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| import streamlit as st | |
| import os | |
| from dotenv import load_dotenv | |
| # Load environment variables from .env file | |
| load_dotenv() | |
| st.set_page_config( | |
| page_title=" Credit Card & Transaction Fraud Detection", | |
| layout="wide", | |
| page_icon="🛡️", | |
| initial_sidebar_state="expanded" | |
| ) | |
| import pandas as pd | |
| import numpy as np | |
| import xgboost as xgb | |
| import lightgbm as lgb | |
| import time | |
| import shap | |
| import matplotlib.pyplot as plt | |
| import plotly.express as px | |
| import plotly.graph_objects as go | |
| from plotly.subplots import make_subplots | |
| import datetime | |
| import base64 | |
| import yagmail | |
| import seaborn as sns | |
| import warnings | |
| warnings.filterwarnings('ignore') | |
| from sklearn.ensemble import (RandomForestClassifier, IsolationForest, GradientBoostingClassifier, VotingClassifier) | |
| from sklearn.linear_model import LogisticRegression | |
| from sklearn.preprocessing import StandardScaler, RobustScaler | |
| from sklearn.model_selection import train_test_split, cross_val_score | |
| from sklearn.metrics import (classification_report, confusion_matrix, roc_auc_score, precision_recall_curve, roc_curve) | |
| from sklearn.cluster import DBSCAN, KMeans | |
| from sklearn.decomposition import PCA | |
| from sklearn.feature_selection import SelectKBest, f_classif | |
| from sklearn.svm import OneClassSVM | |
| from imblearn.over_sampling import SMOTE | |
| from scipy import stats | |
| import joblib | |
| import hashlib | |
| import json | |
| from datetime import timedelta | |
| import sqlite3 | |
| import pytz | |
| # ------------------- Timezone Configuration ------------------- | |
| india = pytz.timezone('Asia/Kolkata') | |
| current_time = datetime.datetime.now(india).strftime('%Y-%m-%d %H:%M:%S') | |
| # ------------------- Session State Initialization ------------------- | |
| if 'authenticated' not in st.session_state: | |
| st.session_state.authenticated = False | |
| if 'user_role' not in st.session_state: | |
| st.session_state.user_role = None | |
| if 'username' not in st.session_state: | |
| st.session_state.username = None | |
| if 'show_users' not in st.session_state: | |
| st.session_state.show_users = False | |
| if 'adding_user' not in st.session_state: | |
| st.session_state.adding_user = False | |
| if 'registering' not in st.session_state: | |
| st.session_state.registering = False | |
| if 'login_time' not in st.session_state: | |
| st.session_state.login_time = None | |
| if 'locked_until' not in st.session_state: | |
| st.session_state.locked_until = None | |
| if 'login_attempts' not in st.session_state: | |
| st.session_state.login_attempts = 0 | |
| if 'show_edit_form' not in st.session_state: | |
| st.session_state.show_edit_form = False | |
| if 'selected_user' not in st.session_state: | |
| st.session_state.selected_user = None | |
| # ------------------- Role Permissions ------------------- | |
| role_permissions = { | |
| "User": ["Read"], | |
| "Manager": ["Read", "Read/Write"], | |
| "Admin": ["Read", "Read/Write", "Full"] | |
| } | |
| # ------------------- Authentication Functions ------------------- | |
| def hash_password(password): | |
| return hashlib.sha256(password.encode()).hexdigest() | |
| def verify_password(stored_hash, input_password): | |
| return stored_hash == hash_password(input_password) | |
| def get_users_file_path(): | |
| return os.path.join(os.path.dirname(__file__), 'data', 'users.csv') | |
| def load_users(): | |
| users_file = get_users_file_path() | |
| if os.path.exists(users_file): | |
| return pd.read_csv(users_file) | |
| return pd.DataFrame(columns=['Username', 'Password', 'Email', 'Role', 'Last_Login', 'Status', 'Permissions', 'Department']) | |
| def save_users_data(df): | |
| try: | |
| file_path = get_users_file_path() | |
| os.makedirs(os.path.dirname(file_path), exist_ok=True) | |
| df.to_csv(file_path, index=False) | |
| return True | |
| except Exception as e: | |
| st.error(f"Error saving user data: {str(e)}") | |
| return False | |
| def authenticate_user(username, password): | |
| users_df = load_users() | |
| user = users_df[users_df['Username'] == username] | |
| if len(user) == 0: | |
| return False, None | |
| stored_hash = user.iloc[0]['Password'] | |
| if verify_password(stored_hash, password): | |
| return True, user.iloc[0]['Role'] | |
| return False, None | |
| def update_last_login(username): | |
| try: | |
| users_df = load_users() | |
| current_time = datetime.datetime.now(india).strftime('%Y-%m-%d %H:%M:%S') | |
| users_df.loc[users_df['Username'] == username, 'Last_Login'] = current_time | |
| save_users_data(users_df) | |
| return True | |
| except Exception as e: | |
| st.error(f"Error updating last login: {str(e)}") | |
| return False | |
| def check_session_expired(): | |
| if not st.session_state.get('login_time'): | |
| return | |
| current_time = datetime.datetime.now(india) | |
| session_duration = current_time - st.session_state.login_time | |
| if session_duration.total_seconds() > 3600: | |
| st.session_state.authenticated = False | |
| st.session_state.user_role = None | |
| st.session_state.username = None | |
| st.session_state.login_time = None | |
| st.warning("🔒 Your session has expired. Please log in again.") | |
| st.stop() | |
| elif session_duration.total_seconds() > 3300: | |
| st.warning("⚠️ Your session will expire in 5 minutes. Please save your work.") | |
| st.markdown('<h1 class="main-header">💳 Credit Card & Transaction Fraud Detection System</h1>', unsafe_allow_html=True) | |
| # Enhanced Authentication | |
| st.sidebar.markdown("### 🔐 System Access Control") | |
| # ------------------- Registration Form (Single Instance) ------------------- | |
| if not st.session_state.get('authenticated', False): | |
| st.sidebar.markdown("---") | |
| if st.sidebar.button("📝 New User? Register Here", key="register_sidebar"): | |
| st.session_state.registering = True | |
| st.rerun() | |
| if st.session_state.get('registering', False) and not st.session_state.get('authenticated', False): | |
| st.markdown("### 📝 New User Registration") | |
| with st.form("registration_form"): | |
| new_username = st.text_input("Username*") | |
| new_email = st.text_input("Email*") | |
| new_password = st.text_input("Password*", type="password") | |
| confirm_password = st.text_input("Confirm Password*", type="password") | |
| new_department = st.selectbox("Department*", ["General", "Risk", "Compliance", "IT", "Operations"], key="register_department") | |
| col1, col2 = st.columns(2) | |
| with col1: | |
| if st.form_submit_button("Register"): | |
| if not new_username or not new_email or not new_password: | |
| st.error("Please fill all required fields") | |
| elif new_password != confirm_password: | |
| st.error("Passwords do not match") | |
| else: | |
| users_df = load_users() | |
| if new_username in users_df['Username'].values: | |
| st.error("Username already exists!") | |
| else: | |
| new_user = pd.DataFrame([{ | |
| 'Username': new_username, | |
| 'Password': hash_password(new_password), | |
| 'Email': new_email, | |
| 'Role': 'User', | |
| 'Last_Login': 'Never', | |
| 'Status': 'Active', | |
| 'Permissions': 'Read', | |
| 'Department': new_department | |
| }]) | |
| users_df = pd.concat([users_df, new_user], ignore_index=True) | |
| if save_users_data(users_df): | |
| st.success("✅ Registration successful! Please login.") | |
| time.sleep(2) | |
| st.session_state.registering = False | |
| st.rerun() | |
| else: | |
| st.error("Failed to save user data") | |
| with col2: | |
| if st.form_submit_button("Cancel"): | |
| st.session_state.registering = False | |
| st.rerun() | |
| # ------------------- Login Logic ------------------- | |
| available_roles = ["User", "Manager", "Admin"] | |
| selected_role = st.sidebar.selectbox("👥 Select Role", available_roles, key="first_role_select") | |
| username = st.sidebar.text_input("👤 Username", key="first_username") | |
| password = st.sidebar.text_input("🔑 Password", type="password", key="first_password") | |
| if st.sidebar.button("🔑 Login", key="first_login_button"): | |
| if username and password: | |
| authenticated, user_role = authenticate_user(username, password) | |
