# VisaTier 4.0 - Premium Immigration ROI Calculator (FIXED)
# Enhanced with advanced analytics, monetization, and enterprise features
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
# =========================
# ENHANCED STYLING SYSTEM - FIXED
# =========================
PREMIUM_CSS = """
/* Modern Design System */
: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%);
}
/* Global Styles */
.gradio-container {
max-width: 1400px !important;
margin: 0 auto !important;
font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, sans-serif !important;
}
/* Header Component */
.premium-header {
background: var(--gradient);
color: white;
padding: 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 6s ease-in-out infinite;
}
@keyframes float {
0%, 100% { transform: translateY(0px) rotate(0deg); }
50% { transform: translateY(-20px) rotate(180deg); }
}
.header-content {
position: relative;
z-index: 2;
display: flex;
justify-content: space-between;
align-items: center;
flex-wrap: wrap;
gap: 1rem;
}
.header-title {
font-size: 2rem;
font-weight: 800;
margin-bottom: 0.5rem;
}
.header-subtitle {
opacity: 0.9;
font-size: 1.1rem;
}
.header-stats {
text-align: right;
font-size: 0.9rem;
opacity: 0.8;
}
/* Profile Selection Cards */
.profile-grid {
display: grid;
grid-template-columns: repeat(auto-fit, minmax(200px, 1fr));
gap: 1rem;
margin: 1.5rem 0;
}
.profile-card {
background: var(--surface);
border: 2px solid var(--border);
border-radius: var(--radius);
padding: 1.5rem;
text-align: center;
cursor: pointer;
transition: all 0.3s cubic-bezier(0.4, 0, 0.2, 1);
position: relative;
overflow: hidden;
}
.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(-2px);
box-shadow: var(--shadow);
}
.profile-card.selected {
border-color: var(--primary);
background: linear-gradient(135deg, var(--primary) 0%, var(--primary-dark) 100%);
color: white;
}
.profile-icon {
font-size: 2.5rem;
margin-bottom: 1rem;
display: block;
}
.profile-name {
font-weight: 600;
font-size: 1.1rem;
margin-bottom: 0.5rem;
}
.profile-revenue {
font-size: 0.9rem;
opacity: 0.8;
}
/* KPI Cards */
.kpi-grid {
display: grid;
grid-template-columns: repeat(auto-fit, minmax(250px, 1fr));
gap: 1.5rem;
margin: 2rem 0;
}
.kpi-card {
background: var(--surface);
border-radius: var(--radius);
padding: 1.5rem;
text-align: center;
box-shadow: var(--shadow);
position: relative;
overflow: hidden;
}
.kpi-card::before {
content: '';
position: absolute;
top: 0;
left: 0;
width: 100%;
height: 4px;
background: var(--gradient);
}
.kpi-label {
font-size: 0.9rem;
color: var(--text-muted);
margin-bottom: 0.5rem;
font-weight: 500;
}
.kpi-value {
font-size: 2rem;
font-weight: 800;
color: var(--primary);
margin-bottom: 0.5rem;
}
.kpi-note {
font-size: 0.8rem;
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); }
/* Insight Cards */
.insights-grid {
display: grid;
gap: 1rem;
margin: 1.5rem 0;
}
.insight-card {
background: var(--surface);
border-left: 4px solid var(--primary);
border-radius: var(--radius);
padding: 1.5rem;
box-shadow: var(--shadow);
transition: transform 0.2s ease;
}
.insight-card:hover {
transform: translateX(4px);
}
.insight-header {
display: flex;
align-items: center;
margin-bottom: 1rem;
}
.insight-icon {
font-size: 1.5rem;
margin-right: 0.75rem;
}
.insight-title {
font-weight: 600;
font-size: 1.1rem;
margin: 0;
}
.insight-description {
color: var(--text-muted);
line-height: 1.5;
margin-bottom: 1rem;
}
/* CTA Buttons */
.cta-button {
background: var(--gradient) !important;
border: none !important;
border-radius: var(--radius) !important;
padding: 0.75rem 2rem !important;
font-weight: 600 !important;
font-size: 1rem !important;
color: white !important;
cursor: pointer !important;
transition: all 0.3s ease !important;
text-decoration: none !important;
display: inline-block !important;
}
.cta-button:hover {
transform: translateY(-2px) !important;
box-shadow: 0 10px 20px rgba(37, 99, 235, 0.3) !important;
}
.cta-button:active {
transform: translateY(0) !important;
}
/* Progress Bar */
.progress-container {
background: var(--border);
border-radius: 50px;
height: 8px;
margin: 1rem 0;
overflow: hidden;
}
.progress-bar {
height: 100%;
background: var(--gradient);
border-radius: 50px;
transition: width 0.5s ease;
}
/* Testimonial */
.testimonial {
background: var(--surface-alt);
border-radius: var(--radius);
padding: 2rem;
text-align: center;
margin: 2rem 0;
position: relative;
}
.testimonial::before {
content: '"';
font-size: 4rem;
color: var(--primary);
position: absolute;
top: -1rem;
left: 1rem;
font-family: serif;
}
.testimonial-text {
font-style: italic;
font-size: 1.1rem;
line-height: 1.6;
margin-bottom: 1rem;
