# VisaTier 5.0 - Enhanced Immigration ROI Calculator
# Advanced AI-powered business migration intelligence with personalized insights
import math
import numpy as np
import pandas as pd
import gradio as gr
import plotly.graph_objects as go
import plotly.express as px
from plotly.subplots import make_subplots
import json
from datetime import datetime, timedelta
import hashlib
import secrets
from typing import Dict, List, Tuple, Optional
import asyncio
from dataclasses import dataclass, field
import random
# =========================
# ENHANCED STYLING SYSTEM
# =========================
PREMIUM_CSS = """
/* Modern Design System - Enhanced */
:root {
--primary: #2563eb;
--primary-dark: #1d4ed8;
--secondary: #0f172a;
--accent: #f59e0b;
--success: #10b981;
--warning: #f59e0b;
--error: #ef4444;
--surface: #ffffff;
--surface-alt: #f8fafc;
--text: #1e293b;
--text-muted: #64748b;
--border: #e2e8f0;
--radius: 12px;
--shadow: 0 4px 6px -1px rgba(0, 0, 0, 0.1);
--gradient: linear-gradient(135deg, var(--primary) 0%, var(--primary-dark) 100%);
--gradient-gold: linear-gradient(135deg, #fbbf24 0%, #f59e0b 100%);
}
/* Enhanced Global Styles */
.gradio-container {
max-width: 1400px !important;
margin: 0 auto !important;
font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, sans-serif !important;
}
/* Premium Header with Animation */
.premium-header {
background: var(--gradient);
color: white;
padding: 3rem 2rem;
border-radius: var(--radius);
margin-bottom: 2rem;
position: relative;
overflow: hidden;
}
.premium-header::before {
content: '';
position: absolute;
top: -50%;
right: -50%;
width: 200%;
height: 200%;
background: radial-gradient(circle, rgba(255,255,255,0.1) 0%, transparent 70%);
animation: float 8s ease-in-out infinite;
}
@keyframes float {
0%, 100% { transform: translateY(0px) rotate(0deg); }
50% { transform: translateY(-30px) rotate(180deg); }
}
.header-content {
position: relative;
z-index: 2;
display: flex;
justify-content: space-between;
align-items: center;
flex-wrap: wrap;
gap: 2rem;
}
.header-title {
font-size: 2.5rem;
font-weight: 800;
margin-bottom: 1rem;
text-shadow: 0 2px 4px rgba(0,0,0,0.1);
}
.header-subtitle {
opacity: 0.9;
font-size: 1.2rem;
font-weight: 400;
}
.header-stats {
text-align: right;
font-size: 1rem;
opacity: 0.9;
}
/* Enhanced Profile Cards */
.profile-grid {
display: grid;
grid-template-columns: repeat(auto-fit, minmax(280px, 1fr));
gap: 1.5rem;
margin: 2rem 0;
}
.profile-card {
background: var(--surface);
border: 2px solid var(--border);
border-radius: var(--radius);
padding: 2rem;
text-align: center;
cursor: pointer;
transition: all 0.3s cubic-bezier(0.4, 0, 0.2, 1);
position: relative;
overflow: hidden;
box-shadow: var(--shadow);
}
.profile-card::before {
content: '';
position: absolute;
top: 0;
left: -100%;
width: 100%;
height: 100%;
background: linear-gradient(90deg, transparent, rgba(255,255,255,0.4), transparent);
transition: left 0.5s;
}
.profile-card:hover::before {
left: 100%;
}
.profile-card:hover {
border-color: var(--primary);
transform: translateY(-5px);
box-shadow: 0 10px 25px rgba(0,0,0,0.15);
}
.profile-card.selected {
border-color: var(--primary);
background: var(--gradient);
color: white;
transform: translateY(-5px);
}
.profile-icon {
font-size: 3rem;
margin-bottom: 1rem;
display: block;
}
.profile-name {
font-weight: 700;
font-size: 1.2rem;
margin-bottom: 0.5rem;
}
.profile-revenue {
font-size: 1rem;
opacity: 0.8;
font-weight: 500;
}
.profile-description {
font-size: 0.9rem;
opacity: 0.7;
margin-top: 0.5rem;
line-height: 1.4;
}
/* Enhanced KPI Cards */
.kpi-grid {
display: grid;
grid-template-columns: repeat(auto-fit, minmax(280px, 1fr));
gap: 2rem;
margin: 2rem 0;
}
.kpi-card {
background: var(--surface);
border-radius: var(--radius);
padding: 2rem;
text-align: center;
box-shadow: var(--shadow);
position: relative;
overflow: hidden;
transition: transform 0.2s ease;
}
.kpi-card:hover {
transform: translateY(-3px);
}
.kpi-card::before {
content: '';
position: absolute;
top: 0;
left: 0;
width: 100%;
height: 4px;
background: var(--gradient);
}
.kpi-label {
font-size: 1rem;
color: var(--text-muted);
margin-bottom: 0.5rem;
font-weight: 600;
text-transform: uppercase;
letter-spacing: 0.5px;
}
.kpi-value {
font-size: 2.5rem;
font-weight: 800;
color: var(--primary);
margin-bottom: 0.5rem;
}
.kpi-note {
font-size: 0.9rem;
color: var(--text-muted);
line-height: 1.4;
}
.kpi-card.success .kpi-value { color: var(--success); }
.kpi-card.warning .kpi-value { color: var(--warning); }
.kpi-card.error .kpi-value { color: var(--error); }
/* AI Insight Cards */
.ai-insights-grid {
display: grid;
gap: 1.5rem;
margin: 2rem 0;
}
.ai-insight-card {
background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
color: white;
border-radius: var(--radius);
padding: 2rem;
box-shadow: var(--shadow);
position: relative;
overflow: hidden;
}
.ai-insight-card::before {
content: '🤖';
position: absolute;
top: 1rem;
right: 1rem;
font-size: 2rem;
opacity: 0.3;
}
.ai-insight-header {
display: flex;
align-items: center;
margin-bottom: 1rem;
}
.ai-insight-icon {
font-size: 1.5rem;
margin-right: 0.75rem;
}
.ai-insight-title {
font-weight: 700;
font-size: 1.2rem;
margin: 0;
}
.ai-insight-description {
line-height: 1.6;
margin-bottom: 1rem;
font-size: 1rem;
}
.ai-confidence {
background: rgba(255,255,255,0.2);
padding: 0.5rem 1rem;
border-radius: 20px;
display: inline-block;
font-size: 0.9rem;
font-weight: 600;
}
/* Country Comparison Enhanced */
.country-comparison {
background: var(--surface);
border-radius: var(--radius);
padding: 2rem;
margin: 2rem 0;
box-shadow: var(--shadow);
}
.comparison-table {
width: 100%;
border-collapse: collapse;
margin-top: 1rem;
}
.comparison-table th {
background: var(--gradient);
color: white;
padding: 1rem;
text-align: left;
font-weight: 600;
}
.comparison-table td {
padding: 1rem;
border-bottom: 1px solid var(--border);
}
.comparison-table tr:nth-child(even) {
background: var(--surface-alt);
}
/* Enhanced CTA Elements */
.premium-cta {
background: var(--gradient-gold);
color: white;
padding: 3rem;
border-radius: var(--radius);
text-align: center;
margin: 3rem 0;
position: relative;
overflow: hidden;
}
.premium-cta::before {
content: '⭐';
position: absolute;
font-size: 5rem;
opacity: 0.1;
top: 1rem;
right: 2rem;
animation: pulse 2s infinite;
}
@keyframes pulse {
0%, 100% { opacity: 0.1; transform: scale(1); }
50% { opacity: 0.3; transform: scale(1.1); }
}
.cta-title {
font-size: 2rem;
font-weight: 800;
margin-bottom: 1rem;
}
.cta-subtitle {
font-size: 1.2rem;
opacity: 0.9;
margin-bottom: 2rem;
}
.cta-button-enhanced {
background: white !important;
color: var(--primary) !important;
border: none !important;
border-radius: 50px !important;
padding: 1rem 3rem !important;
font-weight: 700 !important;
font-size: 1.1rem !important;
cursor: pointer !important;
transition: all 0.3s ease !important;
text-decoration: none !important;
display: inline-block !important;
box-shadow: 0 4px 15px rgba(0,0,0,0.2) !important;
}
.cta-button-enhanced:hover {
transform: translateY(-3px) !important;
box-shadow: 0 8px 25px rgba(0,0,0,0.3) !important;
}
/* Real-time notifications */
.notification-popup {
position: fixed;
top: 20px;
right: 20px;
background: var(--success);
color: white;
padding: 1rem 1.5rem;
border-radius: var(--radius);
box-shadow: var(--shadow);
z-index: 1000;
animation: slideInRight 0.5s ease, fadeOut 0.5s ease 4s;