| if authenticated and user_role == selected_role: | |
| st.session_state.authenticated = True | |
| st.session_state.user_role = user_role | |
| st.session_state.username = username | |
| st.session_state.login_time = datetime.datetime.now(india) | |
| st.session_state.login_attempts = 0 | |
| if update_last_login(username): | |
| st.success(f"✅ Welcome {username}! Authenticated as {user_role}") | |
| else: | |
| st.warning("Login successful but failed to update last login time") | |
| st.rerun() | |
| else: | |
| st.session_state.login_attempts += 1 | |
| remaining_attempts = 3 - st.session_state.login_attempts | |
| if remaining_attempts > 0: | |
| st.error(f"🚫 Invalid credentials or role mismatch. {remaining_attempts} attempts remaining.") | |
| else: | |
| current_time = datetime.datetime.now(india) | |
| st.session_state.locked_until = current_time + datetime.timedelta(minutes=5) | |
| st.error("🔒 Too many failed attempts. Account locked for 5 minutes.") | |
| st.session_state.login_attempts = 0 | |
| st.stop() | |
| else: | |
| st.error("⚠️ Please enter both username and password") | |
| if st.session_state.get('authenticated', False): | |
| st.sidebar.success(f"Logged in as: {st.session_state.username} ({st.session_state.user_role})") | |
| if st.sidebar.button("Logout", key="logout_button"): | |
| st.session_state.authenticated = False | |
| st.session_state.user_role = None | |
| st.session_state.username = None | |
| st.session_state.login_time = None | |
| st.rerun() | |
| # ------------------- Permission Check Function ------------------- | |
| def check_permission(required_permission): | |
| permission_levels = { | |
| 'User': 0, | |
| 'Read': 1, | |
| 'Read/Write': 2, | |
| 'Full': 3 | |
| } | |
| user_permission = st.session_state.get('user_permission', st.session_state.get('user_role', 'User')) | |
| return permission_levels.get(user_permission, 0) >= permission_levels.get(required_permission, 0) | |
| # ------------------- Main App Logic (Only if Authenticated) ------------------- | |
| if not st.session_state.get('authenticated', False): | |
| st.warning("🔒 Please log in to access the system") | |
| st.stop() | |
| else: | |
| # Advanced Feature Engineering Class | |
| class AdvancedFeatureEngineering: | |
| def __init__(self): | |
| self.scaler = RobustScaler() | |
| self.pca = PCA(n_components=5) | |
| self.transaction_history = [] | |
| def create_velocity_features(self, transaction_data, user_history): | |
| """Create velocity-based features""" | |
| features = {} | |
| # Transaction velocity (last hour, 24h, 7 days) | |
| now = datetime.datetime.now(india) | |
| hour_ago = now - timedelta(hours=1) | |
| day_ago = now - timedelta(days=1) | |
| week_ago = now - timedelta(days=7) | |
| features['txn_last_hour'] = len([t for t in user_history if t['timestamp'] > hour_ago]) | |
| features['txn_last_day'] = len([t for t in user_history if t['timestamp'] > day_ago]) | |
| features['txn_last_week'] = len([t for t in user_history if t['timestamp'] > week_ago]) | |
| # Amount velocity | |
| hour_amounts = [t['amount'] for t in user_history if t['timestamp'] > hour_ago] | |
| features['amount_velocity_1h'] = sum(hour_amounts) if hour_amounts else 0 | |
| day_amounts = [t['amount'] for t in user_history if t['timestamp'] > day_ago] | |
| features['amount_velocity_24h'] = sum(day_amounts) if day_amounts else 0 | |
| return features | |
| def create_behavioral_features(self, transaction_data, user_profile): | |
| """Create behavioral deviation features""" | |
| features = {} | |
| # Convert amount_log back to amount for deviation calculation | |
| amount = np.exp(transaction_data.get('amount_log', 0)) - 1 | |
| # Deviation from user's normal patterns | |
| features['amount_deviation'] = abs(amount - user_profile.get('avg_amount', 0)) | |
| features['hour_deviation'] = abs(transaction_data.get('hour', 0) - user_profile.get('usual_hour', 12)) | |
| features['merchant_familiarity'] = 1 if transaction_data.get('category', '') in user_profile.get('frequent_categories', []) else 0 | |
| # Geographic features | |
| features['location_risk'] = np.random.uniform(0, 1) # Mock risk score | |
| features['distance_from_home'] = transaction_data.get('location_distance', 0) | |
| return features | |
| def create_time_features(self, timestamp): | |
| """Advanced time-based features""" | |
| features = {} | |
| features['hour_sin'] = np.sin(2 * np.pi * timestamp.hour / 24) | |
| features['hour_cos'] = np.cos(2 * np.pi * timestamp.hour / 24) | |
| features['day_sin'] = np.sin(2 * np.pi * timestamp.weekday() / 7) | |
| features['day_cos'] = np.cos(2 * np.pi * timestamp.weekday() / 7) | |
| features['month_sin'] = np.sin(2 * np.pi * timestamp.month / 12) | |
| features['month_cos'] = np.cos(2 * np.pi * timestamp.month / 12) | |
| # Business hours indicator | |
| features['business_hours'] = 1 if 9 <= timestamp.hour <= 17 else 0 | |
| features['late_night'] = 1 if timestamp.hour >= 23 or timestamp.hour <= 5 else 0 | |
| return features | |
| # Advanced Ensemble Model Class | |
| class AdvancedEnsembleModel: | |
| def __init__(self): | |
| self.models = {} | |
| self.weights = {} | |
| self.anomaly_detectors = {} | |
| self.is_trained = False | |
| def initialize_models(self): | |
| """Initialize ensemble of models""" | |
| self.models = { | |
| 'xgb': xgb.XGBClassifier( | |
| n_estimators=200, | |
| max_depth=6, | |
| learning_rate=0.1, | |
| subsample=0.8, | |
| colsample_bytree=0.8, | |
| random_state=42 | |
| ), | |
| 'lgb': lgb.LGBMClassifier( | |
| n_estimators=200, | |
| max_depth=6, | |
| learning_rate=0.1, | |
| subsample=0.8, | |
| colsample_bytree=0.8, | |
| random_state=42, | |
| verbose=-1 | |
| ), | |
| 'rf': RandomForestClassifier( | |
| n_estimators=100, | |
| max_depth=10, | |
| random_state=42 | |
| ), | |
| 'gb': GradientBoostingClassifier( | |
| n_estimators=100, | |
| max_depth=6, | |
| learning_rate=0.1, | |
| random_state=42 | |
| ) | |
| } | |
| # Anomaly detection models | |
| self.anomaly_detectors = { | |
| 'isolation_forest': IsolationForest(contamination=0.1, random_state=42), | |
| 'one_class_svm': OneClassSVM(nu=0.1), | |
| 'dbscan': DBSCAN(eps=0.5, min_samples=5) | |
| } | |
| def predict_ensemble(self, X): | |
| """Advanced ensemble prediction with multiple strategies""" | |
| if not self.is_trained: | |
| # Mock training for demonstration | |
| self.weights = {'xgb': 0.4, 'lgb': 0.3, 'rf': 0.2, 'gb': 0.1} | |
| self.is_trained = True | |
| # Simulate predictions (in real implementation, use trained models) | |
| predictions = {} | |
| probabilities = {} | |
| for name in self.models.keys(): | |
| # Mock predictions for demonstration | |
| prob = np.random.beta(2, 5) # Skewed towards low fraud probability | |
| predictions[name] = 1 if prob > 0.5 else 0 | |
| probabilities[name] = prob | |
| # Weighted ensemble | |
| weighted_prob = sum(probabilities[name] * self.weights[name] | |
| for name in probabilities.keys()) | |
| # Anomaly detection | |
| anomaly_scores = {} | |
| for name, detector in self.anomaly_detectors.items(): | |
| # Mock anomaly score | |
| anomaly_scores[name] = np.random.uniform(-1, 1) | |
| return { | |
| 'ensemble_probability': weighted_prob, | |
| 'individual_predictions': predictions, | |
| 'individual_probabilities': probabilities, | |
| 'anomaly_scores': anomaly_scores | |
| } | |
| # Risk Scoring Engine | |
| class RiskScoringEngine: | |
| def __init__(self): | |
| self.risk_factors = { | |
| 'amount': {'weight': 0.25, 'threshold': 1000}, | |
| 'velocity': {'weight': 0.20, 'threshold': 5}, | |
| 'location': {'weight': 0.15, 'threshold': 0.7}, | |
| 'behavioral': {'weight': 0.20, 'threshold': 0.6}, | |
| 'temporal': {'weight': 0.10, 'threshold': 0.5}, | |
| 'anomaly': {'weight': 0.10, 'threshold': 0.3} | |
| } | |
| def calculate_risk_score(self, features, model_output): | |
| """Calculate comprehensive risk score""" | |
| risk_components = {} | |
| # Amount risk | |
| amount_risk = min(features.get('amount_log', 0) / 10, 1.0) | |
| risk_components['amount'] = amount_risk | |
| # Velocity risk | |
| velocity_risk = min(features.get('txn_last_hour', 0) / 10, 1.0) | |
| risk_components['velocity'] = velocity_risk | |
| # Location risk | |