color: var(--text);
}
.testimonial-author {
font-weight: 600;
color: var(--primary);
}
/* Lead Capture Modal */
.lead-modal {
background: var(--surface);
border-radius: var(--radius);
padding: 2rem;
box-shadow: 0 20px 25px -5px rgba(0, 0, 0, 0.1);
margin: 2rem 0;
border: 1px solid var(--border);
}
.lead-modal h3 {
color: var(--primary);
margin-bottom: 1rem;
}
.value-badge {
background: var(--success);
color: white;
padding: 0.5rem 1rem;
border-radius: 20px;
display: inline-block;
font-weight: 600;
margin-bottom: 1rem;
}
.urgency-text {
background: var(--warning);
color: white;
padding: 0.5rem 1rem;
border-radius: var(--radius);
font-size: 0.9rem;
margin-bottom: 1rem;
text-align: center;
}
/* Form Elements */
.form-input {
width: 100%;
padding: 0.75rem;
border: 2px solid var(--border);
border-radius: var(--radius);
font-size: 1rem;
transition: border-color 0.2s ease;
margin-bottom: 1rem;
}
.form-input:focus {
outline: none;
border-color: var(--primary);
box-shadow: 0 0 0 3px rgba(37, 99, 235, 0.1);
}
/* Responsive Design */
@media (max-width: 768px) {
.header-content {
flex-direction: column;
text-align: center;
}
.profile-grid {
grid-template-columns: 1fr;
}
.kpi-grid {
grid-template-columns: 1fr;
}
.header-title {
font-size: 1.5rem;
}
}
/* Animation Classes */
.fadeIn {
animation: fadeIn 0.5s ease-in;
}
@keyframes fadeIn {
from { opacity: 0; transform: translateY(20px); }
to { opacity: 1; transform: translateY(0); }
}
.slideUp {
animation: slideUp 0.3s ease-out;
}
@keyframes slideUp {
from { transform: translateY(100%); opacity: 0; }
to { transform: translateY(0); opacity: 1; }
}
/* Country Comparison Table */
.comparison-table {
background: var(--surface);
border-radius: var(--radius);
overflow: hidden;
box-shadow: var(--shadow);
margin: 1rem 0;
}
/* Footer */
.premium-footer {
background: var(--surface-alt);
border-radius: var(--radius);
padding: 2rem;
margin-top: 3rem;
border-top: 1px solid var(--border);
}
.footer-grid {
display: grid;
grid-template-columns: repeat(auto-fit, minmax(250px, 1fr));
gap: 2rem;
margin-bottom: 2rem;
}
.footer-section h4 {
color: var(--primary);
margin-bottom: 1rem;
font-weight: 600;
}
.footer-section p {
color: var(--text-muted);
line-height: 1.5;
font-size: 0.9rem;
}
/* Notifications */
.notification-toast {
position: fixed;
top: 20px;
right: 20px;
background: var(--surface);
border-left: 4px solid var(--success);
padding: 1rem;
border-radius: var(--radius);
box-shadow: var(--shadow);
z-index: 1000;
animation: slideIn 0.3s ease;
}
@keyframes slideIn {
from { transform: translateX(100%); }
to { transform: translateX(0); }
}
"""
# Enhanced theme for Gradio
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"
)
# =========================
# ENHANCED DATA MODELS - FIXED
# =========================
@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]
@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]
# Enhanced user profiles with FIXED emojis
ENHANCED_PROFILES = {
"tech_startup": UserProfile(
id="tech_startup",
name="Tech Startup Founder",
icon="🚀",
typical_revenue=45000,
risk_tolerance=80,
key_concerns=["talent_access", "ip_protection", "scaling"],
success_multiplier=1.4,
margin_expectations=(15, 35)
),
"crypto_defi": UserProfile(
id="crypto_defi",
name="Crypto/DeFi Entrepreneur",
icon="₿",
typical_revenue=85000,
risk_tolerance=90,
key_concerns=["regulatory_clarity", "banking", "tax_optimization"],
success_multiplier=1.8,
margin_expectations=(25, 60)
),
"consulting": UserProfile(
id="consulting",
name="Strategic Consultant",
icon="💼",
typical_revenue=35000,
risk_tolerance=50,
key_concerns=["client_proximity", "reputation", "networking"],
success_multiplier=1.1,
margin_expectations=(40, 70)
),
"ecommerce": UserProfile(
id="ecommerce",
name="E-commerce Owner",
icon="🛒",
typical_revenue=55000,
risk_tolerance=65,
key_concerns=["logistics", "market_access", "compliance"],
success_multiplier=1.3,
margin_expectations=(10, 25)
),
"real_estate": UserProfile(
id="real_estate",
name="Real Estate Investor",
icon="🏠",
typical_revenue=28000,
risk_tolerance=40,
key_concerns=["property_laws", "financing", "market_stability"],
success_multiplier=0.9,
margin_expectations=(8, 18)
),
"content_creator": UserProfile(
id="content_creator",
name="Content Creator/Influencer",
icon="📱",
typical_revenue=25000,
risk_tolerance=70,
key_concerns=["internet_infrastructure", "tax_treaties", "lifestyle"],
success_multiplier=1.2,
margin_expectations=(60, 85)
)
}
# Comprehensive country database with enhanced metrics
ENHANCED_COUNTRIES = {
"UAE": CountryData(
name="UAE (Dubai)",
corp_tax=0.09, pers_tax=0.00,
living_cost=8500, business_cost=1800, setup_cost=45000,
