Ayomide
80 lines
2.2 KiB
Python
80 lines
2.2 KiB
Python
from flask import Flask, render_template, request, jsonify
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import joblib
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import pandas as pd
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import numpy as np
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from datetime import datetime
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app = Flask(__name__)
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# Load the model
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try:
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model = joblib.load('models/fraud_model.pkl')
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except Exception as e:
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print(f"Error loading model: {e}")
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raise
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def preprocess_input(data):
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# Convert to DataFrame
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df = pd.DataFrame([data])
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# Convert numeric fields explicitly
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numeric_fields = ['amt', 'city_pop', 'lat', 'long', 'merch_lat', 'merch_long']
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for field in numeric_fields:
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df[field] = pd.to_numeric(df[field], errors='coerce')
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# Convert transaction time to datetime
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df['trans_date_trans_time'] = pd.to_datetime(df['trans_date_trans_time'])
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# Extract time features
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df['hour'] = df['trans_date_trans_time'].dt.hour
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df['day_of_week'] = df['trans_date_trans_time'].dt.dayofweek
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df['month'] = df['trans_date_trans_time'].dt.month
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# Calculate age from dob
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df['dob'] = pd.to_datetime(df['dob'])
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df['age'] = (pd.to_datetime('today') - df['dob']).dt.days // 365
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# Calculate distance between user and merchant
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df['distance'] = np.sqrt(
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(df['lat'].astype(float) - df['merch_lat'].astype(float))**2 +
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(df['long'].astype(float) - df['merch_long'].astype(float))**2
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)
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# Drop unnecessary columns
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return df.drop(['trans_date_trans_time', 'dob'], axis=1, errors='ignore')
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@app.route('/')
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def home():
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return render_template('index.html')
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@app.route('/predict', methods=['POST'])
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def predict():
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try:
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# Get data from form
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data = request.form.to_dict()
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# Preprocess the input
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processed_data = preprocess_input(data)
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# Make prediction
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prediction = model.predict(processed_data)
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probability = model.predict_proba(processed_data)[0][1]
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result = {
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'prediction': int(prediction[0]),
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'probability': float(probability),
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'is_fraud': bool(prediction[0])
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}
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return render_template('index.html', prediction=result)
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except Exception as e:
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return jsonify({'error': str(e)}), 400
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if __name__ == '__main__':
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app.run(debug=True)
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