Update app.py

app.py

@@ -1,508 +1,29 @@
 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
+from pages import dashboard, financial_analysis, sign_language, settings
 
-# 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']
-        )
+    st.set_page_config(
+        page_title="FinAI Dashboard",
+        page_icon="📊",
+        layout="wide"
+    )
+
+    # Sidebar navigation
+    st.sidebar.title("Navigation")
+    page = st.sidebar.radio(
+        "Go to",
+        ["Dashboard", "Financial Analysis", "Sign Language", "Settings"]
+    )
+
+    # Page routing
+    if page == "Dashboard":
+        dashboard.render_page()
+    elif page == "Financial Analysis":
+        financial_analysis.render_page()
+    elif page == "Sign Language":
+        sign_language.render_page()
+    elif page == "Settings":
+        settings.render_page()
 
 if __name__ == "__main__":
-    main()
+    main()