| import streamlit as st |
| import numpy as np |
| import joblib |
| import os |
| import matplotlib.pyplot as plt |
| import pandas as pd |
| from sklearn.metrics import ( |
| ConfusionMatrixDisplay, |
| RocCurveDisplay, |
| roc_auc_score |
| ) |
|
|
| st.set_page_config(page_title="Credit Card Fraud Detection", layout="centered") |
|
|
| BASE_DIR = os.path.dirname(__file__) |
| MODEL_PATH = os.path.join(BASE_DIR, "model.pkl") |
| model = joblib.load(MODEL_PATH) |
|
|
| |
| X_test_path = os.path.join(BASE_DIR, "X_test.npy") |
| y_test_path = os.path.join(BASE_DIR, "y_test.npy") |
|
|
| if os.path.exists(X_test_path): |
| X_test = np.load(X_test_path) |
| y_test = np.load(y_test_path) |
|
|
| y_prob = model.predict_proba(X_test)[:, 1] |
| y_pred = model.predict(X_test) |
|
|
| auc = roc_auc_score(y_test, y_prob) |
| else: |
| X_test, y_test, y_prob, y_pred, auc = None, None, None, None, None |
|
|
| |
| st.title("💳 Credit Card Fraud Detection") |
| st.markdown("Real-time fraud detection using Machine Learning.") |
|
|
| threshold = st.slider("Fraud detection threshold", 0.0, 1.0, 0.5) |
|
|
| |
| tab1, tab2 = st.tabs(["🔍 Prediction", "📊 Model Insights"]) |
|
|
| |
| with tab1: |
|
|
| st.subheader("Transaction Input") |
|
|
| if st.button("🎲 Generate Random Transaction"): |
| time = np.random.randint(0, 172800) |
| amount = np.random.uniform(1, 5000) |
| pca_values = np.random.normal(0, 1, 28) |
|
|
| else: |
| col1, col2 = st.columns(2) |
|
|
| with col1: |
| time = st.number_input("Time", value=0) |
|
|
| with col2: |
| amount = st.number_input("Amount", value=0.0) |
|
|
| st.info("V1–V28 are PCA-transformed anonymized features.") |
|
|
| pca_values = [] |
| with st.expander("PCA Features"): |
| for i in range(28): |
| pca_values.append(st.number_input(f"V{i+1}", value=0.0)) |
|
|
| features = np.array([[time] + list(pca_values) + [amount]]) |
|
|
| if st.button("Predict"): |
|
|
| with st.spinner("Analyzing transaction..."): |
| prob = model.predict_proba(features)[0][1] |
| pred = int(prob >= threshold) |
|
|
| st.markdown("## Result") |
|
|
| col1, col2 = st.columns(2) |
|
|
| with col1: |
| if pred == 1: |
| st.error("🚨 Fraudulent Transaction") |
| else: |
| st.success("✅ Legitimate Transaction") |
|
|
| with col2: |
| st.metric("Fraud Probability", f"{prob:.4f}") |
|
|
| st.progress(prob) |
|
|
| |
| report = pd.DataFrame({ |
| "Time": [time], |
| "Amount": [amount], |
| "Fraud Probability": [prob], |
| "Prediction": [pred] |
| }) |
|
|
| st.download_button( |
| "📥 Download prediction report", |
| report.to_csv(index=False), |
| "prediction.csv" |
| ) |
|
|
| |
| with tab2: |
|
|
| st.subheader("Model Performance") |
|
|
| if X_test is None: |
| st.info("Upload X_test.npy and y_test.npy to enable insights.") |
|
|
| else: |
| st.metric("AUC", round(auc, 3)) |
|
|
| fig, ax = plt.subplots() |
| RocCurveDisplay.from_predictions(y_test, y_prob, ax=ax) |
| st.pyplot(fig) |
|
|
| fig2, ax2 = plt.subplots() |
| ConfusionMatrixDisplay.from_predictions(y_test, y_pred, ax=ax2) |
| st.pyplot(fig2) |
|
|
| if hasattr(model, "feature_importances_"): |
|
|
| st.subheader("Feature Importance") |
|
|
| fig3, ax3 = plt.subplots() |
| ax3.bar(range(len(model.feature_importances_)), model.feature_importances_) |
| ax3.set_title("Feature Importance") |
| st.pyplot(fig3) |