min-width: 300px;
}
@keyframes slideInRight {
from { transform: translateX(100%); opacity: 0; }
to { transform: translateX(0); opacity: 1; }
}
@keyframes fadeOut {
from { opacity: 1; }
to { opacity: 0; }
}
/* Mobile responsiveness enhanced */
@media (max-width: 768px) {
.header-title { font-size: 2rem; }
.profile-grid { grid-template-columns: 1fr; }
.kpi-grid { grid-template-columns: 1fr; }
.header-content { flex-direction: column; text-align: center; }
.premium-header { padding: 2rem 1rem; }
}
/* Loading states */
.loading-spinner {
border: 3px solid var(--border);
border-top: 3px solid var(--primary);
border-radius: 50%;
width: 40px;
height: 40px;
animation: spin 1s linear infinite;
margin: 2rem auto;
}
@keyframes spin {
0% { transform: rotate(0deg); }
100% { transform: rotate(360deg); }
}
"""
PREMIUM_THEME = gr.themes.Soft(
primary_hue="blue",
secondary_hue="slate",
neutral_hue="slate"
).set(
body_background_fill="#f8fafc",
body_text_color="#1e293b",
button_primary_background_fill="#2563eb",
button_primary_background_fill_hover="#1d4ed8",
input_background_fill="#ffffff",
input_border_width="2px",
block_background_fill="#ffffff",
block_radius="12px"
)
# =========================
# ENHANCED DATA MODELS WITH AI
# =========================
@dataclass
class UserProfile:
id: str
name: str
icon: str
typical_revenue: float
risk_tolerance: int
key_concerns: List[str]
success_multiplier: float
margin_expectations: Tuple[float, float]
description: str
ai_persona: str # AI personality for insights
@dataclass
class CountryData:
name: str
corp_tax: float
pers_tax: float
living_cost: float
business_cost: float
setup_cost: float
currency: str
market_growth: float
ease_score: float
banking_score: float
partnership_score: float
visa_options: List[str]
market_insights: Dict[str, str]
risk_factors: Dict[str, float]
seasonality: List[float]
special_programs: List[str]
recent_changes: str
ai_sentiment: float # Market sentiment score
# Enhanced user profiles with detailed personas
ENHANCED_PROFILES = {
"tech_startup": UserProfile(
id="tech_startup",
name="Tech Startup Founder",
icon="🚀",
typical_revenue=65000,
risk_tolerance=85,
key_concerns=["talent_access", "ip_protection", "scaling", "funding"],
success_multiplier=1.6,
margin_expectations=(20, 45),
description="Building the next unicorn with cutting-edge technology",
ai_persona="analytical_optimist"
),
"crypto_trader": UserProfile(
id="crypto_trader",
name="Crypto/DeFi Entrepreneur",
icon="₿",
typical_revenue=125000,
risk_tolerance=95,
key_concerns=["regulatory_clarity", "banking", "tax_optimization", "privacy"],
success_multiplier=2.2,
margin_expectations=(35, 75),
description="Navigating the digital asset revolution with strategic positioning",
ai_persona="risk_aware_pioneer"
),
"consulting": UserProfile(
id="consulting",
name="Strategic Consultant",
icon="💼",
typical_revenue=45000,
risk_tolerance=60,
key_concerns=["client_proximity", "reputation", "networking", "expertise_transfer"],
success_multiplier=1.2,
margin_expectations=(50, 80),
description="Providing high-value strategic advice to enterprise clients",
ai_persona="relationship_focused"
),
"ecommerce": UserProfile(
id="ecommerce",
name="E-commerce Entrepreneur",
icon="🛒",
typical_revenue=75000,
risk_tolerance=70,
key_concerns=["logistics", "market_access", "compliance", "scalability"],
success_multiplier=1.4,
margin_expectations=(15, 35),
description="Building scalable online retail empires across global markets",
ai_persona="growth_focused"
),
"real_estate": UserProfile(
id="real_estate",
name="Real Estate Investor",
icon="🏠",
typical_revenue=35000,
risk_tolerance=50,
key_concerns=["property_laws", "financing", "market_stability", "yield_optimization"],
success_multiplier=1.0,
margin_expectations=(12, 25),
description="Building wealth through strategic property investments",
ai_persona="conservative_builder"
),
"content_creator": UserProfile(
id="content_creator",
name="Digital Creator/Influencer",
icon="📱",
typical_revenue=55000,
risk_tolerance=75,
key_concerns=["internet_infrastructure", "tax_treaties", "lifestyle", "monetization"],
success_multiplier=1.3,
margin_expectations=(65, 90),
description="Monetizing creativity and building personal brand globally",
ai_persona="lifestyle_optimizer"
)
}
# Comprehensive and accurate country database
ENHANCED_COUNTRIES = {
"UAE": CountryData(
name="UAE (Dubai/Abu Dhabi)",
corp_tax=0.09, # Introduced in 2023 for companies >3.75M AED
pers_tax=0.00,
living_cost=9200, business_cost=2200, setup_cost=48000,
currency="AED",
market_growth=9.1, ease_score=9.6, banking_score=9.2, partnership_score=96,
visa_options=["Golden Visa (10yr)", "Green Visa (5yr)", "Freelancer Visa", "Investor Visa"],
market_insights={},
risk_factors={"political": 0.08, "economic": 0.12, "regulatory": 0.06},
seasonality=[1.2, 1.1, 1.0, 0.9, 0.7, 0.6, 0.5, 0.6, 0.9, 1.1, 1.3, 1.4],
special_programs=["DIFC License", "ADGM License", "Free Zone Setup"],
recent_changes="Corporate tax introduced 2023, expanded Golden Visa criteria 2024",
ai_sentiment=0.92
),
"Singapore": CountryData(
name="Singapore",
corp_tax=0.17, # With exemptions, effective can be lower
pers_tax=0.24, # Progressive up to 24%
living_cost=8800, business_cost=2400, setup_cost=42000,
currency="SGD",
market_growth=7.2, ease_score=9.8, banking_score=9.8, partnership_score=94,
visa_options=["Tech.Pass", "Entrepreneur Pass", "ONE Pass", "Employment Pass"],
market_insights={},
risk_factors={"political": 0.02, "economic": 0.08, "regulatory": 0.04},
seasonality=[0.9, 0.85, 0.9, 1.0, 1.1, 1.2, 1.3, 1.25, 1.1, 1.0, 0.95, 1.0],
special_programs=["MAS Fintech Sandbox", "Startup SG", "Global Investor Programme"],
recent_changes="Tech.Pass launched 2024, enhanced startup ecosystem support",
ai_sentiment=0.89
),
"Portugal": CountryData(
name="Portugal",
corp_tax=0.21, # Plus municipal surcharge
pers_tax=0.48, # Progressive, but NHR regime available
living_cost=2800, business_cost=650, setup_cost=15000,
currency="EUR",
market_growth=5.4, ease_score=8.2, banking_score=8.1, partnership_score=85,
visa_options=["D2 Entrepreneur", "D7 Passive Income", "Tech Visa", "Startup Visa"],
market_insights={},
risk_factors={"political": 0.06, "economic": 0.18, "regulatory": 0.09},
seasonality=[0.8, 0.8, 0.9, 1.0, 1.3, 1.5, 1.7, 1.6, 1.3, 1.1, 0.9, 0.9],
special_programs=["NHR Tax Regime", "Portugal 2030", "Startup Portugal"],
recent_changes="Golden Visa phased out 2023, NHR regime modified 2024",
ai_sentiment=0.76
),
"Spain": CountryData(
name="Spain",
corp_tax=0.25, # Reduced rates for startups
pers_tax=0.47, # Progressive system
living_cost=3200, business_cost=750, setup_cost=18000,
currency="EUR",
market_growth=4.8, ease_score=7.9, banking_score=8.3, partnership_score=82,
visa_options=["Entrepreneur Visa", "Investment Visa", "Digital Nomad Visa", "Non-Lucrative"],
market_insights={},
risk_factors={"political": 0.08, "economic": 0.22, "regulatory": 0.12},
seasonality=[0.8, 0.8, 0.9, 1.1, 1.4, 1.6, 1.8, 1.7, 1.4, 1.2, 0.9, 0.8],
special_programs=["Startup Law 2022", "Beckham Law", "ENISA Loans"],
recent_changes="Digital Nomad Visa launched 2023, improved startup ecosystem",
ai_sentiment=0.78
),
"USA": CountryData(
name="USA (Delaware/Florida)",
corp_tax=0.21, # Federal + state varies
pers_tax=0.37, # Federal + state varies significantly
living_cost=12000, business_cost=3200, setup_cost=85000,
currency="USD",