| location_risk = features.get('location_risk', 0) | |
| risk_components['location'] = location_risk | |
| # Behavioral risk | |
| behavioral_risk = features.get('amount_deviation', 0) / 1000 | |
| risk_components['behavioral'] = min(behavioral_risk, 1.0) | |
| # Temporal risk | |
| temporal_risk = 1.0 if features.get('late_night', 0) else 0.3 | |
| risk_components['temporal'] = temporal_risk | |
| # Anomaly risk | |
| anomaly_risk = max(model_output.get('anomaly_scores', {}).values()) if model_output.get('anomaly_scores') else 0 | |
| risk_components['anomaly'] = min(abs(anomaly_risk), 1.0) | |
| # Calculate weighted risk score | |
| total_risk = sum( | |
| risk_components[factor] * self.risk_factors[factor]['weight'] | |
| for factor in risk_components.keys() | |
| ) | |
| return { | |
| 'total_risk_score': total_risk, | |
| 'risk_components': risk_components, | |
| 'risk_level': self.get_risk_level(total_risk) | |
| } | |
| def get_risk_level(self, score): | |
| """Determine risk level based on score""" | |
| if score >= 0.8: | |
| return "CRITICAL" | |
| elif score >= 0.6: | |
| return "HIGH" | |
| elif score >= 0.4: | |
| return "MEDIUM" | |
| elif score >= 0.2: | |
| return "LOW" | |
| else: | |
| return "MINIMAL" | |
| # Advanced Alert System | |
| class AdvancedAlertSystem: | |
| def __init__(self): | |
| self.alert_rules = { | |
| 'CRITICAL': {'email': True, 'sms': True, 'webhook': True, 'block': True}, | |
| 'HIGH': {'email': True, 'sms': False, 'webhook': True, 'block': False}, | |
| 'MEDIUM': {'email': True, 'sms': False, 'webhook': False, 'block': False}, | |
| 'LOW': {'email': False, 'sms': False, 'webhook': False, 'block': False} | |
| } | |
| def process_alert(self, risk_data, transaction_data): | |
| """Process alerts based on risk level""" | |
| risk_level = risk_data['risk_level'] | |
| actions = self.alert_rules.get(risk_level, {}) | |
| alert_info = { | |
| 'timestamp': datetime.datetime.now(india), | |
| 'risk_level': risk_level, | |
| 'risk_score': risk_data['total_risk_score'], | |
| 'transaction_id': self.generate_transaction_id(transaction_data), | |
| 'actions_taken': [] | |
| } | |
| if actions.get('email'): | |
| alert_info['actions_taken'].append('Email sent') | |
| if actions.get('block'): | |
| alert_info['actions_taken'].append('Transaction blocked') | |
| return alert_info | |
| def generate_transaction_id(self, transaction_data): | |
| """Generate unique transaction ID""" | |
| data_string = json.dumps(transaction_data, sort_keys=True, default=str) | |
| return hashlib.md5(data_string.encode()).hexdigest()[:12] | |
| # Initialize advanced components | |
| def initialize_advanced_system(): | |
| feature_engineer = AdvancedFeatureEngineering() | |
| ensemble_model = AdvancedEnsembleModel() | |
| risk_engine = RiskScoringEngine() | |
| alert_system = AdvancedAlertSystem() | |
| ensemble_model.initialize_models() | |
| return feature_engineer, ensemble_model, risk_engine, alert_system | |
| # Load advanced system | |
| feature_engineer, ensemble_model, risk_engine, alert_system = initialize_advanced_system() | |
| # Enhanced Email Alert Function | |
| def send_advanced_email_alert(to_email, transaction_data, risk_data, model_output): | |
| """Send advanced email alert with detailed risk analysis""" | |
| risk_level = risk_data['risk_level'] | |
| risk_score = risk_data['total_risk_score'] | |
| subject = f"🚨 {risk_level} RISK ALERT: Fraud Detection System" | |
| # Create detailed risk breakdown | |
| risk_breakdown = "\n".join([ | |
| f" • {factor.title()}: {score:.2%}" | |
| for factor, score in risk_data['risk_components'].items() | |
| ]) | |
| model_breakdown = "\n".join([ | |
| f" • {model.upper()}: {prob:.2%}" | |
| for model, prob in model_output['individual_probabilities'].items() | |
| ]) | |
| content = f""" | |
| 🔒 ADVANCED FRAUD DETECTION ALERT | |
| Risk Level: {risk_level} | |
| Overall Risk Score: {risk_score:.2%} | |
| 📊 TRANSACTION DETAILS: | |
| • Amount: ${np.exp(transaction_data.get('amount_log', 0)) - 1:.2f} | |
| • Category: {transaction_data.get('category', 'Unknown')} | |
| • Time: Hour {transaction_data.get('hour', 0)} | |
| • Location Distance: {transaction_data.get('location_distance', 0):.2f} miles | |
| 🎯 RISK COMPONENT BREAKDOWN: | |
| {risk_breakdown} | |
| 🤖 MODEL ENSEMBLE RESULTS: | |
| {model_breakdown} | |
| • Ensemble Prediction: {model_output['ensemble_probability']:.2%} | |
| 🚨 RECOMMENDED ACTIONS: | |
| • Immediate investigation required for {risk_level} risk transactions | |
| • Consider temporary hold on similar transaction patterns | |
| • Review customer's recent transaction history | |
| Generated at timezone India ('Asia/Kolkata'): {datetime.datetime.now(india).strftime('%Y-%m-%d %H:%M:%S')} | |
| Best regards, | |
| Advanced AI Fraud Detection System | |
| """ | |
| try: | |
| yag = yagmail.SMTP(user="sanjay.dev925@gmail.com", password="splh yrfu ebuq ghve") | |
| yag.send(to=to_email, subject=subject, contents=content) | |
| return True | |
| except Exception as e: | |
| st.error(f"Failed to send email alert: {e}") | |
| return False | |
| # Add this after imports | |
| def setup_email_config(): | |
| """Setup email configuration using Streamlit secrets""" | |
| try: | |
| if not st.secrets.get("email"): | |
| st.error("Email configuration not found in secrets") | |
| return None | |
| return { | |
| "smtp_user": st.secrets.email.SMTP_USER, | |
| "smtp_password": st.secrets.email.SMTP_PASSWORD, | |
| "smtp_server": st.secrets.email.SMTP_SERVER, | |
| "smtp_port": st.secrets.email.SMTP_PORT | |
| } | |
| except Exception as e: | |
| st.error(f"Error loading email configuration: {str(e)}") | |
| return None | |
| def send_fraud_alert_email(user_email, transaction_details, risk_level): | |
| """Send fraud alert email with proper SSL configuration""" | |
| try: | |
| # Initialize yagmail with SSL settings | |
| yag = yagmail.SMTP( | |
| user="sanjay.dev925@gmail.com", | |
| password="splh yrfu ebuq ghve", | |
| host='smtp.gmail.com', | |
| smtp_ssl=True, # Enable SSL | |
| port=465 # Use SSL port instead of 587 | |
| ) | |
| subject = f"🚨 FRAUD ALERT: {risk_level} Risk Detected" | |
| body = f""" | |
| 🔒 Fraud Detection Alert | |
| Risk Level: {risk_level} | |
| Transaction Details: | |
| • Amount: {transaction_details['amount']} | |
| • Time: {transaction_details['timestamp']} | |
| • Location: {transaction_details['location']} | |
| • Risk Score: {transaction_details['risk_score']} | |
| Please review this transaction immediately. | |
| """ | |
| # Send email with error handling | |
| try: | |
| yag.send( | |
| to=user_email, | |
| subject=subject, | |
| contents=body | |
| ) | |
| st.success(f"🚨 Alert email sent successfully to {user_email}") | |
| return True, "Email sent successfully" | |
| except Exception as e: | |
| st.error(f"Failed to send email: {str(e)}") | |
| return False, f"Failed to send email: {str(e)}" | |
| finally: | |
| yag.close() | |
| except Exception as e: | |
| st.error(f"Email configuration error: {str(e)}") | |
| return False, f"Email configuration error: {str(e)}" | |
| # Modify the risk analysis section to include email alerts | |
| def process_transaction_with_alerts(user_email, transaction_data, risk_data): | |
| """Process transaction and send alerts if necessary""" | |
| risk_level = risk_data['risk_level'] | |
| # Define risk thresholds for alerts | |
| alert_thresholds = { | |
| 'CRITICAL': 0.9, | |
| 'HIGH': 0.7, | |
| 'MEDIUM': 0.5 | |
| } | |
| # Check if alert should be sent | |
| if risk_data['total_risk_score'] >= alert_thresholds.get(risk_level, 0): | |
| # Prepare transaction details for email | |
| email_transaction_details = { | |
| 'amount': transaction_data.get('amount', 'N/A'), | |
| 'timestamp': datetime.datetime.now(india).strftime('%Y-%m-%d %H:%M:%S'), | |
| 'location': transaction_data.get('location', 'Unknown'), | |
| 'risk_score': f"{risk_data['total_risk_score']:.2%}" | |
| } | |
| # Send alert email | |
| success, message = send_fraud_alert_email( | |
| user_email, | |
| email_transaction_details, | |
| risk_level | |
| ) | |
| if success: | |
| st.success(f"🚨 Alert email sent to {user_email}") | |
| else: | |
| st.error(f"❌ Failed to send alert: {message}") | |
| # Streamlit App Configuration | |
| # Custom CSS for enhanced UI | |
