .main-header {
+        font-size: 2.5rem;
+        color: #0066cc;
+        margin-bottom: 0.5rem;
+    }
+    .sub-header {
+        font-size: 1.5rem;
+        color: #5c5c5c;
+        margin-bottom: 1.5rem;
+    }
+    .metric-card {
+        background-color: #f8f9fa;
+        border-radius: 10px;
+        padding: 20px;
+        box-shadow: 0 4px 6px rgba(0,0,0,0.1);
+    }
+    .metric-label {
+        font-size: 1rem;
+        color: #5c5c5c;
+    }
+    .metric-value {
+        font-size: 1.8rem;
+        color: #0066cc;
+        font-weight: bold;
+    }
+    .good-metric {
+        color: #28a745;
+    }
+    .warning-metric {
+        color: #ffc107;
+    }
+    .danger-metric {
+        color: #dc3545;
+    }
+    
+    /* Style for sidebar buttons */
+    div.stButton > button {
+        width: 100%;
+        padding: 10px 10px;
+        border: none;
+        background-color: #E6F3FF;
+        color: #0066cc;
+        border-radius: 10px;
+        text-align: left;
+        margin: 5px 0;
+        font-weight: bold;
+    }
+    
+    div.stButton > button:hover {
+        background-color: #CCE5FF;
+        color: #004080;
+    }
+    
+    /* Style for title box */
+    .title-box {
+        background: linear-gradient(45deg, #0066cc, #66b3ff);
+        padding: 20px;
+        border-radius: 10px;
+        margin-bottom: 20px;
+        text-align: center;
+        color: white;
+        cursor: pointer;
+    }
+</style>
+""", unsafe_allow_html=True)
+
+# Sample data
+def load_sample_data():
+    """Load sample data for demonstration"""
+    # 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"}
+    ])
+    
+    return startup_data, df, transactions
+
+# 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}"
+
+# Upload and process financial data files
+def render_upload_page():
+    """Render the upload page for startup data"""
+    st.markdown("<h1 class='main-header'>Upload Your Startup Data</h1>", unsafe_allow_html=True)
+    st.markdown("<p class='sub-header'>Upload CSV files or use sample data to get started</p>", unsafe_allow_html=True)
+    
+    with st.expander("Upload Instructions", expanded=False):
+        st.markdown("""
+        ### How to Upload Your Startup Data
+        
+        You can upload three types of files:
+        
+        1. **Company Profile** - A CSV with basic information about your startup including:
+           - name, stage, founded, employees, last_funding, cash, burn_rate, revenue, growth_rate
+        
+        2. **Cash Flow Data** - A CSV with monthly cash flow data with columns:
+           - Month, Revenue, Payroll, Marketing, Office, Software, Travel, Legal, Misc
+        
+        3. **Transaction Data** - A CSV with transaction details:
+           - Date, Category, Vendor, Amount, Description, Flag
+           
+        If you don't have these files ready, you can use our sample data.
+        """)
+    
+    col1, col2 = st.columns(2)
+    
+    with col1:
+        startup_name = st.text_input("Startup Name", value="My Startup")
+        
+        profile_file = st.file_uploader("Upload Company Profile (CSV)", type=['csv'])
+        cash_flow_file = st.file_uploader("Upload Cash Flow Data (CSV)", type=['csv'])
+        transactions_file = st.file_uploader("Upload Transactions Data (CSV)", type=['csv'])
+    
+    with col2:
+        st.markdown("""
+        <div style="background-color: #f0f7ff; padding: 15px; border-radius: 10px; height: 90%;">
+            <h4>Why Upload Your Data?</h4>
+            <p>By uploading your actual financial data, you'll get:</p>
+            <ul>
+                <li>Personalized AI insights tailored to your startup</li>
+                <li>Accurate runway projections based on your real spending patterns</li>
+                <li>Custom recommendations to optimize your burn rate</li>
+                <li>More realistic decision simulations</li>
+            </ul>
+            <p>All data is processed securely and never stored permanently.</p>
+        </div>
+        """, unsafe_allow_html=True)
+    
+    # Process the files if uploaded
+    if st.button("Process Data"):
+        # Initialize with default values
+        startup_data = {
+            "name": startup_name,
+            "stage": "Seed",
+            "founded": "12 months ago",
+            "employees": 5,
+            "last_funding": "Not specified",
+            "cash": 100000,
+            "burn_rate": 20000,
+            "revenue": 5000,
+            "growth_rate": 0.05
+        }
+        
+        cash_flow_df = None
+        transactions_df = None
+        
+        # Parse company profile
+        if profile_file:
+            try:
+                profile_df, error = parse_csv_to_df(profile_file)
+                if error:
+                    st.error(error)
+                else:
+                    # Get the first row as a dictionary
+                    if len(profile_df) > 0:
+                        startup_data.update(profile_df.iloc[0].to_dict())
+                        st.success(f"Successfully loaded company profile for {startup_data['name']}")
+            except Exception as e:
+                st.error(f"Error processing company profile: {e}")
+        
+        # Parse cash flow data
+        if cash_flow_file:
+            cash_flow_df, error = parse_csv_to_df(cash_flow_file)
+            if error:
+                st.error(error)
+            else:
+                # Add calculated fields if not present
+                if "Total_Expenses" not in cash_flow_df.columns:
+                    expense_columns = [col for col in cash_flow_df.columns if col not in ["Month", "Revenue", "Total_Expenses", "Net_Burn"]]
+                    cash_flow_df["Total_Expenses"] = cash_flow_df[expense_columns].sum(axis=1)
+                
+                if "Net_Burn" not in cash_flow_df.columns:
+                    cash_flow_df["Net_Burn"] = cash_flow_df["Total_Expenses"] - cash_flow_df["Revenue"]
+                
+                st.success("Successfully loaded cash flow data")
+        
+        # Parse transactions data
+        if transactions_file:
+            transactions_df, error = parse_csv_to_df(transactions_file)
+            if error:
+                st.error(error)
+            else:
+                # Ensure transactions data has required columns
+                required_columns = ["Date", "Category", "Vendor", "Amount", "Description"]
+                if all(col in transactions_df.columns for col in required_columns):
+                    if "Flag" not in transactions_df.columns:
+                        transactions_df["Flag"] = "Normal"  # Default flag
+                    
+                    st.success("Successfully loaded transactions data")
+                else:
+                    st.error("Transactions file is missing required columns")
+        
+        # If any files were processed, save the data to session state
+        if profile_file or cash_flow_file or transactions_file:
+            if cash_flow_df is None:
+                # Create a sample cash flow dataframe if none was uploaded
+                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"]
+            
+            if transactions_df is None:
+                # Create a sample transactions dataframe if none was uploaded
+                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"]
+                }
+                transactions_df = pd.DataFrame(transactions_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
+            startup_data, cash_flow_df, transactions_df = load_sample_data()
+            
+            # Store in session state
+            st.session_state.startups["TechHealth AI"] = {
+                'profile': startup_data,
+                'cash_flow': cash_flow_df,
+                'transactions': transactions_df
+            }
+            
+            # Set as current startup
+            st.session_state.current_startup = "TechHealth AI"
+            
+            st.success("Successfully loaded TechHealth AI sample data")
+            # Redirect to dashboard
+            switch_page('dashboard')
+    
+    with sample_col2:
+        if st.button("Use Custom Sample"):
+            # Create a custom 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
+            }
+            
+            # Sample cash flow data
+            cash_flow_data = {
+                "Month": [f"Month {i}" for i in range(1, 11)],
+                "Revenue": [45000, 48000, 52000, 57000, 62000, 66000, 70000, 72000, 74000, 75000],
+                "Payroll": [140000, 142000, 145000, 150000, 160000, 165000, 175000, 180000, 185000, 190000],
+                "Marketing": [25000, 28000, 30000, 32000, 35000, 32000, 30000, 28000, 26000, 25000],
+                "Office": [12000, 12000, 12000, 12000, 12000, 12000, 12000, 12000, 12000, 12000],
+                "Software": [8000, 8500, 9000, 9500, 10000, 10500, 11000, 11500, 12000, 12500],
+                "Travel": [5000, 6000, 7000, 8000, 7000, 6000, 5000, 6000, 7000, 8000],
+                "Legal": [4000, 3000, 3500, 2500, 3000, 4000, 3500, 3000, 2500, 3000],
+                "Misc": [3000, 3500, 4000, 4500, 5000, 5500, 6000, 6500, 7000, 7500]
+            }
+            
+            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"]
+            
+            # Sample transaction data
+            transactions_df = pd.DataFrame([
+                {"Date": "2023-11-10", "Category": "Travel", "Vendor": "First Class Flights", "Amount": 12000, "Description": "Executive Retreat", "Flag": "Suspicious"},
+                {"Date": "2023-11-18", "Category": "Marketing", "Vendor": "VIP Events Co", "Amount": 18000, "Description": "Investor Dinner", "Flag": "Suspicious"},