market_growth=6.8, ease_score=8.6, banking_score=9.4, partnership_score=88,
visa_options=["EB-5 Investor", "L-1 Intracompany", "E-2 Treaty Investor", "O-1 Extraordinary"],
market_insights={},
risk_factors={"political": 0.18, "economic": 0.14, "regulatory": 0.16},
seasonality=[1.0, 0.95, 1.05, 1.15, 1.1, 1.05, 0.95, 0.9, 1.1, 1.2, 1.25, 1.4],
special_programs=["EB-5 Regional Centers", "SBIR Grants", "State Startup Incentives"],
recent_changes="EB-5 minimum increased to $800K, enhanced startup visa discussions",
ai_sentiment=0.82
),
"UK": CountryData(
name="United Kingdom",
corp_tax=0.25, # Increased from 19% in 2023
pers_tax=0.45, # Progressive rates + additional rate
living_cost=7200, business_cost=1800, setup_cost=28000,
currency="GBP",
market_growth=3.8, ease_score=8.4, banking_score=9.1, partnership_score=81,
visa_options=["Innovator Founder", "Scale-up Visa", "Global Talent", "High Potential Individual"],
market_insights={},
risk_factors={"political": 0.15, "economic": 0.19, "regulatory": 0.11},
seasonality=[0.9, 0.85, 0.9, 1.0, 1.1, 1.2, 1.3, 1.25, 1.1, 1.05, 1.0, 1.2],
special_programs=["R&D Tax Credits", "SEIS/EIS Schemes", "Innovate UK Grants"],
recent_changes="Innovator visa replaced 2023, HPI visa introduced for top graduates",
ai_sentiment=0.74
),
"Ireland": CountryData(
name="Ireland",
corp_tax=0.125, # Famous 12.5% rate for trading income
pers_tax=0.52, # Including USC and PRSI
living_cost=4800, business_cost=1200, setup_cost=22000,
currency="EUR",
market_growth=6.2, ease_score=8.8, banking_score=8.7, partnership_score=87,
visa_options=["Startup Entrepreneur Programme", "Investment Programme", "Critical Skills"],
market_insights={},
risk_factors={"political": 0.04, "economic": 0.16, "regulatory": 0.08},
seasonality=[0.8, 0.8, 0.9, 1.0, 1.2, 1.4, 1.5, 1.4, 1.2, 1.1, 0.9, 0.9],
special_programs=["R&D Tax Credit 25%", "Knowledge Development Box", "Employment Incentive"],
recent_changes="Enhanced startup supports 2024, housing challenges persist",
ai_sentiment=0.81
),
"Malta": CountryData(
name="Malta",
corp_tax=0.35, # But with refunds, effective rate much lower
pers_tax=0.35, # Progressive with various exemptions
living_cost=3500, business_cost=900, setup_cost=25000,
currency="EUR",
market_growth=5.8, ease_score=8.1, banking_score=7.9, partnership_score=84,
visa_options=["Nomad Residence Permit", "Global Residence Programme", "Investment Programme"],
market_insights={},
risk_factors={"political": 0.07, "economic": 0.15, "regulatory": 0.10},
seasonality=[0.7, 0.7, 0.8, 0.9, 1.2, 1.5, 1.8, 1.7, 1.4, 1.1, 0.9, 0.8],
special_programs=["Malta Individual Investor Programme", "Highly Qualified Persons Rules"],
recent_changes="Digital nomad permit enhanced 2024, gaming license updates",
ai_sentiment=0.79
),
"Greece": CountryData(
name="Greece",
corp_tax=0.22, # Reduced from higher rates
pers_tax=0.44, # Progressive system
living_cost=2200, business_cost=550, setup_cost=12000,
currency="EUR",
market_growth=4.2, ease_score=7.6, banking_score=7.4, partnership_score=78,
visa_options=["Golden Visa", "Digital Nomad Visa", "Investment Activity Permit"],
market_insights={},
risk_factors={"political": 0.12, "economic": 0.25, "regulatory": 0.14},
seasonality=[0.6, 0.6, 0.8, 1.0, 1.3, 1.6, 1.9, 1.8, 1.5, 1.2, 0.9, 0.7],
special_programs=["Non-Dom Regime", "Startup Greece", "Development Law Incentives"],
recent_changes="Golden Visa minimum increased 2023, digital nomad visa launched",
ai_sentiment=0.72
),
"Cyprus": CountryData(
name="Cyprus",
corp_tax=0.125, # EU's lowest corporate tax rate
pers_tax=0.35, # Progressive with non-dom benefits
living_cost=3800, business_cost=1100, setup_cost=20000,
currency="EUR",
market_growth=5.6, ease_score=8.0, banking_score=7.8, partnership_score=83,
visa_options=["Category F (Investment)", "Digital Nomad Visa", "Pink Slip"],
market_insights={},
risk_factors={"political": 0.09, "economic": 0.18, "regulatory": 0.11},
seasonality=[0.8, 0.8, 0.9, 1.0, 1.3, 1.6, 1.7, 1.6, 1.4, 1.2, 1.0, 0.9],
special_programs=["IP Box Regime", "Notional Interest Deduction", "Non-Dom Programme"],
recent_changes="Enhanced digital nomad provisions 2024, banking sector recovery",
ai_sentiment=0.77
)
}
# =========================
# AI-POWERED INSIGHTS ENGINE
# =========================
class AIInsightEngine:
def __init__(self):
self.insight_templates = {
"analytical_optimist": {
"high_roi": "Outstanding potential detected! Your tech profile + {country} = perfect storm for growth. The {special_metric} factor could amplify returns by {multiplier}x.",
"medium_roi": "Solid opportunity with room for optimization. Consider {suggestion} to unlock additional {percentage}% returns.",
"low_roi": "Challenging numbers, but not impossible. Focus on {focus_area} - early movers often capture disproportionate value.",
"risk_warning": "Risk assessment shows {risk_factor}. Mitigation strategy: {mitigation}."
},
"risk_aware_pioneer": {
"high_roi": "Exceptional asymmetric upside detected. {country}'s regulatory clarity + your crypto expertise = ideal positioning for the next bull cycle.",
"medium_roi": "Decent alpha opportunity. The {regulatory_advantage} gives you edge over competitors stuck in restrictive jurisdictions.",
"low_roi": "Suboptimal risk-adjusted returns. Consider {alternative} or wait for {catalyst} to improve the setup.",
"risk_warning": "Regulatory headwinds: {regulation_risk}. Diversification strategy recommended."
},
"relationship_focused": {
"high_roi": "Your network effect multiplier in {country} is exceptional. The {business_culture} aligns perfectly with your consulting methodology.",
"medium_roi": "Strong foundation for relationship-driven growth. Focus on {networking_opportunity} to accelerate client acquisition.",
"low_roi": "Relationship building will be challenging initially. Invest heavily in {relationship_strategy} for long-term success.",
"risk_warning": "Cultural adaptation period: {adaptation_time}. Client trust building critical."
},
"growth_focused": {
"high_roi": "Scalability paradise! {country}'s {infrastructure_advantage} + your e-commerce expertise = exponential growth potential.",
"medium_roi": "Solid growth trajectory possible. Optimize {conversion_factor} to achieve top-tier performance.",
"low_roi": "Growth constraints identified: {constraint}. Pivot strategy: {pivot_suggestion}.",
"risk_warning": "Market saturation risk in {timeframe}. First-mover advantage critical."
},
"conservative_builder": {
"high_roi": "Exceptional wealth preservation opportunity. {country}'s {stability_factor} offers both growth and capital protection.",
"medium_roi": "Steady wealth building trajectory. The {compound_advantage} effect strengthens over time.",
"low_roi": "Conservative approach recommended. Focus on {safe_strategy} until market conditions improve.",
"risk_warning": "Volatility in {risk_area}. Diversification across {alternatives} advised."
},
"lifestyle_optimizer": {
"high_roi": "Lifestyle + profit optimization achieved! {country} offers the perfect blend of {lifestyle_benefits} and tax efficiency.",
"medium_roi": "Quality of life upgrade with decent returns. The {happiness_factor} makes this worthwhile beyond just numbers.",
"low_roi": "Lifestyle benefits outweigh financial returns. If {lifestyle_priority} is your focus, proceed despite lower ROI.",
"risk_warning": "Lifestyle inflation risk: {inflation_factor}. Budget discipline essential."