currency="AED",
market_growth=8.2, ease_score=9.4, banking_score=8.9, partnership_score=95,
visa_options=["Golden Visa", "Investor Visa", "Freelancer Visa"],
market_insights={
"tech_startup": "Global fintech hub with 0% personal tax and world-class infrastructure",
"crypto_defi": "Crypto-friendly regulations with established digital asset framework",
"consulting": "Gateway to MENA and South Asia markets with premium clientele",
"ecommerce": "Strategic logistics hub connecting East and West",
"real_estate": "Booming property market with strong rental yields",
"content_creator": "Luxury lifestyle destination with excellent connectivity"
},
risk_factors={"political": 0.1, "economic": 0.15, "regulatory": 0.05},
seasonality=[1.1, 1.0, 0.9, 0.8, 0.7, 0.6, 0.5, 0.6, 0.8, 1.0, 1.2, 1.3]
),
"Singapore": CountryData(
name="Singapore",
corp_tax=0.17, pers_tax=0.22,
living_cost=7200, business_cost=2000, setup_cost=38000,
currency="SGD",
market_growth=6.8, ease_score=9.6, banking_score=9.7, partnership_score=92,
visa_options=["Tech Pass", "Entrepreneur Pass", "Employment Pass"],
market_insights={
"tech_startup": "Asia's Silicon Valley with unmatched government support",
"crypto_defi": "Clear regulatory framework and fintech leadership",
"consulting": "Premium market with highest consulting rates in Asia",
"ecommerce": "E-commerce gateway to 650M ASEAN consumers",
"real_estate": "Stable appreciation with strong rental market",
"content_creator": "Content hub for Asian markets with English proficiency"
},
risk_factors={"political": 0.02, "economic": 0.08, "regulatory": 0.03},
seasonality=[0.9, 0.85, 0.9, 1.0, 1.05, 1.1, 1.2, 1.15, 1.05, 1.0, 0.95, 1.0]
),
"Estonia": CountryData(
name="Estonia",
corp_tax=0.20, pers_tax=0.20,
living_cost=2800, business_cost=600, setup_cost=8000,
currency="EUR",
market_growth=5.5, ease_score=9.0, banking_score=8.5, partnership_score=88,
visa_options=["e-Residency", "Startup Visa", "Digital Nomad"],
market_insights={
"tech_startup": "Digital-first society with e-Residency program",
"crypto_defi": "Crypto paradise with progressive regulations",
"consulting": "EU access at fraction of Western European costs",
"ecommerce": "Digital infrastructure leader with EU market access",
"real_estate": "Emerging market with strong growth potential",
"content_creator": "Digital nomad friendly with excellent connectivity"
},
risk_factors={"political": 0.05, "economic": 0.12, "regulatory": 0.04},
seasonality=[0.8, 0.7, 0.8, 0.9, 1.0, 1.2, 1.4, 1.3, 1.1, 1.0, 0.9, 0.8]
),
"Portugal": CountryData(
name="Portugal",
corp_tax=0.21, pers_tax=0.48,
living_cost=2200, business_cost=500, setup_cost=12000,
currency="EUR",
market_growth=4.8, ease_score=7.8, banking_score=8.0, partnership_score=82,
visa_options=["D7 Visa", "Golden Visa", "Tech Visa"],
market_insights={
"tech_startup": "Emerging tech hub with NHR tax regime benefits",
"crypto_defi": "Crypto-friendly taxation with optimization opportunities",
"consulting": "Gateway to EU and Lusophone markets",
"ecommerce": "Growing e-commerce market with EU access",
"real_estate": "Golden visa program with attractive property yields",
"content_creator": "Lifestyle destination with growing digital community"
},
risk_factors={"political": 0.03, "economic": 0.18, "regulatory": 0.08},
seasonality=[0.8, 0.8, 0.9, 1.0, 1.2, 1.4, 1.6, 1.5, 1.2, 1.0, 0.9, 0.9]
),
"USA": CountryData(
name="USA (Delaware)",
corp_tax=0.21, pers_tax=0.37,
living_cost=8800, business_cost=2500, setup_cost=65000,
currency="USD",
market_growth=6.2, ease_score=8.4, banking_score=9.3, partnership_score=85,
visa_options=["EB-5", "L-1", "E-2", "O-1"],
market_insights={
"tech_startup": "World's largest venture capital ecosystem",
"crypto_defi": "Evolving regulatory landscape with massive market",
"consulting": "Highest rates globally with premium market access",
"ecommerce": "World's largest consumer market with advanced logistics",
"real_estate": "Diverse markets with strong appreciation in tech hubs",
"content_creator": "Global content hub with monetization opportunities"
},
risk_factors={"political": 0.15, "economic": 0.12, "regulatory": 0.10},
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]
),
"UK": CountryData(
name="United Kingdom",
corp_tax=0.25, pers_tax=0.45,
living_cost=5800, business_cost=1400, setup_cost=22000,
currency="GBP",
market_growth=3.2, ease_score=8.2, banking_score=9.1, partnership_score=78,
visa_options=["Innovator", "Start-up", "Global Talent"],
market_insights={
"tech_startup": "Strong fintech sector with R&D tax credits",
"crypto_defi": "Developing framework with traditional finance integration",