+                {"Date": "2023-12-01", "Category": "Office", "Vendor": "Luxury Furniture", "Amount": 25000, "Description": "Executive Office Upgrade", "Flag": "Suspicious"},
+                {"Date": "2023-12-15", "Category": "Legal", "Vendor": "Premium Law Group", "Amount": 35000, "Description": "Legal Consultation", "Flag": "Normal"},
+                {"Date": "2023-12-20", "Category": "Payroll", "Vendor": "December Payroll", "Amount": 190000, "Description": "Monthly Payroll", "Flag": "Normal"}
+            ])
+            
+            # Store in session state
+            st.session_state.startups["GreenTech Innovations"] = {
+                'profile': startup_data,
+                'cash_flow': cash_flow_df,
+                'transactions': transactions_df
+            }
+            
+            # Set as current startup
+            st.session_state.current_startup = "GreenTech Innovations"
+            
+            st.success("Successfully loaded GreenTech Innovations sample data")
+            # Redirect to dashboard
+            switch_page('dashboard')
+
+# Create sidebar navigation
+def create_sidebar():
+    with st.sidebar:
+        # Title box that works as home button
+        st.markdown("""
+            <div class="title-box" onclick="window.location.href='#'">
+                <h1>💰 StartupFinancePilot</h1>
+                <p>AI-powered financial assistant for startups</p>
+            </div>
+        """, unsafe_allow_html=True)
+
+        # Startup selector (if there are startups in the session state)
+        if st.session_state.startups:
+            st.subheader("Selected Startup")
+            startup_names = list(st.session_state.startups.keys())
+            selected_startup = st.selectbox(
+                "Choose Startup", 
+                startup_names,
+                index=startup_names.index(st.session_state.current_startup) if st.session_state.current_startup in startup_names else 0
+            )
+            st.session_state.current_startup = selected_startup
+            
+            # Show basic startup info
+            if selected_startup in st.session_state.startups:
+                startup_data = st.session_state.startups[selected_startup]['profile']
+                st.markdown(f"""
+                **Stage:** {startup_data['stage']}  
+                **Cash:** ${startup_data['cash']:,}  
+                **Monthly Burn:** ${startup_data['burn_rate']:,}  
+                **Monthly Revenue:** ${startup_data['revenue']:,}  
+                """)
+
+        st.markdown("<hr>", unsafe_allow_html=True)  # Divider
+        
+        # Upload data button at the top
+        if st.button("📤 Upload Startup Data", use_container_width=True):
+            switch_page('upload')
+
+        # Navigation buttons
+        if st.button("📊 Financial Dashboard", use_container_width=True):
+            switch_page('dashboard')
+            
+        if st.button("🔮 Decision Simulator", use_container_width=True):
+            switch_page('simulator')
+            
+        if st.button("🕵️ Fund Monitoring", use_container_width=True):
+            switch_page('monitoring')
+            
+        if st.button("🤖 AI Financial Advisor", use_container_width=True):
+            switch_page('advisor')
+
+# Main function
+def main():
+    # Create sidebar navigation
+    create_sidebar()
+    
+    # Check if we have any data to work with
+    if not st.session_state.startups and st.session_state.current_page != 'upload':
+        st.warning("No startup data found. Please upload your data or use a sample dataset.")
+        render_upload_page()
+        return
+    
+    # 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':
+        # Replace with your dashboard page function
+        from dashboard import render_financial_dashboard
+        render_financial_dashboard(
+            st.session_state.startups[st.session_state.current_startup]['profile'],
+            st.session_state.startups[st.session_state.current_startup]['cash_flow']
+        )
+    elif st.session_state.current_page == 'simulator':
+        # Replace with your simulator page function
+        from decision_simulator import render_decision_simulator
+        render_decision_simulator(
+            st.session_state.startups[st.session_state.current_startup]['profile']
+        )
+    elif st.session_state.current_page == 'monitoring':
+        # Replace with your monitoring page function
+        from fund_monitoring import render_fund_monitoring
+        render_fund_monitoring(
+            st.session_state.startups[st.session_state.current_startup]['transactions']
+        )
+    elif st.session_state.current_page == 'advisor':
+        # Replace with your advisor page function
+        from advisor import render_ai_financial_advisor
+        render_ai_financial_advisor(
+            st.session_state.startups[st.session_state.current_startup]['profile']
+        )
+
+if __name__ == "__main__":
+    main()