| st.markdown(""" | |
| <style> | |
| .main-header { | |
| font-size: 2.5rem; | |
| font-weight: bold; | |
| text-align: center; | |
| background: linear-gradient(90deg, #667eea 0%, #764ba2 100%); | |
| -webkit-background-clip: text; | |
| -webkit-text-fill-color: transparent; | |
| margin-bottom: 2rem; | |
| } | |
| .risk-critical { | |
| border-left: 5px solid #ffff00; | |
| padding: 1rem; | |
| border-radius: 5px; | |
| } | |
| .risk-high { | |
| border-left: 5px solid #ff0000; | |
| padding: 1rem; | |
| border-radius: 5px; | |
| } | |
| .risk-medium { | |
| border-left: 5px solid #4caf50; | |
| padding: 1rem; | |
| border-radius: 5px; | |
| } | |
| </style> | |
| """, unsafe_allow_html=True) | |
| def update_last_login(username): | |
| """Update user's last login time""" | |
| try: | |
| users_df = load_users() | |
| current_time = datetime.datetime.now(india).strftime('%Y-%m-%d %H:%M:%S') | |
| users_df.loc[users_df['Username'] == username, 'Last_Login'] = current_time | |
| save_users_data(users_df) | |
| return True | |
| except Exception as e: | |
| st.error(f"Error updating last login: {str(e)}") | |
| return False | |
| # Add Register button in sidebar before login | |
| if not st.session_state.get('authenticated', False): | |
| st.sidebar.markdown("---") | |
| if st.sidebar.button("📝 New User? Register Here", key="register_sidebar_second"): | |
| st.session_state.registering = True | |
| st.rerun() | |
| # Registration form | |
| if st.session_state.get('registering', False) and not st.session_state.get('authenticated', False): | |
| st.markdown("### 📝 New User Registration") | |
| with st.form("registration_form"): | |
| new_username = st.text_input("Username*") | |
| new_email = st.text_input("Email*") | |
| new_password = st.text_input("Password*", type="password") | |
| confirm_password = st.text_input("Confirm Password*", type="password") | |
| new_department = st.selectbox("Department*", | |
| ["General", "Risk", "Compliance", "IT", "Operations"], key="register_department_second") | |
| col1, col2 = st.columns(2) | |
| with col1: | |
| if st.form_submit_button("Register"): | |
| if not new_username or not new_email or not new_password: | |
| st.error("Please fill all required fields") | |
| elif new_password != confirm_password: | |
| st.error("Passwords do not match") | |
| else: | |
| # Load existing users or create new file | |
| try: | |
| users_df = load_users() | |
| except: | |
| users_df = pd.DataFrame(columns=[ | |
| 'Username', 'Password', 'Email', 'Role', | |
| 'Last_Login', 'Status', 'Permissions', 'Department' | |
| ]) | |
| # Check if username exists | |
| if new_username in users_df['Username'].values: | |
| st.error("Username already exists!") | |
| else: | |
| # Create new user with User role | |
| new_user = pd.DataFrame([{ | |
| 'Username': new_username, | |
| 'Password': hash_password(new_password), | |
| 'Email': new_email, | |
| 'Role': 'User', | |
| 'Last_Login': 'Never', | |
| 'Status': 'Active', | |
| 'Permissions': 'Read', | |
| 'Department': new_department | |
| }]) | |
| # Add new user and save | |
| users_df = pd.concat([users_df, new_user], ignore_index=True) | |
| if save_users_data(users_df): | |
| st.success("✅ Registration successful! Please login.") | |
| time.sleep(2) | |
| st.session_state.registering = False | |
| st.rerun() | |
| else: | |
| st.error("Failed to save user data") | |
| with col2: | |
| if st.form_submit_button("Cancel"): | |
| st.session_state.registering = False | |
| st.rerun() | |
| # Add this before showing any data/content | |
| if not st.session_state.get('authenticated', False): | |
| st.warning("🔒 Please log in to access the system") | |
| st.stop() | |
| else: | |
| # Load necessary data after authentication | |
| users_df = load_users() # <-- FIXED: was load_users_data() | |
| check_session_expired() | |
| # Show role-specific content | |
| if st.session_state.get('user_role') == 'Admin': | |
| # Show admin content | |
| pass | |
| elif st.session_state.get('user_role') == 'Manager': | |
| # Show manager content | |
| pass | |
| elif st.session_state.get('user_role') == 'User': | |
| # Show user content | |
| pass | |
| else: | |
| # Show User user content | |
| pass | |
| # Advanced Input Panel | |
| st.sidebar.markdown("### 📋 Transaction Input Panel") | |
| # Primary transaction details | |
| amount = st.sidebar.slider("💰 Transaction Amount ($)", 1, 1000000, 100, format="$%d") | |
| hour = st.sidebar.slider("🕐 Hour of Transaction", 0, 23, 12) | |
| age = st.sidebar.slider("👤 Customer Age", 18, 100, 35) | |
| # Enhanced categorical inputs | |
| category = st.sidebar.selectbox("🏪 Merchant Category", | |
| ['gas_transport', 'grocery_pos', 'shopping_net', 'travel', | |
| 'misc_pos', 'health_fitness', 'entertainment', 'food_dining', | |
| 'bills_utilities', 'personal_care']) | |
| gender = st.sidebar.radio("⚧ Gender", ["M", "F", "Other"]) | |
| city_pop = st.sidebar.slider("🏙️ City Population", 100, 10000000, 50000) | |
| job = st.sidebar.selectbox("💼 Customer Job", | |
| ['Teacher', 'Engineer', 'Doctor', 'Lawyer', 'Nurse', 'Artist', | |
| 'Freelancer', 'Manager', 'Student', 'Retired']) | |
| transaction_date = st.sidebar.date_input("📅 Transaction Date", datetime.date.today()) | |
| # Advanced settings | |
| st.sidebar.markdown("### ⚙️ Advanced Settings") | |
| model_sensitivity = st.sidebar.slider("🎯 Model Sensitivity", 0.1, 1.0, 0.5, 0.1) | |
| enable_email = st.sidebar.checkbox("📧 Enable Email Alerts", value=True) | |
| enable_realtime = st.sidebar.checkbox("⚡ Real-time Processing", value=True) | |
| # Mock user profile and history | |
| user_profile = { | |
| 'avg_amount': np.random.uniform(50, 500), | |
| 'usual_hour': np.random.randint(8, 20), | |
| 'frequent_categories': np.random.choice( | |
| ['grocery_pos', 'gas_transport', 'shopping_net'], | |
| size=2, replace=False | |
| ).tolist() | |
| } | |
| user_history = [ | |
| { | |
| 'timestamp': datetime.datetime.now(india) - timedelta(hours=np.random.randint(1, 168)), | |
| 'amount': np.random.uniform(10, 1000), | |
| 'category': np.random.choice(['grocery_pos', 'gas_transport', 'shopping_net']) | |
| } | |
| for _ in range(np.random.randint(5, 20)) | |
| ] | |
| # Feature Engineering | |
| timestamp = datetime.datetime.combine(transaction_date, datetime.time(hour)) | |
| # User features | |
| cat_map = {v: i for i, v in enumerate(['gas_transport','grocery_pos','shopping_net','travel','misc_pos','health_fitness','entertainment', 'food_dining', 'bills_utilities', 'personal_care'])} | |
| gender_map = {'M': 0, 'F': 1, 'Other': 2} | |
| job_map = {v: i for i, v in enumerate(['Teacher', 'Engineer', 'Doctor', 'Lawyer', 'Nurse', 'Artist', 'Freelancer', 'Manager', 'Student', 'Retired'])} | |
| User_features = { | |
| "amount_log": np.log(amount + 1), | |
| "category": cat_map.get(category, 0), | |
| "gender": gender_map[gender], | |
| "city_pop_log": np.log(city_pop + 1), | |
| "job": job_map[job], | |
| "age": age, | |
| "hour": hour, | |
| "weekday": timestamp.weekday(), | |
| "is_weekend": 1 if timestamp.weekday() >= 5 else 0, | |
| "location_distance": round(np.random.uniform(0.1, 10.0), 2) | |
| } | |
| # Advanced feature engineering | |
| velocity_features = feature_engineer.create_velocity_features(User_features, user_history) | |
| behavioral_features = feature_engineer.create_behavioral_features(User_features, user_profile) | |
| time_features = feature_engineer.create_time_features(timestamp) | |
| # Combine all features | |
| all_features = {**User_features, **velocity_features, **behavioral_features, **time_features} | |
| # Create DataFrame for model input | |
| input_df = pd.DataFrame([all_features]) | |
| # Advanced Model Prediction | |
| model_output = ensemble_model.predict_ensemble(input_df) | |
| risk_data = risk_engine.calculate_risk_score(all_features, model_output) | |
| alert_info = alert_system.process_alert(risk_data, all_features) | |
| # Main Dashboard | |
| col1, col2, col3 = st.columns([2, 2, 1]) | |
| with col1: | |
| st.markdown("### 🎯 Fraud Detection Results") | |
| risk_level = risk_data['risk_level'] | |
| risk_score = risk_data['total_risk_score'] | |
| ensemble_prob = model_output['ensemble_probability'] | |
| user_email = users_df[users_df['Username'] == st.session_state.username]['Email'].iloc[0] | |
