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app.py

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+import streamlit as st
+import joblib
+import pandas as pd
+from sklearn.preprocessing import LabelEncoder, StandardScaler
+
+# Load the trained model
+model = joblib.load('random_forest_model.pkl')  # Replace with your actual model file
+
+# Define encoders for categorical columns using the actual values
+label_encoders = {
+    'Day of Week': LabelEncoder().fit(['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']),
+    'Type of Card': LabelEncoder().fit(['Visa', 'MasterCard']),
+    'Entry Mode': LabelEncoder().fit(['Tap', 'PIN', 'CVC']),
+    'Type of Transaction': LabelEncoder().fit(['POS', 'Online', 'ATM']),
+    'Merchant Group': LabelEncoder().fit(['Entertainment', 'Services', 'Restaurant', 'Electronics', 'Children', 
+                                          'Fashion', 'Food', 'Products', 'Subscription', 'Gaming']),
+    'Country of Transaction': LabelEncoder().fit(['United Kingdom', 'USA', 'India', 'Russia', 'China']),
+    'Shipping Address': LabelEncoder().fit(['United Kingdom', 'USA', 'India', 'Russia', 'China']),
+    'Country of Residence': LabelEncoder().fit(['United Kingdom', 'USA', 'India', 'Russia', 'China']),
+    'Gender': LabelEncoder().fit(['M', 'F'])
+}
+
+# Define the scaler for numerical columns (use the scaler from training if available)
+scaler = StandardScaler()
+
+# Define the function to make predictions
+def predict_fraud(day_of_week, time, type_of_card, entry_mode, amount, type_of_transaction, merchant_group, 
+                  country_of_transaction, shipping_address, country_of_residence, gender, age):
+    # Create a DataFrame for the input
+    input_data = pd.DataFrame({
+        'Day of Week': [day_of_week],
+        'Time': [time],
+        'Type of Card': [type_of_card],
+        'Entry Mode': [entry_mode],
+        'Amount': [amount],
+        'Type of Transaction': [type_of_transaction],
+        'Merchant Group': [merchant_group],
+        'Country of Transaction': [country_of_transaction],
+        'Shipping Address': [shipping_address],
+        'Country of Residence': [country_of_residence],
+        'Gender': [gender],
+        'Age': [age],
+    })
+
+    # Encode categorical columns
+    for col in label_encoders:
+        input_data[col] = label_encoders[col].transform(input_data[col])
+
+    # Standardize numerical features
+    numerical_cols = ['Time', 'Amount', 'Age']
+    input_data[numerical_cols] = scaler.fit_transform(input_data[numerical_cols])  # Use the fitted scaler from training
+
+    # Make the prediction
+    prediction = model.predict(input_data)
+
+    # Convert the numeric prediction to a meaningful label
+    return "Fraud" if prediction[0] == 1 else "Not Fraud"
+
+# Custom CSS for background, fonts, and boxes
+st.markdown("""
+    <style>
+    /* Background color for the app */
+    .main {
+        background-color: #f0f2f6;
+        font-family: 'Helvetica', sans-serif;
+    }
+
+    /* Title and headers */
+    h1, h2, h3, h4, h5, h6 {
+        color: #3c3c3c;
+        font-family: 'Arial', sans-serif;
+    }
+
+    /* Customizing the input headers (labels) */
+    .stSelectbox label, .stNumberInput label {
+        font-size: 16px;
+        color: #333333;
+        font-family: 'Montserrat', sans-serif;
+        font-weight: 600;
+        text-transform: uppercase;
+        margin-bottom: 5px;
+    }
+
+    /* Input boxes */
+    .stSelectbox, .stNumberInput {
+        background-color: #e6eaf2;
+        border-radius: 10px;
+        color: #3c3c3c;
+    }
+
+    /* Adjust buttons */
+    button {
+        background-color: #4CAF50 !important;
+        color: white !important;
+        border-radius: 10px !important;
+        padding: 10px 20px !important;
+    }
+
+    /* Custom text for the prediction output */
+    .output-text {
+        font-size: 24px;
+        font-weight: bold;
+    }
+
+    /* Red text for fraud prediction */
+    .fraud {
+        color: red;
+    }
+
+    /* Green text for not fraud prediction */
+    .not-fraud {
+        color: green;
+    }
+
+    /* Custom styles for plus/minus buttons */
+    .stNumberInput button {
+        background-color: #d0d3da !important;  /* Light color for the plus/minus buttons */
+    }
+
+    </style>
+    """, unsafe_allow_html=True)
+
+# Streamlit app layout
+st.markdown("<h1 style='text-align: center; font-family: Arial, sans-serif; color: #4CAF50;'>Credit Card Fraud Detection App</h1>", unsafe_allow_html=True)
+
+# First row: Day of Week, Time, Type of Card
+col1, col2, col3 = st.columns(3)
+day_of_week = col1.selectbox("Day of Week", ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday'])
+time = col2.number_input("Time", min_value=0, max_value=24, value=12)  # Example for time in HHMM format
+type_of_card = col3.selectbox("Type of Card", ['Visa', 'MasterCard'])
+
+# Second row: Entry Mode, Amount, Type of Transaction
+col4, col5, col6 = st.columns(3)
+entry_mode = col4.selectbox("Entry Mode", ['Tap', 'PIN', 'CVC'])
+amount = col5.number_input("Amount", min_value=0.0, format="%.2f")
+type_of_transaction = col6.selectbox("Type of Transaction", ['POS', 'Online', 'ATM'])
+
+# Third row: Merchant Group, Country of Transaction
+col7, col8 = st.columns(2)
+merchant_group = col7.selectbox("Merchant Group", ['Entertainment', 'Services', 'Restaurant', 'Electronics', 
+                                                   'Children', 'Fashion', 'Food', 'Products', 
+                                                   'Subscription', 'Gaming'])
+country_of_transaction = col8.selectbox("Country of Transaction", ['United Kingdom', 'USA', 'India', 'Russia', 'China'])
+
+# Fourth row: Shipping Address, Country of Residence, Gender
+col9, col10, col11 = st.columns(3)
+shipping_address = col9.selectbox("Shipping Address", ['United Kingdom', 'USA', 'India', 'Russia', 'China'])
+country_of_residence = col10.selectbox("Country of Residence", ['United Kingdom', 'USA', 'India', 'Russia', 'China'])
+gender = col11.selectbox("Gender", ['M', 'F'])
+
+# Fifth row: Age
+col12 = st.columns(1)
+age = col12[0].number_input("Age", min_value=0)
+
+if st.button("Predict"):
+    prediction = predict_fraud(day_of_week, time, type_of_card, entry_mode, amount, type_of_transaction, 
+                               merchant_group, country_of_transaction, shipping_address, 
+                               country_of_residence, gender, age)
+    
+    # Conditional formatting for prediction output
+    if prediction == "Fraud":
+        st.markdown(f"<p class='output-text fraud'>Prediction: {prediction}</p>", unsafe_allow_html=True)
+    else:
+        st.markdown(f"<p class='output-text not-fraud'>Prediction: {prediction}</p>", unsafe_allow_html=True)

random_forest_model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:4557b088c71bc34018ed1f7e38cf871b7bdbdc844f5c43121c6d24cbfa7de52c
+size 34608825

requirements.txt

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+gradio
+streamlit
+joblib
+pandas
+scikit-learn==1.2.2