File size: 3,511 Bytes
fd20d60 90874d3 fd20d60 1496213 fd20d60 1496213 fd20d60 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 |
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')
if is_fraud_class==1:
# 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}% Confidence): {'Fraudulent' if is_fraud_class == 1 else 'Not Fraudulent'}", f"Predicted Fraud Type: {fraud_type_class}" if fraud_type_class==1 else "", 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="By detecting fraudulent transactions, this model helps protect users and businesses in the Indian digital landscape, fostering a safer and more trustworthy online environment.",theme=gr.themes.Soft())
# Launch Gradio Interface
interface.launch()
|