| # Dynamic risk display section | |
| if risk_level == "CRITICAL": | |
| st.markdown(f'<div class="risk-critical"><h3>🚨 CRITICAL RISK DETECTED</h3><p>Risk Score: {risk_score:.1%}</p><p>Ensemble Probability: {ensemble_prob:.1%}</p></div>', unsafe_allow_html=True) | |
| if enable_email: | |
| send_advanced_email_alert(user_email, all_features, risk_data, model_output) | |
| st.info("📧 Alert email sent due to CRITICAL risk level") | |
| else: | |
| st.info("📧 Email alerts are currently disabled") | |
| elif risk_level == "HIGH": | |
| st.markdown(f'<div class="risk-high"><h3>⚠️ HIGH RISK TRANSACTION</h3><p>Risk Score: {risk_score:.1%}</p><p>Ensemble Probability: {ensemble_prob:.1%}</p></div>', unsafe_allow_html=True) | |
| if enable_email: | |
| send_advanced_email_alert(user_email, all_features, risk_data, model_output) | |
| st.info("📧 Alert email sent due to HIGH risk level") | |
| else: | |
| st.info("📧 Email alerts are currently disabled") | |
| else: | |
| st.markdown(f'<div class="risk-medium"><h3>✅ {risk_level} RISK</h3><p>Risk Score: {risk_score:.1%}</p><p>Ensemble Probability: {ensemble_prob:.1%}</p></div>', unsafe_allow_html=True) | |
| # Model breakdown | |
| st.markdown("#### 🤖 Model Ensemble Breakdown") | |
| model_df = pd.DataFrame([ | |
| {'Model': model.upper(), 'Probability': f"{prob:.1%}", 'Prediction': 'Fraud' if pred else 'Legitimate'} | |
| for model, (prob, pred) in zip( | |
| model_output['individual_probabilities'].keys(), | |
| zip(model_output['individual_probabilities'].values(), | |
| model_output['individual_predictions'].values()) | |
| ) | |
| ]) | |
| st.dataframe(model_df, use_container_width=True) | |
| with col2: | |
| st.markdown("### 📊 Risk Analysis Dashboard") | |
| # Risk components radar chart | |
| risk_components = risk_data['risk_components'] | |
| fig_radar = go.Figure() | |
| fig_radar.add_trace(go.Scatterpolar( | |
| r=list(risk_components.values()), | |
| theta=list(risk_components.keys()), | |
| fill='toself', | |
| name='Risk Components' | |
| )) | |
| fig_radar.update_layout( | |
| polar=dict( | |
| radialaxis=dict(visible=True, range=[0, 1]) | |
| ), | |
| showlegend=False, | |
| title="Risk Component Analysis" | |
| ) | |
| st.plotly_chart(fig_radar, use_container_width=True) | |
| # Ensemble probability gauge | |
| fig_gauge = go.Figure(go.Indicator( | |
| mode="gauge+number+delta", | |
| value=ensemble_prob * 100, | |
| title={'text': "Ensemble Fraud Probability (%)"}, | |
| gauge={ | |
| 'axis': {'range': [0, 100]}, | |
| 'bar': {'color': "darkred"}, | |
| 'steps': [ | |
| {'range': [0, 25], 'color': "lightgreen"}, | |
| {'range': [25, 50], 'color': "yellow"}, | |
| {'range': [50, 75], 'color': "orange"}, | |
| {'range': [75, 100], 'color': "red"} | |
| ], | |
| 'threshold': { | |
| 'line': {'color': "red", 'width': 4}, | |
| 'thickness': 0.75, | |
| 'value': 90 | |
| } | |
| }, | |
| delta={'reference': 50} | |
| )) | |
| fig_gauge.update_layout(height=300) | |
| st.plotly_chart(fig_gauge, use_container_width=True) | |
| with col3: | |
| st.markdown("### 🚨 Alert Status") | |
| st.metric("Transaction ID", alert_info['transaction_id']) | |
| st.metric("Risk Level", risk_level) | |
| st.metric("Alert Time", alert_info['timestamp'].strftime('%H:%M:%S')) | |
| if alert_info['actions_taken']: | |
| st.markdown("**Actions Taken:**") | |
| for action in alert_info['actions_taken']: | |
| st.markdown(f"• {action}") | |
| else: | |
| st.markdown("**No actions required**") | |
| # Advanced Analytics Section | |
| st.markdown("---") | |
| st.markdown("### 📈 Advanced Analytics & Insights") | |
| tab1, tab2, tab3, tab4 = st.tabs(["🔍 Feature Analysis", "🎯 Model Performance", "📊 Transaction Patterns", "🛡️ Security Dashboard"]) | |
| with tab1: | |
| col1, col2 = st.columns(2) | |
| with col1: | |
| # Feature importance simulation | |
| feature_importance = { | |
| 'amount_log': 0.25, | |
| 'txn_last_hour': 0.18, | |
| 'location_distance': 0.15, | |
| 'amount_deviation': 0.12, | |
| 'hour': 0.10, | |
| 'age': 0.08, | |
| 'category': 0.07, | |
| 'late_night': 0.05 | |
| } | |
| fig_importance = px.bar( | |
| x=list(feature_importance.values()), | |
| y=list(feature_importance.keys()), | |
| orientation='h', | |
| title="Top Feature Importance", | |
| labels={'x': 'Importance Score', 'y': 'Features'} | |
| ) | |
| st.plotly_chart(fig_importance, use_container_width=True) | |
| with col2: | |
| # SHAP values simulation | |
| shap_values = { | |
| 'amount_log': np.random.uniform(-0.1, 0.3), | |
| 'txn_last_hour': np.random.uniform(-0.05, 0.2), | |
| 'location_distance': np.random.uniform(-0.02, 0.15), | |
| 'hour': np.random.uniform(-0.1, 0.1), | |
| 'age': np.random.uniform(-0.05, 0.05) | |
| } | |
| fig_shap = px.bar( | |
| x=list(shap_values.values()), | |
| y=list(shap_values.keys()), | |
| orientation='h', | |
| title="SHAP Values (Current Transaction)", | |
| labels={'x': 'SHAP Value', 'y': 'Features'}, | |
| color=[v > 0 for v in shap_values.values()], | |
| color_discrete_map={True: 'red', False: 'blue'} | |
| ) | |
| st.plotly_chart(fig_shap, use_container_width=True) | |
| with tab2: | |
| col1, col2 = st.columns(2) | |
| with col1: | |
| # ROC Curve simulation | |
| fpr = np.linspace(0, 1, 100) | |
| tpr = 1 - np.exp(-5 * fpr) # Mock ROC curve | |
| fig_roc = px.line( | |
| x=fpr, y=tpr, | |
| title="ROC Curve (Model Performance)", | |
| labels={'x': 'False Positive Rate', 'y': 'True Positive Rate'} | |
| ) | |
| fig_roc.add_shape( | |
| type='line', line=dict(dash='dash'), | |
| x0=0, x1=1, y0=0, y1=1 | |
| ) | |
| st.plotly_chart(fig_roc, use_container_width=True) | |
| with col2: | |
| # Precision-Recall curve | |
| recall = np.linspace(0, 1, 100) | |
| precision = 0.9 * np.exp(-2 * recall) # Mock PR curve | |
| fig_pr = px.line( | |
| x=recall, y=precision, | |
| title="Precision-Recall Curve", | |
| labels={'x': 'Recall', 'y': 'Precision'} | |
| ) | |
| st.plotly_chart(fig_pr, use_container_width=True) | |
| with tab3: | |
| # Transaction patterns analysis | |
| col1, col2 = st.columns(2) | |
| with col1: | |
| # Hourly fraud patterns | |
| hours = range(24) | |
| fraud_rates = [0.02 + 0.03 * abs(np.sin(h * np.pi / 12)) for h in hours] | |
| fig_hourly = px.bar( | |
| x=hours, y=fraud_rates, | |
| title="Fraud Rate by Hour of Day", | |
| labels={'x': 'Hour', 'y': 'Fraud Rate'} | |
| ) | |
| st.plotly_chart(fig_hourly, use_container_width=True) | |
| with col2: | |
| # Category risk analysis | |
| categories = ['grocery_pos', 'gas_transport', 'shopping_net', 'travel', 'entertainment'] | |
| risk_scores = [0.15, 0.12, 0.28, 0.35, 0.22] | |
| fig_category = px.pie( | |
| values=risk_scores, names=categories, | |
| title="Risk Distribution by Category" | |
| ) | |
| st.plotly_chart(fig_category, use_container_width=True) | |
| with tab4: | |
| # Security dashboard | |
| col1, col2, col3 = st.columns(3) | |
| with col1: | |
| st.metric("🛡️ Threats Blocked Today", "47", delta="12") | |
| st.metric("⚡ Real-time Alerts", "23", delta="5") | |
| with col2: | |
| st.metric("🎯 Detection Accuracy", "94.2%", delta="2.1%") | |
| st.metric("⏱️ Avg Response Time", "0.34s", delta="-0.05s") | |
| with col3: | |
| st.metric("💰 Losses Prevented", "$234,567", delta="$45,123") | |
| st.metric("🔍 Investigations Active", "8", delta="2") | |
| # Security timeline | |
| st.markdown("#### 🕒 Recent Security Events") | |
| security_events = pd.DataFrame({ | |
| 'Time': pd.date_range(start='2024-06-01 08:00', periods=10, freq='1H'), | |
| 'Event': ['High Risk Transaction', 'Anomaly Detected', 'Location Alert', | |
| 'Velocity Warning', 'Blocked Transaction'] * 2, | |
| 'Risk Level': ['HIGH', 'MEDIUM', 'LOW', 'HIGH', 'CRITICAL'] * 2, | |
| 'Amount': np.random.uniform(100, 5000, 10) | |
| }) | |
| fig_timeline = px.scatter( | |
| security_events, x='Time', y='Event', | |
| size='Amount', color='Risk Level', | |
| title="Security Events Timeline" | |
| ) | |
| st.plotly_chart(fig_timeline, use_container_width=True) | |
| # Real-time Monitoring Dashboard | |
| if enable_realtime: | |
| st.markdown("---") | |
| st.markdown("### ⚡ Real-time Monitoring Dashboard") | |
| # Simulate real-time data | |
| if st.button("🔄 Refresh Real-time Data"): | |
| col1, col2, col3, col4 = st.columns(4) | |
| with col1: | |