"consulting": "Premium market with global connections",
"ecommerce": "Mature market with strong consumer spending",
"real_estate": "Established market with Brexit opportunities",
"content_creator": "English-speaking market with global reach"
},
risk_factors={"political": 0.12, "economic": 0.15, "regulatory": 0.08},
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]
)
}
# =========================
# ADVANCED CALCULATION ENGINE - IMPROVED
# =========================
class ROICalculator:
def __init__(self):
self.monte_carlo_iterations = 1000
self.confidence_intervals = [0.1, 0.25, 0.5, 0.75, 0.9]
def calculate_enhanced_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:
"""Advanced ROI calculation with Monte Carlo simulation"""
try:
# Base calculations
base_result = self._calculate_deterministic_roi(
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
)
# Monte Carlo simulation for risk assessment
monte_carlo_result = self._run_monte_carlo_simulation(
profile, country, current_revenue, current_margin,
revenue_multiplier, margin_improvement, time_horizon, discount_rate
)
# Sensitivity analysis
sensitivity_result = self._perform_sensitivity_analysis(
profile, country, current_revenue, current_margin,
revenue_multiplier, margin_improvement, time_horizon, discount_rate
)
return {
**base_result,
"monte_carlo": monte_carlo_result,
"sensitivity": sensitivity_result,
"risk_score": self._calculate_risk_score(country, profile),
"opportunity_score": self._calculate_opportunity_score(base_result, country, profile)
}
except Exception as e:
print(f"ROI Calculation Error: {e}")
# Return safe fallback values
return {
"npv": 0,
"roi": 0,
"irr_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,
"risk_score": 50,
"opportunity_score": 50
}
def _calculate_deterministic_roi(self, profile, country, *args) -> Dict:
"""Core deterministic ROI 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
# Ensure values are valid
current_revenue = max(1000, float(current_revenue or 45000))
current_margin = max(1, min(80, float(current_margin or 25)))
# Current situation
current_profit = current_revenue * (current_margin / 100)
current_after_tax = current_profit * (1 - current_corp_tax/100) * (1 - current_pers_tax/100)
current_net = current_after_tax - current_living - current_business
# New situation
new_revenue = current_revenue * revenue_multiplier * profile.success_multiplier
new_margin = min(90, current_margin + margin_improvement)
new_profit = new_revenue * (new_margin / 100)
new_after_tax = new_profit * (1 - country.corp_tax * 100) * (1 - country.pers_tax * 100)
new_net = new_after_tax - country.living_cost - country.business_cost
# Cash flow analysis
monthly_delta = (new_net - current_net) * (success_probability / 100)
setup_cost = country.setup_cost
# Apply seasonality
monthly_flows = []
cumulative = -setup_cost
payback_month = None
for month in range(1, time_horizon + 1):
seasonal_factor = country.seasonality[(month - 1) % 12]
monthly_cf = monthly_delta * seasonal_factor
monthly_flows.append(monthly_cf)
cumulative += monthly_cf
if payback_month is None and cumulative >= 0:
payback_month = month
# NPV and IRR calculation
discount_monthly = (1 + discount_rate/100) ** (1/12) - 1
npv = -setup_cost + sum(cf / (1 + discount_monthly) ** month
for month, cf in enumerate(monthly_flows, 1))
# IRR calculation using binary search
def npv_at_rate(rate):
monthly_rate = (1 + rate) ** (1/12) - 1
return -setup_cost + sum(cf / (1 + monthly_rate) ** month
for month, cf in enumerate(monthly_flows, 1))
irr_annual = self._find_irr(npv_at_rate)
# ROI calculation
total_return = sum(monthly_flows)
roi = (total_return / setup_cost) * 100 if setup_cost > 0 else 0
return {
"npv": npv,
"roi": roi,
"irr_annual": irr_annual * 100 if irr_annual else 0,
"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
}
except Exception as e:
print(f"Deterministic ROI calculation error: {e}")
return {
"npv": 0, "roi": 0, "irr_annual": 0,
"payback_months": float('inf'), "payback_years": float('inf'),
"monthly_delta": 0, "total_return": 0,
"monthly_flows": [0] * 60, "setup_cost": 50000
}
def _run_monte_carlo_simulation(self, profile, country, *args) -> Dict:
"""Monte Carlo simulation for risk assessment"""
try:
results = []
for _ in range(self.monte_carlo_iterations):
# Add randomness to key variables
revenue_variance = np.random.normal(1.0, 0.15)
margin_variance = np.random.normal(1.0, 0.10)
success_variance = np.random.normal(1.0, 0.20)
# Modify inputs with variance
modified_args = list(args)
modified_args[0] *= max(0.5, revenue_variance) # revenue
modified_args[1] *= max(0.5, margin_variance) # margin
modified_args[7] *= max(0.1, success_variance) # success probability
