import pandas as pd
import numpy as np
from datetime import datetime, timedelta

def calculate_runway(current_cash, burn_rate, monthly_revenue, growth_rate, months=12):
    """
    Calculate the startup's runway based on financial parameters
    
    Args:
        current_cash (float): Current cash balance
        burn_rate (float): Monthly burn rate
        monthly_revenue (float): Current monthly revenue
        growth_rate (float): Monthly revenue growth rate
        months (int, optional): Projection period. Defaults to 12.
    
    Returns:
        tuple: (runway months, runway dataframe)
    """
    # Create projection dataframe
    df = pd.DataFrame(index=range(months))
    
    # Initialize first month
    df.loc[0, 'Revenue'] = monthly_revenue
    df.loc[0, 'Burn_Rate'] = burn_rate
    df.loc[0, 'Net_Burn'] = burn_rate - monthly_revenue
    df.loc[0, 'Cumulative_Cash'] = current_cash - df.loc[0, 'Net_Burn']
    
    # Project forward
    for i in range(1, months):
        # Grow revenue
        df.loc[i, 'Revenue'] = df.loc[i-1, 'Revenue'] * (1 + growth_rate)
        
        # Adjust burn rate (simplified model)
        df.loc[i, 'Burn_Rate'] = burn_rate  # Could make this more sophisticated
        
        # Calculate net burn
        df.loc[i, 'Net_Burn'] = df.loc[i, 'Burn_Rate'] - df.loc[i, 'Revenue']
        
        # Calculate cumulative cash
        df.loc[i, 'Cumulative_Cash'] = df.loc[i-1, 'Cumulative_Cash'] - df.loc[i, 'Net_Burn']
        
        # Stop if out of cash
        if df.loc[i, 'Cumulative_Cash'] <= 0:
            break
    
    # Calculate runway months
    runway_months = df[df['Cumulative_Cash'] > 0].shape[0]
    
    return runway_months, df

def generate_sample_startup_data():
    """
    Generate sample startup financial data for demonstration
    
    Returns:
        dict: Sample startup financial data
    """
    return {
        "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
    }

def generate_sample_cash_flow_data():
    """
    Generate sample cash flow data for demonstration
    
    Returns:
        pd.DataFrame: Sample cash flow DataFrame
    """
    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]
    }
    
    # Create DataFrame
    df = pd.DataFrame(cash_flow_data)
    
    # Add calculated fields
    df["Total_Expenses"] = df[["Payroll", "Marketing", "Office", "Software", "Travel", "Legal", "Misc"]].sum(axis=1)
    df["Net_Burn"] = df["Total_Expenses"] - df["Revenue"]
    
    return df

def generate_sample_transactions_data():
    """
    Generate sample transactions data for demonstration
    
    Returns:
        pd.DataFrame: Sample transactions DataFrame
    """
    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 transactions