| current_tps = np.random.poisson(15) # Transactions per second | |
| st.metric("🔄 Live TPS", current_tps, delta=np.random.randint(-3, 4)) | |
| with col2: | |
| fraud_rate = np.random.uniform(0.01, 0.05) | |
| st.metric("🎯 Live Fraud Rate", f"{fraud_rate:.1%}", | |
| delta=f"{np.random.uniform(-0.01, 0.01):.2%}") | |
| with col3: | |
| avg_response = np.random.uniform(0.2, 0.8) | |
| st.metric("⚡ Avg Response Time", f"{avg_response:.2f}s", | |
| delta=f"{np.random.uniform(-0.1, 0.1):.2f}s") | |
| with col4: | |
| system_health = np.random.choice(['Healthy', 'Warning', 'Critical'], | |
| p=[0.8, 0.15, 0.05]) | |
| health_emoji = {'Healthy': '🟢', 'Warning': '🟡', 'Critical': '🔴'} | |
| st.metric("🏥 System Health", f"{health_emoji[system_health]} {system_health}") | |
| # Real-time transaction stream simulation | |
| st.markdown("#### 📡 Live Transaction Stream") | |
| # Generate mock real-time transactions | |
| realtime_data = [] | |
| for i in range(10): | |
| transaction = { | |
| 'Time': (datetime.datetime.now(india) - timedelta(seconds=i*5)).strftime('%H:%M:%S'), | |
| 'Amount': f"${np.random.uniform(10, 2000):.0f}", | |
| 'Risk': np.random.choice(['LOW', 'MEDIUM', 'HIGH', 'CRITICAL'], | |
| p=[0.7, 0.2, 0.08, 0.02]), | |
| 'Location': np.random.choice(['New York', 'Los Angeles', 'Chicago', 'Houston', 'Phoenix']), | |
| 'Status': np.random.choice(['✅ Approved', '⏳ Review', '🚫 Blocked'], p=[0.85, 0.1, 0.05]) | |
| } | |
| realtime_data.append(transaction) | |
| realtime_df = pd.DataFrame(realtime_data) | |
| st.dataframe(realtime_df, use_container_width=True) | |
| # AI Model Management Section | |
| if st.session_state.get('user_role') in ['Manager', 'Admin']: | |
| st.markdown("---") | |
| st.markdown("### 🤖 AI Model Management & Optimization") | |
| tab1, tab2, tab3 = st.tabs(["📊 Model Performance", "⚙️ Model Config", "🔄 Model Updates"]) | |
| with tab1: | |
| col1, col2 = st.columns(2) | |
| with col1: | |
| # Model performance over time | |
| dates = pd.date_range(start='2024-05-01', end='2024-06-01', freq='D') | |
| accuracy_trend = 0.9 + 0.05 * np.sin(np.arange(len(dates)) * 0.2) + np.random.normal(0, 0.01, len(dates)) | |
| fig_trend = px.line( | |
| x=dates, y=accuracy_trend, | |
| title="Model Accuracy Trend (30 Days)", | |
| labels={'x': 'Date', 'y': 'Accuracy'} | |
| ) | |
| fig_trend.add_hline(y=0.9, line_dash="dash", annotation_text="Target Accuracy") | |
| st.plotly_chart(fig_trend, use_container_width=True) | |
| with col2: | |
| # Model comparison | |
| models_comparison = { | |
| 'XGBoost': {'Accuracy': 0.942, 'Precision': 0.876, 'Recall': 0.823}, | |
| 'LightGBM': {'Accuracy': 0.938, 'Precision': 0.881, 'Recall': 0.815}, | |
| 'Random Forest': {'Accuracy': 0.935, 'Precision': 0.869, 'Recall': 0.834}, | |
| 'Ensemble': {'Accuracy': 0.951, 'Precision': 0.889, 'Recall': 0.841} | |
| } | |
| comparison_df = pd.DataFrame(models_comparison).T | |
| fig_comparison = px.bar( | |
| comparison_df.reset_index(), | |
| x='index', y=['Accuracy', 'Precision', 'Recall'], | |
| title="Model Performance Comparison", | |
| barmode='group' | |
| ) | |
| st.plotly_chart(fig_comparison, use_container_width=True) | |
| with tab2: | |
| st.markdown("#### ⚙️ Advanced Model Configuration") | |
| col1, col2 = st.columns(2) | |
| with col1: | |
| st.markdown("**Ensemble Weights**") | |
| xgb_weight = st.slider("XGBoost Weight", 0.0, 1.0, 0.4, 0.1) | |
| lgb_weight = st.slider("LightGBM Weight", 0.0, 1.0, 0.3, 0.1) | |
| rf_weight = st.slider("Random Forest Weight", 0.0, 1.0, 0.2, 0.1) | |
| gb_weight = st.slider("Gradient Boost Weight", 0.0, 1.0, 0.1, 0.1) | |
| total_weight = xgb_weight + lgb_weight + rf_weight + gb_weight | |
| if abs(total_weight - 1.0) > 0.01: | |
| st.warning(f"⚠️ Weights sum to {total_weight:.2f}, should sum to 1.0") | |
| with col2: | |
| st.markdown("**Detection Thresholds**") | |
| fraud_threshold = st.slider("Fraud Detection Threshold", 0.1, 0.9, 0.5, 0.05) | |
| high_risk_threshold = st.slider("High Risk Threshold", 0.1, 0.9, 0.75, 0.05) | |
| critical_threshold = st.slider("Critical Risk Threshold", 0.1, 0.9, 0.9, 0.05) | |
| st.markdown("**Anomaly Detection**") | |
| anomaly_sensitivity = st.slider("Anomaly Sensitivity", 0.01, 0.2, 0.1, 0.01) | |
| isolation_contamination = st.slider("Isolation Forest Contamination", 0.01, 0.2, 0.1, 0.01) | |
| with tab3: | |
| st.markdown("#### 🔄 Model Update & Retraining") | |
| col1, col2 = st.columns(2) | |
| with col1: | |
| st.markdown("**Current Model Status**") | |
| st.info("📊 Last Updated: 2024-05-28 14:30:00") | |
| st.info("📈 Current Version: v2.3.1") | |
| st.info("🎯 Performance: 94.2% accuracy") | |
| if st.button("🔄 Retrain Models"): | |
| with st.spinner("Retraining models with latest data..."): | |
| progress = st.progress(0) | |
| for i in range(100): | |
| progress.progress(i + 1) | |
| if i % 20 == 19: | |
| st.write(f"Training step {i//20 + 1}/5 completed...") | |
| st.success("✅ Models retrained successfully!") | |
| with col2: | |
| st.markdown("**Automated Retraining Schedule**") | |
| retrain_frequency = st.selectbox("Retraining Frequency", | |
| ["Daily", "Weekly", "Monthly", "Manual"]) | |
| performance_threshold = st.slider("Performance Threshold for Auto-Retrain", | |
| 0.8, 0.99, 0.90, 0.01) | |
| st.markdown("**Data Drift Monitoring**") | |
| drift_detected = np.random.choice([True, False], p=[0.2, 0.8]) | |
| if drift_detected: | |
| st.warning("⚠️ Data drift detected! Consider retraining.") | |
| else: | |
| st.success("✅ No significant data drift detected.") | |
| # Add these new advanced model configurations after the existing model initialization | |
| def initialize_advanced_models(): | |
| """Initialize additional advanced models and configurations""" | |
| return { | |
| 'deep_learning': { | |
| 'architecture': 'Transformer', | |
| 'layers': [512, 256, 128, 64], | |
| 'attention_heads': 8, | |
| 'dropout': 0.3, | |
| 'activation': 'ReLU' | |
| }, | |
| 'anomaly_detection': { | |
| 'algorithms': ['IsolationForest', 'LocalOutlierFactor', 'OneClassSVM'], | |
| 'ensemble_method': 'weighted_voting', | |
| 'contamination': 0.01 | |
| }, | |
| 'feature_selection': { | |
| 'methods': ['mutual_info', 'chi2', 'recursive_elimination'], | |
| 'n_features': 20, | |
| 'threshold': 0.05 | |
| } | |
| } | |
| # Add this new class for advanced decision logic | |
| class AdvancedDecisionEngine: | |
| def __init__(self): | |
| self.decision_thresholds = { | |
| 'fraud_probability': 0.85, | |
| 'risk_score': 0.75, | |
| 'velocity_check': 5, | |
| 'amount_deviation': 2.5, | |
| 'location_risk': 0.8 | |
| } | |
| def evaluate_transaction(self, features, model_outputs): | |
| """Advanced transaction evaluation with multiple criteria""" | |
| decision_factors = { | |
| 'model_confidence': self._calculate_model_confidence(model_outputs), | |
| 'risk_assessment': self._assess_risk_factors(features), | |
| 'behavioral_score': self._analyze_behavior(features), | |
| 'temporal_pattern': self._check_temporal_patterns(features), | |
| 'geographic_risk': self._evaluate_geographic_risk(features) | |
| } | |
| # Calculate weighted decision score | |
| weights = { | |
| 'model_confidence': 0.35, | |
| 'risk_assessment': 0.25, | |
| 'behavioral_score': 0.20, | |
| 'temporal_pattern': 0.10, | |
| 'geographic_risk': 0.10 | |
| } | |
| final_score = sum(score * weights[factor] | |
| for factor, score in decision_factors.items()) | |
| decision_details = { | |
| 'score': final_score, | |
| 'factors': decision_factors, | |
| 'threshold_breaches': self._check_threshold_breaches(decision_factors), | |
| 'recommendation': self._get_recommendation(final_score) | |
| } | |
| return decision_details | |
| def _calculate_model_confidence(self, model_outputs): | |
| """Calculate ensemble model confidence with uncertainty estimation""" | |
| predictions = model_outputs['individual_predictions'] | |
| probabilities = model_outputs['individual_probabilities'] | |
| # Calculate prediction variance | |
| mean_prob = np.mean(list(probabilities.values())) | |
| variance = np.var(list(probabilities.values())) | |
| # Adjust confidence based on model agreement | |
| agreement_ratio = len([p for p in predictions.values() if p == 1]) / len(predictions) | |
| # Consider uncertainty in final confidence | |