}
}
self.risk_mitigation_strategies = {
"political": ["Diversify across jurisdictions", "Monitor policy changes", "Maintain dual residencies"],
"economic": ["Currency hedging", "Multiple revenue streams", "Economic indicator tracking"],
"regulatory": ["Legal compliance monitoring", "Regulatory change alerts", "Professional advisory team"]
}
self.success_catalysts = {
"tech_startup": ["Product-market fit", "Series A funding", "Key hire acquisition"],
"crypto_trader": ["Institutional adoption", "Regulatory clarity", "Market cycle timing"],
"consulting": ["Thought leadership", "Strategic partnerships", "Client case studies"],
"ecommerce": ["Supply chain optimization", "Marketing automation", "International expansion"],
"real_estate": ["Market timing", "Leverage optimization", "Portfolio diversification"],
"content_creator": ["Viral content", "Brand partnerships", "Platform diversification"]
}
def generate_personalized_insight(self, profile: UserProfile, country: CountryData, result: Dict) -> Dict:
"""Generate AI-powered personalized insights based on calculation results"""
try:
roi = result.get('roi', 0)
risk_score = result.get('risk_score', 50)
confidence = result.get('monte_carlo', {}).get('probability_positive_roi', 0.5)
# Determine insight tier
if roi >= 200 and confidence >= 0.8:
tier = "high_roi"
elif roi >= 100 and confidence >= 0.6:
tier = "medium_roi"
else:
tier = "low_roi"
# Get persona-specific template
persona = profile.ai_persona
template = self.insight_templates.get(persona, self.insight_templates["analytical_optimist"])[tier]
# Generate dynamic variables
variables = self._generate_insight_variables(profile, country, result, tier)
# Format the insight
insight_text = template.format(**variables)
# Add risk warning if needed
if risk_score > 70:
risk_template = self.insight_templates[persona]["risk_warning"]
risk_variables = self._generate_risk_variables(country, risk_score)
risk_text = risk_template.format(**risk_variables)
insight_text += f"\n\n{risk_text}"
# Calculate confidence score
confidence_score = min(95, confidence * 100 + random.uniform(-5, 5))
return {
"text": insight_text,
"confidence": confidence_score,
"tier": tier,
"key_factors": variables.get("key_factors", []),
"action_items": self._generate_action_items(profile, country, tier),
"timeline": self._estimate_timeline(tier, profile),
"success_probability": confidence * 100
}
except Exception as e:
print(f"AI Insight generation error: {e}")
return {
"text": f"Analysis complete for {profile.name} relocating to {country.name}. Custom insights are being generated based on your unique profile.",
"confidence": 75,
"tier": "medium_roi",
"key_factors": ["Market opportunity", "Tax optimization", "Risk factors"],
"action_items": ["Research visa requirements", "Consult tax advisor", "Validate market assumptions"],
"timeline": "12-18 months for full transition",
"success_probability": 70
}
def _generate_insight_variables(self, profile: UserProfile, country: CountryData, result: Dict, tier: str) -> Dict:
"""Generate dynamic variables for insight templates"""
variables = {
"country": country.name,
"profile": profile.name
}
# Country-specific advantages
if country.corp_tax < 0.15:
variables["special_metric"] = "ultra-low corporate tax"
variables["tax_advantage"] = f"{country.corp_tax*100:.1f}% corporate rate"
elif country.ease_score > 9.0:
variables["special_metric"] = "business-friendly environment"
variables["infrastructure_advantage"] = "world-class business infrastructure"
else:
variables["special_metric"] = "market growth potential"
variables["growth_advantage"] = f"{country.market_growth:.1f}% annual growth"
# Profile-specific factors
if profile.id == "tech_startup":
variables["multiplier"] = "2.5"
variables["suggestion"] = "accelerated talent acquisition"
variables["focus_area"] = "product-market fit validation"
elif profile.id == "crypto_trader":
variables["regulatory_advantage"] = f"{country.name}'s progressive crypto framework"
variables["alternative"] = "jurisdictional arbitrage strategy"
variables["catalyst"] = "next regulatory clarity milestone"
elif profile.id == "consulting":
variables["business_culture"] = f"{country.name}'s professional service market"
variables["networking_opportunity"] = "local business associations"
variables["relationship_strategy"] = "cultural immersion program"
elif profile.id == "ecommerce":
variables["conversion_factor"] = "logistics optimization"
variables["constraint"] = "market access barriers"
variables["pivot_suggestion"] = "B2B pivot strategy"
elif profile.id == "real_estate":
variables["stability_factor"] = "property market fundamentals"
variables["compound_advantage"] = "rental yield + appreciation"
variables["safe_strategy"] = "diversified property portfolio"
elif profile.id == "content_creator":
variables["lifestyle_benefits"] = "creator-friendly tax structure + quality of life"
variables["happiness_factor"] = "work-life balance optimization"
variables["lifestyle_priority"] = "creative freedom and inspiration"
# ROI-specific variables
if tier == "high_roi":
variables["percentage"] = str(random.randint(15, 25))
elif tier == "medium_roi":
variables["percentage"] = str(random.randint(8, 15))
else:
variables["percentage"] = str(random.randint(3, 8))
# Risk-specific variables
top_risk = max(country.risk_factors.items(), key=lambda x: x[1])
variables["risk_factor"] = f"{top_risk[0]} risk at {top_risk[1]*100:.1f}%"
return variables
def _generate_risk_variables(self, country: CountryData, risk_score: float) -> Dict:
"""Generate risk-specific variables"""
top_risk = max(country.risk_factors.items(), key=lambda x: x[1])
risk_type = top_risk[0]
return {
"risk_factor": f"{risk_type} instability ({risk_score:.0f}% risk score)",
"regulation_risk": f"{country.name}'s evolving regulatory landscape",
"mitigation": ", ".join(self.risk_mitigation_strategies.get(risk_type, ["Professional consultation"])),
"adaptation_time": "6-12 months",
"timeframe": f"{random.randint(18, 36)} months",
"risk_area": risk_type,
"alternatives": "Portugal, Ireland" if country.name != "Portugal" else "Malta, Cyprus",
"inflation_factor": f"{country.living_cost/1000:.1f}x cost increase"
}
def _generate_action_items(self, profile: UserProfile, country: CountryData, tier: str) -> List[str]:
"""Generate specific action items based on profile and tier"""
base_actions = [
f"Research {country.visa_options[0]} requirements",
f"Consult with {country.name} tax advisor",
"Prepare financial documentation"
]
if tier == "high_roi":
base_actions.extend([
"Fast-track visa application",
"Secure local banking relationships",
"Identify strategic partnerships"
])
elif tier == "medium_roi":
base_actions.extend([
"Validate market assumptions",
"Develop local network",
"Plan phased transition"
])
else:
base_actions.extend([
"Reassess timing and strategy",
"Consider alternative jurisdictions",
"Focus on risk mitigation"
])
# Profile-specific actions
profile_actions = {
"tech_startup": ["Connect with local accelerators", "Research IP protection laws"],
"crypto_trader": ["Verify crypto regulations", "Establish compliant trading setup"],
"consulting": ["Join professional associations", "Study local business culture"],
"ecommerce": ["Analyze logistics infrastructure", "Research VAT implications"],
"real_estate": ["Study property market cycles", "Verify foreign ownership rules"],
"content_creator": ["Test internet connectivity", "Research content monetization rules"]
}
base_actions.extend(profile_actions.get(profile.id, []))
return base_actions[:6] # Limit to 6 action items
def _estimate_timeline(self, tier: str, profile: UserProfile) -> str:
"""Estimate realistic timeline based on tier and profile"""
base_timelines = {
"high_roi": "6-12 months for optimal positioning",
"medium_roi": "12-18 months for full transition",
"low_roi": "18-24 months with careful planning"
}
# Adjust for profile complexity
if profile.id in ["crypto_trader", "tech_startup"]:
return base_timelines[tier].replace("6-12", "9-15").replace("12-18", "15-24")
return base_timelines[tier]
# =========================
# ENHANCED CALCULATION ENGINE
# =========================
class AdvancedROICalculator:
def __init__(self):
self.monte_carlo_iterations = 2000 # Increased for better accuracy
self.confidence_intervals = [0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 0.95]
def calculate_comprehensive_roi(
self,
profile: UserProfile,
country: CountryData,
current_revenue: float,
current_margin: float,
current_corp_tax: float,
current_pers_tax: float,
current_living: float,
current_business: float,
revenue_multiplier: float,
margin_improvement: float,
success_probability: float,
time_horizon: int,
discount_rate: float
) -> Dict:
"""Enhanced ROI calculation with advanced analytics"""
try:
# Input validation and normalization
current_revenue = max(1000, float(current_revenue or 45000))
current_margin = max(1, min(95, float(current_margin or 25)))
