app.py

import gradio as gradio
import joblib as joblib
import pip
# pip install gradio
# pip install joblib
# pip install xgboost
# pip install scikit-learn

import joblib
import numpy as np
import gradio as gr

# Load the XGBoost model
xgboost_model = joblib.load('/Users/rak/PycharmProject/Credit_Card_Fraud_Model/xgboost_model_new.pkl')

# Load the StandardScaler
scaler = joblib.load('/Users/rak/PycharmProject/Credit_Card_Fraud_Model/scaler.pkl')

month_to_number = {
    "January": 1,
    "February": 2,
    "March": 3,
    "April": 4,
    "May": 5,
    "June": 6,
    "July": 7,
    "August": 8,
    "September": 9,
    "October": 10,
    "November": 11,
    "December": 12,
}

def time_of_dayy(hour):
    if 6 <= hour < 12:
        return 'Morning'
    elif 12 <= hour < 18:
        return 'Afternoon'
    elif 18 <= hour < 24:
        return 'Evening'
    else:
        return 'Night'

# Define category options
category_options = [
    'Food/Dining',
    'Gas/Transport',
    'Online Grocery',
    'In-Person Grocery',
    'Health/Fitness',
    'Home',
    'Kids/Pets',
    'Miscellaneous Online',
    'Miscellaneous In-Person',
    'Personal Care',
    'Shopping Online',
    'Shopping In-Person',
    'Travel'
]

def predict_credit_card_fraud(amount, city_pop, month, hour, age, gender, category):
    # Map the input month name to its corresponding number
    month = month_to_number[month]

    time_of_day = time_of_dayy(hour)

    # Prepare input data with dummy variables for category
    input_data = np.array([[amount, city_pop, month, hour, age, int(gender == 'M'),
                            int(time_of_day == 'Night'), int(time_of_day == 'Evening'), int(time_of_day == 'Morning')] +
                           [int(category == cat) for cat in category_options]])

    # Scale the input data using the loaded StandardScaler
    input_data[:, 0:2] = scaler.transform(input_data[:, 0:2])

    # Use predict_proba to get probability scores for class 1
    probability = xgboost_model.predict_proba(input_data)[:, 1]

    # Return the probability score
    return round(probability[0], 2)

gender_options = ["M", "F"]
months = list(month_to_number.keys())

iface = gr.Interface(fn=predict_credit_card_fraud,
                    inputs=[
                        gr.Number(label="Amount", info="Enter the Amount of the Transaction in Dollars"),
                        gr.Number(label="City Population", info="Enter the City Population"),
                        gr.Dropdown(
                            months,
                            label="Month",
                            info="Select the month of the transaction"
                        ),
                        gr.Slider(label="Hour", info="Enter the Hour in which the Transaction Occurred", minimum=0, maximum=23, step=1),
                        gr.Slider(label="Age", minimum=10, maximum=100, step=1),
                        gr.Radio(label="Gender", choices=gender_options),
                        gr.Dropdown(
                            category_options,
                            label="Category",
                            info="Select the Category of Purchase"
                        )
                    ],
                    outputs="text")

if __name__ == "__main__":
    iface.launch(share=True)