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app.py

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+import gradio as gr
+import numpy as np
+import pandas as pd
+import pickle
+import matplotlib.pyplot as plt
+
+# Load pre-trained models and encoders
+with open('is_fraud.pkl', 'rb') as f:
+    is_fraud_model = pickle.load(f)
+
+with open('fraud_type.pkl', 'rb') as f:
+    fraud_type_model = pickle.load(f)
+
+# Load label encoders for features
+encoders = {}
+for feature in ['card_type', 'location', 'purchase_category', 'time_of_day']:
+    with open(f'{feature}.pkl', 'rb') as f:
+        encoders[feature] = pickle.load(f)
+
+with open('fraud_type_le.pkl', 'rb') as f:
+    fraud_type_encoder = pickle.load(f)
+
+# Define the prediction function
+def predict_fraud(amount, card_type, location, purchase_category, customer_age, time_of_day):
+    # Preprocess input
+    input_data = pd.DataFrame({
+        'amount': [amount],
+        'card_type': [card_type],
+        'location': [location],
+        'purchase_category': [purchase_category],
+        'customer_age': [customer_age],
+        'time_of_day': [time_of_day]
+    })
+
+    # Label encode the inputs
+    for feature in ['card_type', 'location', 'purchase_category', 'time_of_day']:
+        input_data[feature] = encoders[feature].transform(input_data[feature])
+
+    # Fraud Detection (Binary Classification)
+    is_fraud_pred_prob = is_fraud_model.predict_proba(input_data)[0]
+    is_fraud_class = np.argmax(is_fraud_pred_prob)
+
+    # Fraud Type Classification (Multiclass Classification)
+    fraud_type_pred_prob = fraud_type_model.predict_proba(input_data)[0]
+    fraud_type_class = fraud_type_encoder.inverse_transform([np.argmax(fraud_type_pred_prob)])[0]
+
+    # Bar charts for probabilities
+    fig, axes = plt.subplots(1, 2, figsize=(12, 5))
+
+    # Plot Fraud Detection Probabilities
+    axes[0].bar(['Not Fraudulent', 'Fraudulent'], is_fraud_pred_prob)
+    axes[0].set_title('Fraud Detection Probability')
+    axes[0].set_ylabel('Probability')
+
+    # Plot Fraud Type Probabilities
+    fraud_types = fraud_type_encoder.classes_
+    axes[1].bar(fraud_types, fraud_type_pred_prob)
+    axes[1].set_title('Fraud Type Probability')
+    axes[1].set_ylabel('Probability')
+    plt.xticks(rotation=45, ha="right")
+
+    plt.tight_layout()
+
+    return f"Fraud Detection{max(is_fraud_pred_prob)*100}: {'Fraudulent' if is_fraud_class == 1 else 'Not Fraudulent'}", f"Predicted Fraud Type: {fraud_type_class}", fig
+
+# Gradio Interface Setup
+interface = gr.Interface(
+    fn=predict_fraud,
+    inputs=[
+        gr.Number(label="Transaction Amount"),
+        gr.Dropdown(choices=['MasterCard', 'Visa' ,'Rupay'],label="Card Type"),
+        gr.Dropdown(choices=['Surat' ,'Hyderabad' ,'Kolkata' ,'Mumbai' ,'Delhi' ,'Chennai', 'Jaipur',
+ 'Ahmedabad', 'Bangalore' ,'Pune'],label="Location"),
+        gr.Dropdown(choices=['POS' ,'Digital'],label="Purchase Category"),
+        gr.Number(label="Customer Age"),
+        gr.Dropdown(choices=['night', 'morning', 'afternoon', 'evening'],label="Time of Day")
+    ],
+    outputs=[
+        gr.Markdown(label="Fraud Detection Result"),
+        gr.Markdown(label="Fraud Type Result"),
+        gr.Plot(label="Probabilities Bar Chart")
+    ],
+    title="Fraud Detection & Fraud Type Prediction",
+    description="Enter transaction details to predict whether it is fraudulent and the type of fraud.",theme=gr.themes.Soft())
+
+# Launch Gradio Interface
+interface.launch()

card_type.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:1ff2b4f5e14406dbd6cb47c425241363e0e4ed4bbb79531559f4e1d06d655bcb
+size 270

fraud_type.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:1a65cf52537fa3ecbb1ef33fda5019ac91e91e72b9bc0e4e89ea9e730bab8e05
+size 63175

fraud_type_le.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:8ec57dc1c843288dbcf84da684832520133bbe179fb56fc345c3cd95d1eca405
+size 308

is_fraud.pkl

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+oid sha256:854025fd806b08c16b0bca1c9086e1f92c5b1bece7e7297b4ebd8a0695b5fb8d
+size 25783

location.pkl

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+oid sha256:ea694c94debf38073dccfe683ad081c2b0d045d6022ae2781f07315f8aa48f5e
+size 339

purchase_category.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:61987256eda305442de3f3251c1193e7fc508459e2f90d4fa956c053d668c07b
+size 258

time_of_day.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:036b2ad5a116b419e2ead409493db14d9a2c5da9b8976aa8156c0ba335d2578a
+size 282