import pandas as pd
from sklearn.linear_model import LinearRegression

# Prepare the data
data = {
    'total_plays': [408000, 1110000],
    'monthly_playtime': [62, 68],
    'total_new': [1418, 17000],
    '%_new_players': [0.002, 0.015],
    'average_retention': [11/12, 25],
    'payout': [16500, 83000]
}

df = pd.DataFrame(data)

# Features and target
X = df[['total_plays', 'monthly_playtime', 'total_new', '%_new_players', 'average_retention']]
y = df['payout']

# Train the model
model = LinearRegression()
model.fit(X, y)

# Function to estimate payout
def estimate_payout(total_plays, monthly_playtime, total_new, perc_new_players, average_retention):
    plays_playtime = (total_plays / 1000) * monthly_playtime
    input_data = pd.DataFrame({
        'total_plays': [total_plays],
        'monthly_playtime': [monthly_playtime],
        'total_new': [total_new],
        '%_new_players': [perc_new_players],
        'average_retention': [average_retention]
    })
    estimated_payout = model.predict(input_data)
    return estimated_payout[0]

# Console App
if __name__ == "__main__":
    print("Payout Estimator")
    
    # User input
    total_plays = int(input("Enter the total number of plays: "))
    monthly_playtime = float(input("Enter the monthly playtime in minutes: "))
    total_new = int(input("Enter the total number of new players: "))
    perc_new_players = float(input("Enter the percentage of new players (e.g., 0.002): "))
    average_retention = float(input("Enter the average retention (e.g., 11/12): "))
    
    # Calculate the payout
    estimated_payout = estimate_payout(total_plays, monthly_playtime, total_new, perc_new_players, average_retention)
    
    # Display the result
    print(f"\nEstimated payout: ${estimated_payout:.2f}")