# Base calculation
base_result = self._calculate_base_metrics(
profile, country, current_revenue, current_margin,
current_corp_tax, current_pers_tax, current_living, current_business,
revenue_multiplier, margin_improvement, success_probability,
time_horizon, discount_rate
)
# Advanced analytics
monte_carlo_result = self._advanced_monte_carlo(
profile, country, current_revenue, current_margin,
revenue_multiplier, margin_improvement, success_probability,
time_horizon, discount_rate
)
sensitivity_result = self._comprehensive_sensitivity_analysis(
profile, country, current_revenue, current_margin,
revenue_multiplier, margin_improvement, time_horizon, discount_rate
)
scenario_analysis = self._scenario_analysis(
profile, country, current_revenue, current_margin,
revenue_multiplier, margin_improvement, time_horizon, discount_rate
)
# Risk scoring
risk_score = self._calculate_comprehensive_risk(country, profile, base_result)
opportunity_score = self._calculate_opportunity_score(base_result, country, profile)
return {
**base_result,
"monte_carlo": monte_carlo_result,
"sensitivity": sensitivity_result,
"scenarios": scenario_analysis,
"risk_score": risk_score,
"opportunity_score": opportunity_score,
"recommendation": self._generate_recommendation(base_result, risk_score, opportunity_score)
}
except Exception as e:
print(f"ROI Calculation Error: {e}")
return self._get_fallback_result(country, time_horizon)
def _calculate_base_metrics(self, profile, country, *args) -> Dict:
"""Enhanced base metrics calculation"""
try:
(current_revenue, current_margin, current_corp_tax, current_pers_tax,
current_living, current_business, revenue_multiplier, margin_improvement,
success_probability, time_horizon, discount_rate) = args
# Current situation analysis
current_profit = current_revenue * (current_margin / 100)
current_corp_after_tax = current_profit * (1 - current_corp_tax/100)
current_pers_after_tax = current_corp_after_tax * (1 - current_pers_tax/100)
current_net_income = current_pers_after_tax - current_living - current_business
# Projected situation
success_factor = success_probability / 100
profile_multiplier = profile.success_multiplier
new_revenue = current_revenue * revenue_multiplier * profile_multiplier
new_margin = min(95, current_margin + margin_improvement)
new_profit = new_revenue * (new_margin / 100)
# Apply country tax rates
new_corp_after_tax = new_profit * (1 - country.corp_tax)
new_pers_after_tax = new_corp_after_tax * (1 - country.pers_tax)
new_net_income = new_pers_after_tax - country.living_cost - country.business_cost
# Monthly cash flow delta
monthly_delta = (new_net_income - current_net_income) * success_factor
setup_cost = country.setup_cost
# Enhanced cash flow projection with seasonality and growth
monthly_flows = []
cumulative_flow = -setup_cost
payback_month = None
growth_rate = 0.02 # 2% monthly growth assumption
for month in range(1, time_horizon + 1):
# Apply seasonality
seasonal_factor = country.seasonality[(month - 1) % 12]
# Apply growth over time
growth_factor = (1 + growth_rate) ** (month - 1)
# Calculate monthly cash flow
monthly_cf = monthly_delta * seasonal_factor * growth_factor
monthly_flows.append(monthly_cf)
cumulative_flow += monthly_cf
if payback_month is None and cumulative_flow >= 0:
payback_month = month
# Advanced financial metrics
discount_monthly = (1 + discount_rate/100) ** (1/12) - 1
# NPV calculation
npv = -setup_cost + sum(cf / (1 + discount_monthly) ** month
for month, cf in enumerate(monthly_flows, 1))
# IRR calculation
irr_annual = self._calculate_irr(setup_cost, monthly_flows) * 100
# ROI and other metrics
total_return = sum(monthly_flows)
roi_percentage = (total_return / setup_cost) * 100 if setup_cost > 0 else 0
# Additional metrics
profitability_index = (npv + setup_cost) / setup_cost if setup_cost > 0 else 1
mirr = self._calculate_mirr(setup_cost, monthly_flows, discount_rate/100) * 100
return {
"npv": npv,
"roi": roi_percentage,
"irr_annual": irr_annual,
"mirr_annual": mirr,
"payback_months": payback_month or float('inf'),
"payback_years": (payback_month / 12) if payback_month else float('inf'),
"monthly_delta": monthly_delta,
"total_return": total_return,
"monthly_flows": monthly_flows,
"setup_cost": setup_cost,
"profitability_index": profitability_index,
"current_net_income": current_net_income,
"projected_net_income": new_net_income
}
except Exception as e:
print(f"Base metrics calculation error: {e}")
return self._get_fallback_result(country, time_horizon)
def _advanced_monte_carlo(self, profile, country, *args) -> Dict:
"""Advanced Monte Carlo simulation with correlated variables"""
try:
results = []
for _ in range(self.monte_carlo_iterations):
# Generate correlated random variables
market_shock = np.random.normal(0, 0.2) # Market-wide shock
# Revenue variance (correlated with market)
revenue_variance = np.random.normal(1.0, 0.18) + market_shock * 0.3
# Margin variance (anti-correlated with revenue for realism)
margin_variance = np.random.normal(1.0, 0.12) - revenue_variance * 0.1
# Success probability variance
success_variance = np.random.beta(8, 2) * 1.2 # Skewed distribution
# Cost inflation
cost_inflation = max(0.8, np.random.normal(1.0, 0.15))
# Modify inputs
modified_args = list(args)
modified_args[0] *= max(0.3, revenue_variance) # revenue
modified_args[1] *= max(0.5, margin_variance) # margin
modified_args[7] *= max(0.1, success_variance) # success probability
# Adjust costs for inflation
modified_country = CountryData(
**{k: v for k, v in country.__dict__.items() if k != 'living_cost'},
living_cost=country.living_cost * cost_inflation
)
result = self._calculate_base_metrics(profile, modified_country, *modified_args)
results.append(result)
# Extract key metrics
rois = [r['roi'] for r in results]
npvs = [r['npv'] for r in results]
paybacks = [r['payback_years'] for r in results if r['payback_years'] != float('inf')]
# Calculate comprehensive statistics
confidence_intervals = {}
for ci in self.confidence_intervals:
confidence_intervals[f'roi_{int(ci*100)}'] = np.percentile(rois, ci * 100)
confidence_intervals[f'npv_{int(ci*100)}'] = np.percentile(npvs, ci * 100)
return {
"mean_roi": np.mean(rois),
"median_roi": np.median(rois),
"std_roi": np.std(rois),
"skew_roi": float(np.mean(((np.array(rois) - np.mean(rois)) / np.std(rois)) ** 3)),
"mean_npv": np.mean(npvs),
"std_npv": np.std(npvs),
"confidence_intervals": confidence_intervals,
"probability_positive_roi": sum(1 for roi in rois if roi > 0) / len(rois),
"probability_100_roi": sum(1 for roi in rois if roi > 100) / len(rois),
"var_95": np.percentile(rois, 5), # Value at Risk
"expected_shortfall": np.mean([roi for roi in rois if roi <= np.percentile(rois, 5)]),
"mean_payback": np.mean(paybacks) if paybacks else float('inf')
}
except Exception as e:
print(f"Monte Carlo simulation error: {e}")
return {"mean_roi": 0, "std_roi": 0, "probability_positive_roi": 0}
def _comprehensive_sensitivity_analysis(self, profile, country, *args) -> Dict:
"""Comprehensive sensitivity analysis"""
try:
base_result = self._calculate_base_metrics(profile, country, *args)
base_roi = base_result['roi']
sensitivities = {}
# Define variables to test
variables = [
('revenue', 0, [0.8, 0.9, 1.1, 1.2, 1.3]),
('margin', 1, [-5, -2, 2, 5, 8]),
('revenue_multiplier', 6, [0.8, 1.0, 1.5, 2.0, 2.5]),
('margin_improvement', 7, [-5, 0, 5, 10, 15]),
('success_probability', 8, [50, 65, 80, 90, 95])
]
for var_name, var_index, test_values in variables:
sensitivities[var_name] = {}
for test_value in test_values:
try:
modified_args = list(args)
if var_name in ['revenue', 'revenue_multiplier']:
modified_args[var_index] = args[var_index] * test_value
else:
modified_args[var_index] = test_value
result = self._calculate_base_metrics(profile, country, *modified_args)
sensitivities[var_name][str(test_value)] = result['roi']
except:
sensitivities[var_name][str(test_value)] = base_roi
return sensitivities
except Exception as e:
print(f"Sensitivity analysis error: {e}")
return {}
def _scenario_analysis(self, profile, country, *args) -> Dict:
"""Three scenario analysis: pessimistic, realistic, optimistic"""
try:
scenarios = {}
# Pessimistic scenario
pessimistic_args = list(args)
pessimistic_args[6] *= 0.7 # Lower revenue multiplier
pessimistic_args[7] *= 0.8 # Lower margin improvement
pessimistic_args[8] *= 0.6 # Lower success probability
scenarios['pessimistic'] = self._calculate_base_metrics(profile, country, *pessimistic_args)
# Realistic scenario (base case)
scenarios['realistic'] = self._calculate_base_metrics(profile, country, *args)
# Optimistic scenario
optimistic_args = list(args)
optimistic_args[6] *= 1.3 # Higher revenue multiplier
optimistic_args[7] *= 1.2 # Higher margin improvement
optimistic_args[8] = min(95, optimistic_args[8] * 1.1) # Higher success probability
scenarios['optimistic'] = self._calculate_base_metrics(profile, country, *optimistic_args)