| confidence = mean_prob * (1 - variance) * agreement_ratio | |
| return min(confidence, 1.0) | |
| def _assess_risk_factors(self, features): | |
| """Comprehensive risk factor assessment""" | |
| risk_indicators = { | |
| 'amount': features.get('amount_log', 0), | |
| 'velocity': features.get('txn_last_hour', 0), | |
| 'deviation': features.get('amount_deviation', 0), | |
| 'location': features.get('location_risk', 0) | |
| } | |
| # Calculate normalized risk score | |
| normalized_risks = { | |
| factor: min(value / self.decision_thresholds.get(f'{factor}_risk', 1), 1.0) | |
| for factor, value in risk_indicators.items() | |
| } | |
| return np.mean(list(normalized_risks.values())) | |
| def _analyze_behavior(self, features): | |
| """Advanced behavioral analysis""" | |
| behavioral_factors = { | |
| 'pattern_match': features.get('merchant_familiarity', 0), | |
| 'time_consistency': 1 - features.get('hour_deviation', 0) / 24, | |
| 'location_typical': 1 - min(features.get('distance_from_home', 0) / 100, 1), | |
| 'amount_typical': 1 - features.get('amount_deviation', 0) | |
| } | |
| return np.mean(list(behavioral_factors.values())) | |
| def _check_temporal_patterns(self, features): | |
| """Analyze temporal transaction patterns""" | |
| hour = features.get('hour', 0) | |
| is_business_hours = 9 <= hour <= 17 | |
| is_late_night = hour >= 23 or hour <= 5 | |
| is_weekend = features.get('is_weekend', 0) == 1 | |
| base_score = 0.8 if is_business_hours else 0.6 | |
| if is_late_night: | |
| base_score *= 0.7 | |
| if is_weekend: | |
| base_score *= 0.9 | |
| return base_score | |
| def _evaluate_geographic_risk(self, features): | |
| """Evaluate geographic risk factors""" | |
| distance = features.get('location_distance', 0) | |
| location_risk = features.get('location_risk', 0) | |
| # Distance-based risk | |
| distance_risk = min(distance / 1000, 1.0) # Normalize to 0-1 | |
| # Combine with location-specific risk | |
| return (distance_risk + location_risk) / 2 | |
| def _check_threshold_breaches(self, factors): | |
| """Check which decision thresholds were breached""" | |
| breaches = [] | |
| for factor, value in factors.items(): | |
| if value > self.decision_thresholds.get(factor, 0.9): | |
| breaches.append(factor) | |
| return breaches | |
| def _get_recommendation(self, score): | |
| """Generate action recommendation based on score""" | |
| if score >= 0.9: | |
| return { | |
| 'action': 'BLOCK', | |
| 'confidence': 'HIGH', | |
| 'reason': 'Multiple high-risk indicators detected' | |
| } | |
| elif score >= 0.7: | |
| return { | |
| 'action': 'REVIEW', | |
| 'confidence': 'MEDIUM', | |
| 'reason': 'Suspicious pattern detected' | |
| } | |
| elif score >= 0.5: | |
| return { | |
| 'action': 'MONITOR', | |
| 'confidence': 'LOW', | |
| 'reason': 'Slightly unusual behavior' | |
| } | |
| else: | |
| return { | |
| 'action': 'APPROVE', | |
| 'confidence': 'HIGH', | |
| 'reason': 'Transaction appears normal' | |
| } | |
| # Add to the visualization section | |
| def create_advanced_visualizations(risk_data, model_output, decision_details): | |
| """Create advanced interactive visualizations""" | |
| # 3D Risk Visualization | |
| fig_3d = go.Figure(data=[go.Scatter3d( | |
| x=[risk_data['risk_components']['amount']], | |
| y=[risk_data['risk_components']['velocity']], | |
| z=[risk_data['risk_components']['behavioral']], | |
| mode='markers+text', | |
| marker=dict( | |
| size=20, | |
| color=['red' if risk_data['risk_level'] == 'HIGH' else 'green'], | |
| opacity=0.8 | |
| ), | |
| text=['Current Transaction'], | |
| hoverinfo='text' | |
| )]) | |
| fig_3d.update_layout( | |
| title='3D Risk Analysis', | |
| scene=dict( | |
| xaxis_title='Amount Risk', | |
| yaxis_title='Velocity Risk', | |
| zaxis_title='Behavioral Risk' | |
| ) | |
| ) | |
| # Decision Flow Diagram | |
| decision_flow = go.Figure(go.Sankey( | |
| node=dict( | |
| pad=15, | |
| thickness=20, | |
| line=dict(color="black", width=0.5), | |
| label=["Transaction", "Model Analysis", "Risk Assessment", | |
| "Behavior Check", "Final Decision"], | |
| color=["blue", "green", "red", "yellow", "purple"] | |
| ), | |
| link=dict( | |
| source=[0, 0, 1, 2, 3], | |
| target=[1, 2, 3, 4, 4], | |
| value=[1, 1, 1, 1, 1] | |
| ) | |
| )) | |
| decision_flow.update_layout(title_text="Decision Flow Analysis") | |
| return fig_3d, decision_flow | |
| # Add to the main dashboard | |
| if st.session_state.get('authenticated', False): | |
| # Initialize advanced components | |
| decision_engine = AdvancedDecisionEngine() | |
| advanced_models = initialize_advanced_models() | |
| # Get decision details | |
| decision_details = decision_engine.evaluate_transaction(all_features, model_output) | |
| # Create advanced visualizations | |
| fig_3d, decision_flow = create_advanced_visualizations( | |
| risk_data, model_output, decision_details | |
| ) | |
| # Display advanced analytics | |
| st.markdown("### 🔬 Advanced Transaction Analysis") | |
| col1, col2 = st.columns(2) | |
| with col1: | |
| st.plotly_chart(fig_3d, use_container_width=True) | |
| with col2: | |
| st.plotly_chart(decision_flow, use_container_width=True) | |
| # Display decision details | |
| st.markdown("### 🤖 AI Decision Analysis") | |
| col1, col2, col3 = st.columns(3) | |
| with col1: | |
| st.metric("Decision Confidence", | |
| f"{decision_details['score']:.1%}", | |
| delta=f"{decision_details['score']-0.5:.1%}") | |
| with col2: | |
| st.metric("Risk Level", | |
| decision_details['recommendation']['action'], | |
| delta=decision_details['recommendation']['confidence']) | |
| with col3: | |
| st.metric("Threshold Breaches", | |
| len(decision_details['threshold_breaches']), | |
| delta="Critical" if len(decision_details['threshold_breaches']) > 2 else "Normal") | |
| # Add this after the main header | |
| if st.session_state.get('authenticated', False): | |
| # Quick Start Guide | |
| with st.expander("📚 Quick Start Guide & Documentation"): | |
| st.markdown(""" | |
| ### 🎓 How to Use This Fraud Detection System | |
| #### 1. System Overview | |
| - **Real-time Monitoring**: Watch transactions as they happen | |
| - **Risk Assessment**: AI-powered risk scoring | |
| - **Alert System**: Instant notifications for suspicious activity | |
| - **Detailed Analytics**: In-depth analysis and reporting | |
| #### 2. Key Features | |
| - 🔍 Multi-model fraud detection | |
| - 📊 Interactive dashboards | |
| - 🚨 Real-time alerts | |
| - 📈 Advanced analytics | |
| - 📁 Batch processing | |
| #### 3. Understanding Risk Levels | |
| - 🟢 LOW: Normal transaction | |
| - 🟡 MEDIUM: Requires monitoring | |
| - 🟠 HIGH: Needs investigation | |
| - 🔴 CRITICAL: Immediate action required | |
| """) | |
| # Help tooltips for each section | |
| with st.sidebar: | |
| st.info(""" | |
| 💡 **Navigation Tips:** | |
| - Use the sidebar for data input | |
| - Monitor alerts in real-time | |
| - Download reports as needed | |
| - Check system health regularly | |
| """) | |
| # Add informative metrics at the top | |
| col1, col2, col3, col4 = st.columns(4) | |
| with col1: | |
| st.metric( | |
| "System Health", | |
| "98%", | |
| "↑2%", | |
| help="Overall system performance score" | |
| ) | |
| with col2: | |
| st.metric( | |
| "Detection Rate", | |
| "99.2%", | |
| "↑0.5%", | |
| help="Percentage of fraud cases detected" | |
| ) | |
| with col3: | |
| st.metric( | |
| "Response Time", | |
| "0.3s", | |
| "↓0.1s", | |
| help="Average time to process a transaction" | |
| ) | |
| with col4: | |
| st.metric( | |
| "Active Models", | |
| "4/4", | |
| "✓", | |
| help="Number of AI models currently active" | |
| ) | |
| # Add explanation for each visualization | |
| st.markdown("### 📊 Visual Analytics Guide") | |
| tab1, tab2, tab3 = st.tabs([ | |
| "🎯 Risk Analysis", | |
| "📈 Performance Metrics", | |
| "🔍 Feature Importance" | |
| ]) | |
| with tab1: | |
| col1, col2 = st.columns(2) | |
| with col1: | |
| st.markdown(""" | |
| #### Understanding Risk Score | |
| The risk score is calculated using multiple factors: | |
| - Transaction amount | |
| - User behavior patterns | |
| - Location analysis | |
| - Time-based patterns | |
| - Historical data | |
| """) | |
| # Risk Distribution Chart | |
| fig_risk = px.pie( | |
| names=['Low', 'Medium', 'High', 'Critical'], | |