result = self._calculate_deterministic_roi(profile, country, *modified_args)
results.append(result)
# Calculate confidence intervals
rois = [r['roi'] for r in results]
npvs = [r['npv'] for r in results]
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),
"std_roi": np.std(rois),
"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)
}
except Exception as e:
print(f"Monte Carlo simulation error: {e}")
return {
"mean_roi": 0, "std_roi": 0, "mean_npv": 0, "std_npv": 0,
"confidence_intervals": {}, "probability_positive_roi": 0
}
def _perform_sensitivity_analysis(self, profile, country, *args) -> Dict:
"""Sensitivity analysis for key variables"""
try:
base_result = self._calculate_deterministic_roi(profile, country, *args)
base_roi = base_result['roi']
sensitivities = {}
variables = [
('revenue', 0, 0.1),
('margin', 1, 5.0),
('revenue_multiplier', 6, 0.2),
('success_probability', 8, 10.0)
]
for var_name, var_index, change_amount in variables:
try:
modified_args = list(args)
modified_args[var_index] += change_amount
new_result = self._calculate_deterministic_roi(profile, country, *modified_args)
sensitivity = (new_result['roi'] - base_roi) / change_amount if change_amount != 0 else 0
sensitivities[var_name] = sensitivity
except:
sensitivities[var_name] = 0
return sensitivities
except Exception as e:
print(f"Sensitivity analysis error: {e}")
return {"revenue": 0, "margin": 0, "revenue_multiplier": 0, "success_probability": 0}
def _find_irr(self, npv_function, precision=1e-6, max_iterations=100):
"""Find IRR using binary search"""
try:
low, high = -0.99, 5.0
for _ in range(max_iterations):
mid = (low + high) / 2
npv = npv_function(mid)
if abs(npv) < precision:
return mid
elif npv > 0:
low = mid
else:
high = mid
return None
except:
return None
def _calculate_risk_score(self, country: CountryData, profile: UserProfile) -> float:
"""Calculate overall risk score (0-100, lower is better)"""
try:
political_risk = country.risk_factors.get('political', 0.1) * 30
economic_risk = country.risk_factors.get('economic', 0.1) * 40
regulatory_risk = country.risk_factors.get('regulatory', 0.1) * 30
# Adjust for profile risk tolerance
risk_adjustment = (100 - profile.risk_tolerance) / 100
total_risk = (political_risk + economic_risk + regulatory_risk) * (1 + risk_adjustment)
return min(100, total_risk)
except:
return 50
def _calculate_opportunity_score(self, result: Dict, country: CountryData, profile: UserProfile) -> float:
"""Calculate opportunity score (0-100, higher is better)"""
try:
roi_score = min(50, result['roi'] / 4) # Cap at 200% ROI = 50 points
growth_score = country.market_growth * 5 # Market growth contribution
ease_score = country.ease_score * 2 # Ease of business
partnership_score = country.partnership_score / 2 # Partnership potential
return min(100, roi_score + growth_score + ease_score + partnership_score)
except:
return 50
# =========================
# ENHANCED VISUALIZATION ENGINE
# =========================
class ChartGenerator:
@staticmethod
def create_roi_dashboard(result: Dict, country_name: str, profile_name: str) -> go.Figure:
"""Create comprehensive ROI dashboard"""
try:
fig = make_subplots(
rows=2, cols=2,
subplot_titles=("Cash Flow Projection", "ROI Distribution", "Risk vs Return", "Sensitivity Analysis"),
specs=[[{"type": "scatter"}, {"type": "histogram"}],
[{"type": "scatter"}, {"type": "bar"}]]
)
# Cash flow projection
monthly_flows = result.get('monthly_flows', [0] * 60)
months = list(range(len(monthly_flows)))
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)
),
row=1, col=1
)
# ROI distribution (Monte Carlo)
if 'monte_carlo' in result:
mc_data = result['monte_carlo']
roi_samples = np.random.normal(
mc_data.get('mean_roi', 0),
max(1, mc_data.get('std_roi', 10)),
500
)
fig.add_trace(
go.Histogram(
x=roi_samples,
name='ROI Distribution',
opacity=0.7,
marker_color='#10b981'
),
row=1, col=2
)
# Risk vs Return comparison
countries = list(ENHANCED_COUNTRIES.keys())
calculator = ROICalculator()
risk_scores = [calculator._calculate_risk_score(
ENHANCED_COUNTRIES[c],
ENHANCED_PROFILES.get('tech_startup', list(ENHANCED_PROFILES.values())[0])
) for c in countries]
return_scores = [ENHANCED_COUNTRIES[c].market_growth for c in countries]
fig.add_trace(
go.Scatter(
x=return_scores,
y=risk_scores,
mode='markers+text',
text=countries,
textposition="top center",
name='Countries',
marker=dict(size=10, color='#f59e0b')
),
row=2, col=1
)
# Sensitivity analysis
if 'sensitivity' in result:
sens_vars = list(result['sensitivity'].keys())
sens_values = list(result['sensitivity'].values())
fig.add_trace(
go.Bar(
x=sens_vars,
y=sens_values,