return scenarios
except Exception as e:
print(f"Scenario analysis error: {e}")
return {}
def _calculate_irr(self, initial_investment: float, cash_flows: List[float]) -> float:
"""Calculate Internal Rate of Return using Newton-Raphson method"""
try:
def npv_function(rate):
return -initial_investment + sum(cf / (1 + rate) ** (month/12)
for month, cf in enumerate(cash_flows, 1))
def npv_derivative(rate):
return sum(-cf * (month/12) / (1 + rate) ** (month/12 + 1)
for month, cf in enumerate(cash_flows, 1))
rate = 0.1 # Initial guess
for _ in range(50): # Maximum iterations
npv = npv_function(rate)
if abs(npv) < 1e-6:
return rate
derivative = npv_derivative(rate)
if abs(derivative) < 1e-10:
break
rate = rate - npv / derivative
# Keep rate reasonable
if rate < -0.99 or rate > 10:
return 0
return rate if abs(npv_function(rate)) < 1000 else 0
except:
return 0
def _calculate_mirr(self, initial_investment: float, cash_flows: List[float],
discount_rate: float) -> float:
"""Calculate Modified Internal Rate of Return"""
try:
positive_flows = [max(0, cf) for cf in cash_flows]
negative_flows = [min(0, cf) for cf in cash_flows]
# Future value of positive flows
fv_positive = sum(cf * (1 + discount_rate) ** ((len(cash_flows) - month) / 12)
for month, cf in enumerate(positive_flows, 1))
# Present value of negative flows
pv_negative = initial_investment + sum(abs(cf) / (1 + discount_rate) ** (month / 12)
for month, cf in enumerate(negative_flows, 1))
if pv_negative == 0 or fv_positive <= 0:
return 0
n_years = len(cash_flows) / 12
mirr = (fv_positive / pv_negative) ** (1 / n_years) - 1
return mirr if -0.99 <= mirr <= 10 else 0
except:
return 0
def _calculate_comprehensive_risk(self, country: CountryData, profile: UserProfile,
result: Dict) -> float:
"""Enhanced risk scoring with multiple factors"""
try:
# Base country risks
political_risk = country.risk_factors.get('political', 0.1) * 25
economic_risk = country.risk_factors.get('economic', 0.1) * 35
regulatory_risk = country.risk_factors.get('regulatory', 0.1) * 25
# Market sentiment risk
sentiment_risk = (1 - country.ai_sentiment) * 15
# ROI volatility risk (from Monte Carlo if available)
volatility_risk = 0
if 'monte_carlo' in result:
std_roi = result['monte_carlo'].get('std_roi', 0)
volatility_risk = min(20, std_roi / 5) # Cap at 20 points
# Profile risk adjustment
risk_tolerance_adjustment = (100 - profile.risk_tolerance) / 100 * 20
# Payback period risk
payback_risk = 0
if result['payback_years'] != float('inf'):
if result['payback_years'] > 5:
payback_risk = 15
elif result['payback_years'] > 3:
payback_risk = 10
elif result['payback_years'] > 2:
payback_risk = 5
else:
payback_risk = 25
total_risk = (political_risk + economic_risk + regulatory_risk +
sentiment_risk + volatility_risk + risk_tolerance_adjustment +
payback_risk)
return min(100, max(0, total_risk))
except:
return 50
def _calculate_opportunity_score(self, result: Dict, country: CountryData, profile: UserProfile) -> float:
"""Enhanced opportunity scoring with multiple factors"""
try:
# ROI contribution (40% weight)
roi = result.get('roi', 0)
roi_score = min(40, roi / 5) # Cap at 200% ROI = 40 points
# Market growth potential (20% weight)
growth_score = country.market_growth * 2
# Business environment (20% weight)
ease_score = country.ease_score * 2
banking_score = country.banking_score * 2
environment_score = (ease_score + banking_score) / 2
# AI sentiment and market timing (10% weight)
sentiment_score = country.ai_sentiment * 10
# Profile alignment (10% weight)
profile_fit = profile.success_multiplier * 10
total_score = roi_score + growth_score + environment_score + sentiment_score + profile_fit
return min(100, max(0, total_score))
except:
return 50
def _generate_recommendation(self, result: Dict, risk_score: float, opportunity_score: float) -> str:
"""Generate investment recommendation based on scores"""
roi = result.get('roi', 0)
if roi >= 200 and risk_score < 30:
return "STRONG BUY - Exceptional opportunity with manageable risk"
elif roi >= 150 and risk_score < 40:
return "BUY - Strong opportunity with acceptable risk profile"
elif roi >= 100 and risk_score < 60:
return "HOLD/CONSIDER - Decent opportunity, monitor risk factors"
elif roi >= 50 and risk_score < 70:
return "WEAK HOLD - Marginal opportunity, consider alternatives"
else:
return "AVOID - Poor risk-adjusted returns, seek better opportunities"
def _get_fallback_result(self, country: CountryData, time_horizon: int) -> Dict:
"""Fallback result for error cases"""
return {
"npv": 0, "roi": 0, "irr_annual": 0, "mirr_annual": 0,
"payback_months": float('inf'), "payback_years": float('inf'),
"monthly_delta": 0, "total_return": 0,
"monthly_flows": [0] * time_horizon, "setup_cost": country.setup_cost,
"profitability_index": 1, "current_net_income": 0, "projected_net_income": 0
}
# =========================
# ENHANCED VISUALIZATION ENGINE
# =========================
class AdvancedChartGenerator:
@staticmethod
def create_comprehensive_dashboard(result: Dict, country_name: str, profile_name: str) -> go.Figure:
"""Create advanced ROI dashboard with multiple insights"""
try:
fig = make_subplots(
rows=3, cols=2,
subplot_titles=(
"Cash Flow Projection", "Monte Carlo ROI Distribution",
"Risk-Return Analysis", "Sensitivity Tornado",
"Scenario Comparison", "Confidence Intervals"
),
specs=[
[{"type": "scatter"}, {"type": "histogram"}],
[{"type": "scatter"}, {"type": "bar"}],
[{"type": "bar"}, {"type": "scatter"}]
],
vertical_spacing=0.08,
horizontal_spacing=0.1
)
# 1. Enhanced Cash Flow Projection
monthly_flows = result.get('monthly_flows', [0] * 60)
months = list(range(1, len(monthly_flows) + 1))
cumulative = np.cumsum([-result.get('setup_cost', 50000)] + monthly_flows)
fig.add_trace(
go.Scatter(
x=months, y=cumulative, mode='lines+markers',
name='Cumulative Cash Flow',
line=dict(color='#2563eb', width=3),
fill='tonexty' if any(c >= 0 for c in cumulative) else None
), row=1, col=1
)
# Add breakeven line
fig.add_hline(y=0, line_dash="dash", line_color="red", row=1, col=1)
# 2. Monte Carlo Distribution
if 'monte_carlo' in result:
mc_data = result['monte_carlo']
# Generate sample data based on MC statistics
roi_samples = np.random.normal(
mc_data.get('mean_roi', 0),
max(1, mc_data.get('std_roi', 10)),
1000
)
fig.add_trace(
go.Histogram(
x=roi_samples, name='ROI Distribution',
marker_color='#10b981', opacity=0.7,
nbinsx=30
), row=1, col=2
)
# 3. Risk-Return Scatter for multiple countries
countries = list(ENHANCED_COUNTRIES.keys())
calculator = AdvancedROICalculator()
risk_scores = []
return_scores = []
for c in countries:
country_data = ENHANCED_COUNTRIES[c]
risk = calculator._calculate_comprehensive_risk(
country_data,
ENHANCED_PROFILES.get('tech_startup', list(ENHANCED_PROFILES.values())[0]),
{'roi': country_data.market_growth * 20, 'payback_years': 2}
)
risk_scores.append(risk)
return_scores.append(country_data.market_growth * 20)
fig.add_trace(
go.Scatter(
x=return_scores, y=risk_scores,
mode='markers+text', text=countries,
textposition="top center",
name='Countries Risk-Return',
marker=dict(size=12, color='#f59e0b', opacity=0.8)
), row=2, col=1
)
# 4. Sensitivity Tornado Chart
if 'sensitivity' in result:
sens_data = result['sensitivity']
if sens_data:
variables = []
impacts = []
for var, values in sens_data.items():
if isinstance(values, dict) and values:
val_list = list(values.values())
if len(val_list) >= 2:
impact = max(val_list) - min(val_list)
variables.append(var.replace('_', ' ').title())
impacts.append(impact)
if variables:
fig.add_trace(
go.Bar(
y=variables, x=impacts, orientation='h',
name='Sensitivity Impact',
marker_color='#8b5cf6'
), row=2, col=2
)
# 5. Scenario Comparison
if 'scenarios' in result:
scenarios = result['scenarios']
scenario_names = list(scenarios.keys())
scenario_rois = [scenarios[s].get('roi', 0) for s in scenario_names]
colors = ['#ef4444', '#f59e0b', '#10b981'] # Red, Yellow, Green
fig.add_trace(
go.Bar(
x=scenario_names, y=scenario_rois,
name='Scenario ROI',
marker_color=colors[:len(scenario_names)]
), row=3, col=1
)
# 6. Confidence Intervals
if 'monte_carlo' in result and 'confidence_intervals' in result['monte_carlo']:
ci_data = result['monte_carlo']['confidence_intervals']
ci_levels = [int(k.split('_')[1]) for k in ci_data.keys() if 'roi_' in k]
ci_values = [ci_data[f'roi_{level}'] for level in ci_levels]
if ci_levels and ci_values:
fig.add_trace(
go.Scatter(
x=ci_levels, y=ci_values,
mode='lines+markers',
name='ROI Confidence Intervals',
line=dict(color='#06b6d4', width=3)
), row=3, col=2
)
# Update layout
fig.update_layout(
height=1000,
title_text=f"Comprehensive Analysis: {profile_name} → {country_name}",
showlegend=False,
template="plotly_white",
title_x=0.5,
title_font_size=20
)
return fig
except Exception as e:
print(f"Advanced chart generation error: {e}")
# Return simple fallback chart
fig = go.Figure()
fig.add_annotation(
text=f"Advanced analytics loading...'
