from tabpfn import TabPFNRegressor
import pandas as pd

# Sales prediction features
sales_features = [
    'historical_sales',
    'seasonality_factor',
    'marketing_spend',
    'competitor_pricing',
    'economic_indicators',
    'weather_data',
    'holiday_calendar'
]

# Load sales data
sales_data = pd.read_csv("sales_data.csv")
X = sales_data[sales_features]
y = sales_data['sales_target']

# Predict sales
regressor = TabPFNRegressor()
sales_predictions = regressor.predict(X)

# Get prediction intervals
intervals = regressor.predict_interval(X, confidence=0.95)

# Business insights
for i, (pred, interval) in enumerate(zip(sales_predictions, intervals)):
    lower, upper = interval
    print(f"Period {i}: ${pred:,.0f} (Range: ${lower:,.0f} - ${upper:,.0f})")

# Price optimization features
pricing_features = [
    'product_cost',
    'competitor_price',
    'demand_elasticity',
    'inventory_level',
    'seasonality',
    'customer_segment',
    'market_conditions'
]

def optimize_price(product_data):
    regressor = TabPFNRegressor()
    
    # Predict optimal price
    optimal_price = regressor.predict(product_data)
    
    # Get confidence interval
    price_interval = regressor.predict_interval(product_data, confidence=0.90)
    
    return {
        'recommended_price': optimal_price[0],
        'price_range': price_interval[0],
        'confidence': 0.90
    }

# Credit risk features
credit_features = [
    'credit_score',
    'income',
    'debt_to_income_ratio',
    'employment_history',
    'loan_amount',
    'loan_term',
    'collateral_value'
]

def assess_credit_risk(applicant_data):
    regressor = TabPFNRegressor()
    
    # Predict risk score (0-100)
    risk_score = regressor.predict(applicant_data)
    
    # Get risk confidence interval
    risk_interval = regressor.predict_interval(applicant_data, confidence=0.95)
    
    # Determine risk category
    if risk_score < 30:
        risk_category = "LOW"
    elif risk_score < 60:
        risk_category = "MEDIUM"
    else:
        risk_category = "HIGH"
    
    return {
        'risk_score': risk_score[0],
        'risk_category': risk_category,
        'confidence_interval': risk_interval[0]
    }

# Employee performance features
performance_features = [
    'years_experience',
    'education_level',
    'training_hours',
    'previous_performance',
    'team_size',
    'project_complexity',
    'workload'
]

def predict_employee_performance(employee_data):
    regressor = TabPFNRegressor()
    
    # Predict performance score
    performance_score = regressor.predict(employee_data)
    
    # Get prediction interval
    performance_interval = regressor.predict_interval(employee_data, confidence=0.90)
    
    return {
        'predicted_performance': performance_score[0],
        'performance_range': performance_interval[0],
        'recommendations': get_performance_recommendations(performance_score[0])
    }

# Property valuation features
property_features = [
    'square_footage',
    'bedrooms',
    'bathrooms',
    'location_score',
    'school_rating',
    'crime_rate',
    'transportation_access',
    'market_trends'
]

def estimate_property_value(property_data):
    regressor = TabPFNRegressor()
    
    # Predict property value
    estimated_value = regressor.predict(property_data)
    
    # Get valuation range
    value_interval = regressor.predict_interval(property_data, confidence=0.95)
    
    return {
        'estimated_value': estimated_value[0],
        'value_range': value_interval[0],
        'investment_potential': assess_investment_potential(estimated_value[0])
    }