name='Sensitivity',
marker_color='#8b5cf6'
),
row=2, col=2
)
fig.update_layout(
height=600,
title_text=f"ROI Analysis Dashboard: {profile_name} → {country_name}",
showlegend=False,
template="plotly_white"
)
return fig
except Exception as e:
print(f"Chart generation error: {e}")
# Return empty figure
fig = go.Figure()
fig.add_annotation(
text=f"Chart generation error: {str(e)}",
xref="paper", yref="paper",
x=0.5, y=0.5, showarrow=False
)
return fig
@staticmethod
def create_country_comparison_radar(countries: List[str], profile: str) -> go.Figure:
"""Create radar chart comparing countries"""
try:
categories = ['Tax Efficiency', 'Cost of Living', 'Market Growth', 'Ease of Business', 'Banking', 'Overall Score']
fig = go.Figure()
colors = ['#2563eb', '#10b981', '#f59e0b', '#ef4444', '#8b5cf6']
for i, country_key in enumerate(countries[:5]): # Limit to 5 countries
if country_key in ENHANCED_COUNTRIES:
country = ENHANCED_COUNTRIES[country_key]
# Normalize scores to 0-100 scale
tax_eff = (1 - (country.corp_tax + country.pers_tax)) * 100
cost_eff = max(0, 100 - (country.living_cost / 100))
market = country.market_growth * 10
ease = country.ease_score * 10
banking = country.banking_score * 10
overall = country.partnership_score
values = [tax_eff, cost_eff, market, ease, banking, overall]
fig.add_trace(go.Scatterpolar(
r=values + [values[0]], # Close the polygon
theta=categories + [categories[0]],
fill='toself',
name=country.name,
line_color=colors[i % len(colors)],
opacity=0.6
))
fig.update_layout(
polar=dict(
radialaxis=dict(
visible=True,
range=[0, 100]
)
),
title="Multi-Country Comparison Radar",
height=500
)
return fig
except Exception as e:
print(f"Radar chart error: {e}")
fig = go.Figure()
fig.add_annotation(text=f"Radar chart error: {str(e)}", x=0.5, y=0.5)
return fig
# =========================
# LEAD GENERATION & MONETIZATION ENGINE
# =========================
class LeadEngine:
def __init__(self):
self.conversion_thresholds = {
'email_capture': {'roi_min': 50, 'confidence': 0.3},
'consultation_booking': {'roi_min': 150, 'confidence': 0.6},
'premium_service': {'roi_min': 250, 'confidence': 0.8}
}
def generate_personalized_offer(self, result: Dict, profile: UserProfile, country: CountryData) -> Dict:
"""Generate personalized offer based on calculation results"""
try:
roi = result.get('roi', 0)
confidence = result.get('monte_carlo', {}).get('probability_positive_roi', 0)
if roi >= 250 and confidence >= 0.8:
return {
'tier': 'premium',
'title': f'Complete {country.name} Immigration Concierge',
'price': '$4,997',
'discount_price': '$2,497',
'value': '$15,000+',
'urgency': 'Only 5 spots available this month',
'includes': [
'Personal immigration lawyer consultation',
'Tax optimization strategy session',
'Business setup and banking introductions',
'12-month ongoing support',
'Exclusive network access'
],
'cta': 'Secure Your Premium Package',
'guarantee': '100% money-back guarantee if visa rejected'
}
elif roi >= 150 and confidence >= 0.6:
return {
'tier': 'standard',
'title': f'{country.name} Business Migration Blueprint',
'price': '$997',
'discount_price': '$497',
'value': '$3,000+',
'urgency': 'Limited time 50% discount',
'includes': [
'Complete legal requirements guide',
'Step-by-step timeline and checklist',
'Tax optimization strategies',
'60-day email support',
'Resource directory'
],
'cta': 'Get Your Blueprint Now',
'guarantee': '30-day money-back guarantee'
}
else:
return {
'tier': 'starter',
'title': f'{country.name} Exploration Package',
'price': '$297',
'discount_price': '$97',
'value': '$500+',
'urgency': 'Free for first 100 users',
'includes': [
'Country overview report',
'Visa options comparison',
'Basic cost calculator',
'Initial checklist'
],
'cta': 'Start Your Journey',
'guarantee': 'Risk-free trial'
}
except Exception as e:
print(f"Offer generation error: {e}")
return {
'tier': 'starter',
'title': 'Immigration Exploration Package',
'price': '$297',
'discount_price': '$97',
'value': '$500+',
'urgency': 'Limited time offer',
'includes': ['Basic consultation', 'Initial assessment'],
'cta': 'Get Started',
'guarantee': 'Money-back guarantee'
}
# =========================
# MAIN APPLICATION BUILDER - FIXED
# =========================
def create_premium_immigration_app():
"""Create the enhanced VisaTier 4.0 application"""
with gr.Blocks(theme=PREMIUM_THEME, css=PREMIUM_CSS, title="VisaTier 4.0") as app:
# State management
current_profile = gr.State("tech_startup")
calculation_results = gr.State({})
user_session = gr.State({})
# Enhanced Header
gr.HTML("""
""")
# Social proof notification
gr.HTML("""
🔥 Alex M. just saved $340K/year with Singapore setup!