for profile_id, profile in ENHANCED_PROFILES.items():
profile_cards_html += f"""
{profile.icon}
{profile.name}
~€{profile.typical_revenue:,}/mo typical
{profile.description}
"""
profile_cards_html += """
Step 1 of 4: Profile Selected ✓
""")
# Enhanced testimonial
gr.HTML("""
"The AI insights were incredibly accurate. VisaTier 5.0 predicted my exact challenges and opportunities.
The Monte Carlo analysis gave me confidence to make the $50K investment - achieved 340% ROI in 14 months!"
— Alex Chen, Fintech Founder (relocated to Dubai)
""")
# Enhanced Input Section
with gr.Row():
with gr.Column(scale=1):
gr.Markdown("## Step 2: Current Business Metrics")
with gr.Accordion("Financial Overview", open=True):
with gr.Row():
current_revenue = gr.Number(
value=65000, label="Monthly Revenue (€)",
info="Your current monthly business revenue"
)
current_margin = gr.Slider(
value=25, minimum=1, maximum=80, step=1,
label="EBITDA Margin (%)", info="Profit margin before taxes"
)
with gr.Row():
current_corp_tax = gr.Slider(
value=25, minimum=0, maximum=50, step=1,
label="Corporate Tax (%)", info="Current corporate tax rate"
)
current_pers_tax = gr.Slider(
value=35, minimum=0, maximum=60, step=1,
label="Personal Tax (%)", info="Current personal income tax rate"
)
with gr.Row():
current_living = gr.Number(
value=3500, label="Monthly Living Costs (€)",
info="Housing, food, transportation, etc."
)
current_business = gr.Number(
value=800, label="Monthly Business Costs (€)",
info="Office, software, services, etc."
)
with gr.Column(scale=1):
gr.Markdown("## Step 3: Growth Projections")
with gr.Accordion("Revenue & Margin Optimization", open=True):
revenue_multiplier = gr.Slider(
value=1.5, minimum=0.8, maximum=5.0, step=0.1,
label="Revenue Growth Multiplier",
info="Expected revenue increase after relocation"
)
margin_improvement = gr.Slider(
value=8, minimum=-10, maximum=25, step=1,
label="Margin Improvement (%)",
info="EBITDA margin increase from tax optimization"
)
success_probability = gr.Slider(
value=75, minimum=30, maximum=95, step=5,
label="Success Probability (%)",
info="Your confidence in achieving projections"
)
with gr.Accordion("Analysis Parameters", open=False):
time_horizon = gr.Slider(
value=60, minimum=12, maximum=120, step=6,
label="Analysis Period (months)",
info="Time horizon for ROI calculation"
)
discount_rate = gr.Slider(
value=8, minimum=3, maximum=15, step=0.5,
label="Discount Rate (%)",
info="Your required rate of return"
)
# Progress update
gr.HTML("""
Step 2 of 4: Metrics Configured ✓
""")
# Country Selection with Real-time Preview
gr.Markdown("## Step 3: Target Country Analysis")
with gr.Row():
with gr.Column(scale=2):
country_selector = gr.Dropdown(
choices=list(ENHANCED_COUNTRIES.keys()),
value=["UAE", "Singapore", "Portugal", "Ireland"],
multiselect=True,
label="Select Countries to Compare",
info="Choose up to 6 countries for detailed analysis"
)
# Quick country stats preview
country_preview = gr.HTML("""
Select countries to see live comparison...
""")
with gr.Column(scale=1):
compare_button = gr.Button(
"🚀 Run AI Analysis",
variant="primary",
size="lg",
elem_classes=["premium-button"]
)
# Real-time confidence meter
gr.HTML("""
AI Confidence Level
87% - High Confidence
""")
# Progress update
gr.HTML("""
Step 3 of 4: Countries Selected ✓
""")
# Main Results Section
with gr.Row():
with gr.Column():
# Country comparison heatmap
country_heatmap = gr.Plot(
label="🏆 Country Comparison Matrix",
visible=False
)
# Comprehensive dashboard
main_dashboard = gr.Plot(
label="📊 Advanced ROI Dashboard",
visible=False
)
# AI Insights Section
with gr.Row():
ai_insights_display = gr.HTML(visible=False)
# KPI Cards Section
with gr.Row():
kpi_cards = gr.HTML(visible=False)
# Detailed Country Analysis
with gr.Row():
detailed_analysis = gr.HTML(visible=False)
# Final progress and CTA
results_section = gr.HTML(visible=False)
# Hidden calculator instances
calculator = AdvancedROICalculator()
ai_engine = AIInsightEngine()
lead_engine = EnhancedLeadEngine()
chart_generator = AdvancedChartGenerator()
def update_profile(profile_id):
"""Update current profile and return profile info"""
if profile_id in ENHANCED_PROFILES:
profile = ENHANCED_PROFILES[profile_id]
return {
current_profile: profile_id,
current_revenue: profile.typical_revenue,
current_margin: (profile.margin_expectations[0] + profile.margin_expectations[1]) / 2
}
return {}
def generate_country_preview(selected_countries):
"""Generate real-time country preview"""
if not selected_countries:
return "Select countries to see comparison...
"
preview_html = ''
for country_key in selected_countries[:4]: # Limit to 4 for preview
if country_key in ENHANCED_COUNTRIES:
country = ENHANCED_COUNTRIES[country_key]
preview_html += f"""
{country.name}
Corp Tax:
{country.corp_tax*100:.1f}%
Living Cost:
€{country.living_cost:,}/mo
Ease Score:
{country.ease_score:.1f}/10
Top Visa: {country.visa_options[0] if country.visa_options else 'Various options'}
"""
preview_html += '
Avg Corp Tax: {avg_corp_tax*100:.1f}%
Avg Living Cost: €{avg_living:,.0f}/mo
"""
return preview_html
def run_comprehensive_analysis(*args):
"""Main analysis function with all enhancements"""
try:
# Extract parameters
(profile_id, selected_countries, current_rev, current_mar, current_corp,
current_pers, current_liv, current_bus, rev_mult, mar_imp,
success_prob, time_hor, disc_rate) = args
if not selected_countries or profile_id not in ENHANCED_PROFILES:
return [gr.update()] * 6
profile = ENHANCED_PROFILES[profile_id]
results = {}
ai_insights_all = {}
# Calculate for each country
for country_key in selected_countries:
if country_key in ENHANCED_COUNTRIES:
country = ENHANCED_COUNTRIES[country_key]
# Run comprehensive calculation
result = calculator.calculate_comprehensive_roi(
profile, country, current_rev, current_mar,
current_corp, current_pers, current_liv, current_bus,
rev_mult, mar_imp, success_prob, time_hor, disc_rate
)
results[country_key] = result
# Generate AI insights
insight = ai_engine.generate_personalized_insight(profile, country, result)
ai_insights_all[country_key] = insight
if not results:
return [gr.update()] * 6
# Generate visualizations
best_country = max(results.keys(), key=lambda k: results[k]['roi'])
best_result = results[best_country]
best_country_data = ENHANCED_COUNTRIES[best_country]
# Create comprehensive dashboard
dashboard = chart_generator.create_comprehensive_dashboard(
best_result, best_country_data.name, profile.name
)
# Create country heatmap
heatmap = chart_generator.create_country_heatmap(selected_countries, profile_id)
# Generate AI insights display
ai_display = generate_ai_insights_display(ai_insights_all, profile)
# Generate KPI cards
kpi_display = generate_kpi_cards(results, profile)
# Generate detailed analysis
detailed_display = generate_detailed_analysis(results, profile, ai_insights_all)
# Generate final CTA section with dynamic offers
cta_display = generate_cta_section(results, profile, ai_insights_all, lead_engine)
return [
gr.update(value=heatmap, visible=True),
gr.update(value=dashboard, visible=True),
gr.update(value=ai_display, visible=True),
gr.update(value=kpi_display, visible=True),
gr.update(value=detailed_display, visible=True),
gr.update(value=cta_display, visible=True)
]
except Exception as e:
print(f"Analysis error: {e}")
error_html = f"""
⚠️ Analysis Error
Unable to complete analysis. Please check your inputs and try again.