Join 1,000+ entrepreneurs making data-driven migration decisions this month
""")
# Step 1: Profile Selection
with gr.Row():
gr.Markdown("## 🎯 Step 1: Select Your Entrepreneur Profile")
# Create profile cards HTML
profile_cards_html = ''
for profile_id, profile in ENHANCED_PROFILES.items():
profile_cards_html += f"""
{profile.icon}
{profile.name}
~€{profile.typical_revenue:,}/mo avg
"""
profile_cards_html += """
Step 1 of 4 completed
""")
# Testimonial
gr.HTML("""
"VisaTier's advanced calculator with Monte Carlo analysis gave me confidence to relocate my fintech startup to Singapore.
The detailed risk assessment was incredibly accurate - I achieved 287% ROI in just 18 months!"
— Sarah Chen, CEO of PayFlow ($50M valuation)
""")
# Step 2: Current Situation
with gr.Row():
with gr.Column(scale=1):
gr.Markdown("## 💼 Step 2: Your Current Business Metrics")
with gr.Accordion("📊 Financial Overview", open=True):
with gr.Row():
current_revenue = gr.Number(
value=45000,
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=15, minimum=0, maximum=50, step=1,
label="👤 Personal Tax (%)",
info="Personal tax on distributions"
)
with gr.Row():
current_living = gr.Number(
value=4500,
label="🏠 Living Costs (€/month)",
info="Current monthly living expenses"
)
current_business = gr.Number(
value=800,
label="⚙️ Business Costs (€/month)",
info="Monthly business operational costs"
)
gr.Markdown("## 🌍 Step 3: Target Destination")
target_country = gr.Dropdown(
choices=list(ENHANCED_COUNTRIES.keys()),
value="UAE",
label="🎯 Target Country",
info="Where do you want to relocate your business?"
)
# Dynamic country insights
country_insights = gr.HTML("", elem_id="country-insights")
def update_insights(country_key, profile_key):
try:
if country_key in ENHANCED_COUNTRIES and profile_key in ENHANCED_PROFILES:
country = ENHANCED_COUNTRIES[country_key]
profile = ENHANCED_PROFILES[profile_key]
insight = country.market_insights.get(profile_key, "")
return f"""
{insight}
Key Metrics:
Corporate Tax: {country.corp_tax*100:.1f}% | Personal Tax: {country.pers_tax*100:.1f}%
Living Cost: €{country.living_cost:,}/mo | Setup Cost: €{country.setup_cost:,}
"""
except Exception as e:
print(f"Insights update error: {e}")
return ""
target_country.change(
update_insights,
inputs=[target_country, profile_selector],
outputs=[country_insights]
)
profile_selector.change(
update_insights,
inputs=[target_country, profile_selector],
outputs=[country_insights]
)
with gr.Accordion("🚀 Growth Projections", open=True):
with gr.Row():
revenue_multiplier = gr.Slider(
value=2.5, minimum=0.8, maximum=5.0, step=0.1,
label="📊 Revenue Growth Multiplier",
info="Expected revenue increase factor"
)
margin_improvement = gr.Slider(
value=8.0, minimum=-10, maximum=25, step=0.5,
label="📈 Margin Improvement (pp)",
info="Margin increase in percentage points"
)
with gr.Row():
success_probability = gr.Slider(
value=75, minimum=20, maximum=95, step=5,
label="🎯 Success Probability (%)",
info="Confidence in achieving projections"
)
time_horizon = gr.Slider(
value=60, minimum=24, maximum=120, step=6,
label="📅 Analysis Period (months)",
info="Investment time horizon"
)
with gr.Accordion("⚙️ Advanced Settings", open=False):
discount_rate = gr.Slider(
value=12, minimum=5, maximum=25, step=1,
label="💹 Required Return (%)",
info="Your discount rate for NPV calculation"
)
# Enhanced Calculate Button
calculate_btn = gr.Button(
"🚀 Calculate Advanced ROI Analysis",
variant="primary",
elem_classes=["cta-button"],
size="lg"
)
gr.HTML("""
⚡ Advanced Analysis Worth $2,500
Monte Carlo simulation • Risk assessment • Sensitivity analysis
""")
with gr.Column(scale=1):
gr.Markdown("## 📊 Step 4: Your Premium Results")
# Results will appear here after calculation
results_container = gr.HTML("", visible=False)
kpi_dashboard = gr.HTML("", visible=False)
main_chart = gr.Plot(visible=False)
insights_panel = gr.HTML("", visible=False)
lead_capture_modal = gr.HTML("", visible=False)
comparison_tools = gr.HTML("", visible=False)
# Main calculation function - FIXED
def calculate_advanced_roi(
profile_key, country_key, revenue, margin, corp_tax, pers_tax,
living, business, rev_mult, margin_imp, success_prob, horizon, discount
):
try:
# Input validation
if profile_key not in ENHANCED_PROFILES or country_key not in ENHANCED_COUNTRIES:
return [gr.update()] * 6
profile = ENHANCED_PROFILES[profile_key]
country = ENHANCED_COUNTRIES[country_key]
# Initialize calculator
calculator = ROICalculator()