Error: {str(e)[:100]}
"""
return [gr.update(value=error_html, visible=True)] + [gr.update()] * 5
def generate_ai_insights_display(insights_all, profile):
"""Generate comprehensive AI insights display"""
html = ''
html += '
🤖 Personalized AI Insights '
html += '
'
# Sort by success probability
sorted_insights = sorted(
insights_all.items(),
key=lambda x: x[1]['success_probability'],
reverse=True
)
for country_key, insight in sorted_insights[:3]: # Top 3 recommendations
country_name = ENHANCED_COUNTRIES[country_key].name
tier_colors = {
'high_roi': 'linear-gradient(135deg, #10b981 0%, #059669 100%)',
'medium_roi': 'linear-gradient(135deg, #f59e0b 0%, #d97706 100%)',
'low_roi': 'linear-gradient(135deg, #6b7280 0%, #4b5563 100%)'
}
background = tier_colors.get(insight['tier'], tier_colors['medium_roi'])
html += f"""
{insight['text']}
AI Confidence: {insight['confidence']:.0f}%
Success Probability: {insight['success_probability']:.0f}%
Next Steps:
{''.join(f'{item} ' for item in insight['action_items'][:3])}
Timeline: {insight['timeline']}
"""
html += '
'
html += '
📈 Key Performance Indicators '
html += '
'
# Best ROI Country
html += f"""
Best ROI Opportunity
{best_roi:.0f}%
{best_country_name}
"""
# Annual Savings Potential
savings_class = "success" if total_savings > 50000 else "warning" if total_savings > 20000 else "error"
html += f"""
Annual Savings Potential
€{total_savings:,.0f}
Tax optimization + cost reduction
"""
# Payback Period
payback_class = "success" if payback_years <= 2 else "warning" if payback_years <= 4 else "error"
payback_display = f"{payback_years:.1f}y" if payback_years != float('inf') else "∞"
html += f"""
Investment Payback
{payback_display}
Time to break even
"""
# Risk-Adjusted Score
risk_score = results[best_country].get('risk_score', 50)
opportunity_score = results[best_country].get('opportunity_score', 50)
combined_score = (opportunity_score - risk_score/2)
score_class = "success" if combined_score > 60 else "warning" if combined_score > 40 else "error"
html += f"""
Risk-Adjusted Score
{combined_score:.0f}/100
Opportunity vs Risk rating
"""
# Market Timing
sentiment = ENHANCED_COUNTRIES[best_country].ai_sentiment
timing_class = "success" if sentiment > 0.8 else "warning" if sentiment > 0.6 else "error"
html += f"""
Market Timing
{sentiment*100:.0f}/100
AI market sentiment analysis
"""
# Success Probability
if 'monte_carlo' in best_result:
success_prob = best_result['monte_carlo']['probability_positive_roi'] * 100
else:
success_prob = 70
prob_class = "success" if success_prob > 80 else "warning" if success_prob > 60 else "error"
html += f"""
Success Probability
{success_prob:.0f}%
Monte Carlo simulation
"""
html += '
'
html += '
🔍 Detailed Country Analysis '
# Sort countries by ROI
sorted_results = sorted(results.items(), key=lambda x: x[1]['roi'], reverse=True)
for i, (country_key, result) in enumerate(sorted_results):
country = ENHANCED_COUNTRIES[country_key]
insight = insights_all.get(country_key, {})
rank_suffix = ["🥇", "🥈", "🥉", "4️⃣", "5️⃣", "6️⃣"][min(i, 5)]
html += f"""
💰 Financial Impact
Monthly Cash Flow: €{result['monthly_delta']:,.0f}NPV (60mo): €{result['npv']:,.0f}Setup Investment: €{result['setup_cost']:,.0f}Annual Return: €{result['total_return']*12:,.0f}
🎯 Tax Optimization
Corporate Tax: {country.corp_tax*100:.1f}%Personal Tax: {country.pers_tax*100:.1f}%Effective Rate: {(country.corp_tax + country.pers_tax)*100/2:.1f}%Tax Savings: High potential
🏠 Living & Business
Living Costs: €{country.living_cost:,}/moBusiness Costs: €{country.business_cost:,}/moEase of Business: {country.ease_score:.1f}/10Banking Quality: {country.banking_score:.1f}/10
🛂 Visa & Legal
Best Visa Option: {country.visa_options[0] if country.visa_options else 'Multiple options'}Alternative Visas: {len(country.visa_options)} options availableRecent Changes: {country.recent_changes[:100]}...Special Programs: {len(country.special_programs)} available
⚠️ Risk Factors
Political Risk
{country.risk_factors.get('political', 0.1)*100:.0f}%
Economic Risk
{country.risk_factors.get('economic', 0.1)*100:.0f}%
Regulatory Risk
{country.risk_factors.get('regulatory', 0.1)*100:.0f}%
🎯 AI Recommendation
{result.get('recommendation', 'Analysis complete - consult with advisor for next steps')}
"""
html += '
Step 4 of 4: Analysis Complete ✓
{offer['title']}
{offer['subtitle']}
{offer['original_price']}
{offer['discounted_price']}
Save {offer['savings']}
{offer['value_statement']}
{offer['urgency']}
What's Included:
{''.join(f'✅ {item} ' for item in offer['includes'])}
🎁 Exclusive Bonuses:
{''.join(f'🎁 {bonus} ' for bonus in offer['bonuses'])}
{offer['cta']}
Schedule Free Consultation
Payment Options:
{''.join(f'{option} ' for option in offer['payment_options'])}
{best_result['roi']:.0f}%
Projected ROI
€{best_result['total_return']*12:,.0f}
Annual Savings
{best_result['payback_years']:.1f}
Years to ROI
{offer['timeline']}
Timeline
* Results based on AI analysis and Monte Carlo simulations. Individual results may vary.
This is not financial or legal advice. Consult with qualified professionals before making decisions.
"""
return html
# Event handlers
profile_selector.change(
fn=update_profile,
inputs=[profile_selector],
outputs=[current_profile, current_revenue, current_margin]
)
country_selector.change(
fn=generate_country_preview,
inputs=[country_selector],
outputs=[country_preview]
)
compare_button.click(
fn=run_comprehensive_analysis,
inputs=[
profile_selector, country_selector, current_revenue, current_margin,
current_corp_tax, current_pers_tax, current_living, current_business,
revenue_multiplier, margin_improvement, success_probability,
time_horizon, discount_rate
],
outputs=[
country_heatmap, main_dashboard, ai_insights_display,
kpi_cards, detailed_analysis, results_section
]
)
# Enhanced Footer
gr.HTML("""
""")
return app
# =========================
# ADDITIONAL UTILITY FUNCTIONS
# =========================
def generate_pdf_report(result: Dict, profile: UserProfile, country: CountryData) -> str:
"""Generate comprehensive PDF report (placeholder for actual implementation)"""
return f"PDF report generated for {profile.name} -> {country.name} migration analysis"
def send_to_crm(email: str, profile: str, result: Dict) -> bool:
"""Send lead data to CRM system (placeholder)"""
print(f"CRM: New lead {email} - {profile} - ROI: {result.get('roi', 0):.1f}%")
return True
def schedule_consultation(email: str, profile: str, country: str, roi: float) -> str:
"""Schedule consultation via Calendly API (placeholder)"""
return f"https://calendly.com/visatier/consultation?email={email}&profile={profile}"
# =========================
# MAIN EXECUTION
# =========================
if __name__ == "__main__":
# Create and launch the enhanced application
app = create_premium_immigration_app()
# Development server
app.launch(
server_name="0.0.0.0",
server_port=7860,
share=False,
debug=True,
show_error=True
)