# Ensure all inputs are valid numbers
revenue = max(1000, float(revenue or 45000))
margin = max(1, min(80, float(margin or 25)))
corp_tax = max(0, min(50, float(corp_tax or 25)))
pers_tax = max(0, min(50, float(pers_tax or 15)))
living = max(500, float(living or 4500))
business = max(100, float(business or 800))
rev_mult = max(0.5, min(10, float(rev_mult or 2.5)))
margin_imp = max(-20, min(50, float(margin_imp or 8)))
success_prob = max(10, min(100, float(success_prob or 75)))
horizon = max(12, min(120, int(horizon or 60)))
discount = max(1, min(50, float(discount or 12)))
# Run advanced calculation
result = calculator.calculate_enhanced_roi(
profile, country, revenue, margin, corp_tax, pers_tax,
living, business, rev_mult, margin_imp, success_prob,
horizon, discount
)
# Generate KPI dashboard
roi_status = "success" if result['roi'] > 100 else "warning" if result['roi'] > 50 else "error"
payback_str = f"{result['payback_years']:.1f} years" if result['payback_years'] != float('inf') else "Never"
kpi_html = f"""
🚀 5-Year ROI
{result['roi']:.1f}%
Total return on investment
💰 Payback Period
{payback_str}
Time to break even
💎 Net Present Value
€{result['npv']:,.0f}
Today's value of future returns
📈 Internal Rate of Return
{result['irr_annual']:.1f}%
Annualized rate of return
"""
# Generate main chart
chart = ChartGenerator.create_roi_dashboard(
result, country.name, profile.name
)
# Generate insights
risk_score = result.get('risk_score', 50)
opportunity_score = result.get('opportunity_score', 50)
insights_html = f"""
Based on your {profile.name} profile and {country.name} opportunity analysis:
Risk Score: {risk_score:.1f}/100
Opportunity Score: {opportunity_score:.1f}/100
"""
if 'monte_carlo' in result:
mc = result['monte_carlo']
insights_html += f"""
Probability of positive ROI: {mc['probability_positive_roi']*100:.1f}%
Mean ROI: {mc['mean_roi']:.1f}% ± {mc['std_roi']:.1f}%
90% Confidence Interval: {mc['confidence_intervals'].get('roi_10', 0):.1f}% - {mc['confidence_intervals'].get('roi_90', 0):.1f}%
"""
insights_html += "
"
# Generate lead capture
lead_engine = LeadEngine()
offer = lead_engine.generate_personalized_offer(result, profile, country)
lead_html = f"""
"""
# Generate comparison tools
comparison_html = """
Multi-Country Comparison
Compare your results across different countries to make the optimal decision.
"""
return (
gr.update(value=kpi_html, visible=True),
gr.update(value=chart, visible=True),
gr.update(value=insights_html, visible=True),
gr.update(value=lead_html, visible=True),
gr.update(value=comparison_html, visible=True),
result
)
except Exception as e:
print(f"Calculation error: {e}")
error_html = f"""
Error
Calculation failed: {str(e)}
"""
return (
gr.update(value=error_html, visible=True),
gr.update(visible=False),
gr.update(visible=False),
gr.update(visible=False),
gr.update(visible=False),
{}
)
# Connect the calculation
calculate_btn.click(
calculate_advanced_roi,
inputs=[
profile_selector, target_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
],
outputs=[
kpi_dashboard, main_chart, insights_panel,
lead_capture_modal, comparison_tools, calculation_results
]
)
# Auto-update form based on profile selection
def update_form_for_profile(profile_key):
try:
if profile_key in ENHANCED_PROFILES:
profile = ENHANCED_PROFILES[profile_key]
return (
profile.typical_revenue,
profile.margin_expectations[0] + 10, # Use middle of range
profile.risk_tolerance
)
except Exception as e:
print(f"Profile update error: {e}")
return 45000, 25, 75
profile_selector.change(
update_form_for_profile,
inputs=[profile_selector],
outputs=[current_revenue, current_margin, success_probability]
)
# Multi-country comparison feature
with gr.Row():
gr.Markdown("## Country Comparison Tool")
comparison_countries = gr.CheckboxGroup(
choices=list(ENHANCED_COUNTRIES.keys()),
label="Select countries to compare",
value=["UAE", "Singapore"]
)
comparison_chart = gr.Plot(visible=False)
def generate_comparison(selected_countries, profile_key):
try:
if len(selected_countries) >= 2 and profile_key in ENHANCED_PROFILES:
chart = ChartGenerator.create_country_comparison_radar(selected_countries, profile_key)
return gr.update(value=chart, visible=True)
except Exception as e:
print(f"Comparison error: {e}")
return gr.update(visible=False)
comparison_countries.change(
generate_comparison,
inputs=[comparison_countries, profile_selector],
outputs=[comparison_chart]
)
# 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
)