| values=[45, 30, 15, 10], | |
| title="Risk Level Distribution", | |
| color_discrete_sequence=['green', 'yellow', 'orange', 'red'] | |
| ) | |
| st.plotly_chart(fig_risk, use_container_width=True) | |
| with col2: | |
| st.markdown(""" | |
| #### Risk Factors Explained | |
| Each transaction is evaluated based on: | |
| 1. 💰 Amount Analysis | |
| 2. 🕒 Time Patterns | |
| 3. 📍 Location Risk | |
| 4. 👤 User Behavior | |
| 5. 🔄 Velocity Checks | |
| """) | |
| # Risk Factors Radar Chart | |
| risk_factors = { | |
| 'Amount': 0.8, | |
| 'Time': 0.6, | |
| 'Location': 0.7, | |
| 'Behavior': 0.9, | |
| 'Velocity': 0.5 | |
| } | |
| fig_radar = go.Figure() | |
| fig_radar.add_trace(go.Scatterpolar( | |
| r=list(risk_factors.values()), | |
| theta=list(risk_factors.keys()), | |
| fill='toself' | |
| )) | |
| fig_radar.update_layout(title="Risk Factor Analysis") | |
| st.plotly_chart(fig_radar, use_container_width=True) | |
| with tab2: | |
| st.markdown(""" | |
| #### Performance Metrics Explained | |
| 1. **Model Accuracy**: How often the model makes correct predictions | |
| 2. **Detection Rate**: Percentage of actual fraud cases caught | |
| 3. **False Positive Rate**: Incorrect fraud flags | |
| 4. **Response Time**: Speed of transaction processing | |
| """) | |
| # Performance Metrics Visualization | |
| metrics_data = { | |
| 'Metric': ['Accuracy', 'Detection', 'False Positive', 'Response'], | |
| 'Score': [0.95, 0.92, 0.08, 0.89], | |
| 'Target': [0.90, 0.85, 0.10, 0.85] | |
| } | |
| fig_metrics = px.bar( | |
| metrics_data, | |
| x='Metric', | |
| y=['Score', 'Target'], | |
| barmode='group', | |
| title="Model Performance vs Targets" | |
| ) | |
| st.plotly_chart(fig_metrics, use_container_width=True) | |
| with tab3: | |
| st.markdown(""" | |
| #### Feature Importance Guide | |
| Understanding what factors influence fraud detection: | |
| - Higher bars indicate stronger influence | |
| - Color indicates positive/negative impact | |
| - Hover for detailed information | |
| """) | |
| # Feature Importance Chart | |
| features = { | |
| 'Transaction Amount': 0.85, | |
| 'Time of Day': 0.65, | |
| 'Location': 0.75, | |
| 'User History': 0.80, | |
| 'Device Type': 0.45, | |
| 'Transaction Type': 0.70 | |
| } | |
| fig_importance = px.bar( | |
| x=list(features.keys()), | |
| y=list(features.values()), | |
| title="Feature Importance Analysis", | |
| labels={'x': 'Feature', 'y': 'Importance Score'}, | |
| color=list(features.values()), | |
| color_continuous_scale='Viridis' | |
| ) | |
| st.plotly_chart(fig_importance, use_container_width=True) | |
| # Add interactive help system | |
| with st.expander("❓ Need Help?"): | |
| st.markdown(""" | |
| ### Common Questions | |
| 1. **How to interpret risk scores?** | |
| - 0-0.2: Minimal risk | |
| - 0.2-0.4: Low risk | |
| - 0.4-0.6: Medium risk | |
| - 0.6-0.8: High risk | |
| - 0.8-1.0: Critical risk | |
| 2. **What do the colors mean?** | |
| - 🟢 Green: Safe | |
| - 🟡 Yellow: Caution | |
| - 🟠 Orange: Warning | |
| - 🔴 Red: Danger | |
| 3. **How to use batch processing?** | |
| 1. Upload your CSV file | |
| 2. Configure processing parameters | |
| 3. Run analysis | |
| 4. Review results | |
| 5. Download reports | |
| 4. **Understanding alerts:** | |
| - Email alerts for high-risk transactions | |
| - Real-time dashboard notifications | |
| - Daily summary reports | |
| - Custom alert thresholds | |
| """) | |
| # Add system health monitoring | |
| st.markdown("### 🏥 System Health Monitor") | |
| col1, col2 = st.columns(2) | |
| with col1: | |
| # System Load Chart | |
| load_data = { | |
| 'Time': pd.date_range(start='now', periods=24, freq='H'), | |
| 'Load': np.random.normal(65, 15, 24) | |
| } | |
| fig_load = px.line( | |
| load_data, | |
| x='Time', | |
| y='Load', | |
| title="System Load (24h)", | |
| labels={'Load': 'CPU Load (%)'} | |
| ) | |
| st.plotly_chart(fig_load, use_container_width=True) | |
| with col2: | |
| # Model Health Status | |
| st.markdown("**🤖 Model Health Status**") | |
| # Mock model health data | |
| model_health_data = { | |
| 'Model': ['XGBoost', 'LightGBM', 'RandomForest', 'GradientBoost'], | |
| 'Status': ['Active', 'Active', 'Inactive', 'Active'], | |
| 'Accuracy': [0.942, 0.938, 0.895, 0.901], | |
| 'Last Updated': [ | |
| datetime.datetime.now(india) - timedelta(days=1), | |
| datetime.datetime.now(india) - timedelta(hours=2), | |
| datetime.datetime.now(india) - timedelta(days=10), | |
| datetime.datetime.now(india) - timedelta(hours=5) | |
| ] | |
| } | |
| model_health_df = pd.DataFrame(model_health_data) | |
| # Display model health table | |
| st.table(model_health_df.style.format({ | |
| 'Accuracy': "{:.1%}", | |
| 'Last Updated': lambda t: t.strftime("%Y-%m-%d %H:%M") | |
| })) | |
| # Advanced Batch Processing Section | |
| st.markdown("---") | |
| st.markdown("### 📁 Advanced Batch Processing & Analysis") | |
| uploaded_file = st.file_uploader( | |
| "Upload transaction data for batch analysis", | |
| type=['csv', 'xlsx', 'json'], | |
| help="Upload CSV, Excel, or JSON files containing transaction data" | |
| ) | |
| if uploaded_file is not None: | |
| try: | |
| # Handle different file types | |
| if uploaded_file.name.endswith('.csv'): | |
| df = pd.read_csv(uploaded_file) | |
| elif uploaded_file.name.endswith('.xlsx'): | |
| df = pd.read_excel(uploaded_file) | |
| elif uploaded_file.name.endswith('.json'): | |
| df = pd.read_json(uploaded_file) | |
| st.success(f"✅ Successfully loaded {len(df)} transactions") | |
| # Data preprocessing and validation | |
| st.markdown("#### 🔧 Data Preprocessing") | |
| col1, col2, col3 = st.columns(3) | |
| with col1: | |
| st.metric("Total Transactions", len(df)) | |
| with col2: | |
| missing_data = df.isnull().sum().sum() | |
| st.metric("Missing Values", missing_data) | |
| with col3: | |
| duplicate_data = df.duplicated().sum() | |
| st.metric("Duplicate Records", duplicate_data) | |
| # Advanced batch prediction | |
| if st.button("🚀 Run Advanced Batch Analysis"): | |
| with st.spinner("Processing transactions with advanced AI models..."): | |
| # Simulate batch processing | |
| progress_bar = st.progress(0) | |
| batch_results = [] | |
| for i, row in df.iterrows(): | |
| # Simulate processing each transaction | |
| progress_bar.progress((i + 1) / len(df)) | |
| # Mock advanced features for each transaction | |
| mock_features = { | |
| 'amount_log': np.log(row.get('amount', 100) + 1), | |
| 'velocity_score': np.random.uniform(0, 1), | |
| 'behavioral_score': np.random.uniform(0, 1), | |
| 'anomaly_score': np.random.uniform(-1, 1), | |
| 'ensemble_prob': np.random.beta(2, 8), # Skewed towards legitimate | |
| 'risk_level': np.random.choice(['LOW', 'MEDIUM', 'HIGH', 'CRITICAL'], | |
| p=[0.7, 0.2, 0.08, 0.02]) | |
| } | |
| batch_results.append(mock_features) | |
| # Convert results to DataFrame | |
| results_df = pd.DataFrame(batch_results) | |
| results_df.index = df.index | |
| # Combine with original data | |
| final_df = pd.concat([df, results_df], axis=1) | |
| st.success("✅ Batch processing completed!") | |
| # Results summary | |
| st.markdown("#### 📊 Batch Analysis Results") | |
| col1, col2, col3, col4 = st.columns(4) | |
| fraud_count = (final_df['ensemble_prob'] > 0.5).sum() | |
| critical_count = (final_df['risk_level'] == 'CRITICAL').sum() | |
| high_risk_count = (final_df['risk_level'] == 'HIGH').sum() | |
| total_at_risk = fraud_count + critical_count + high_risk_count | |
| with col1: | |
| st.metric("Flagged as Fraud", | |
| f"{fraud_count:,} ({fraud_count/len(df)*100:.1f}%)") | |
| with col2: | |
| st.metric("Critical Risk", | |
| f"{critical_count:,} ({critical_count/len(df)*100:.1f}%)") | |
| with col3: | |
| st.metric("High Risk", | |
| f"{high_risk_count:,} ({high_risk_count/len(df)*100:.1f}%)") | |
| with col4: | |
| st.metric("Total At Risk", | |
| f"{total_at_risk:,}", | |
| f"${total_at_risk * 1500:,.0f} potential impact") | |
| # Results visualization | |
| st.markdown("#### 📈 Analysis Visualization") | |
| tab1, tab2, tab3, tab4 = st.tabs([ | |
| "Risk Distribution", | |
| "Amount Analysis", | |
| "Model Performance", | |
| "Detailed Results" | |
| ]) | |
| with tab1: | |
| # Risk level distribution | |
| risk_counts = final_df['risk_level'].value_counts() | |
| fig_risk = px.pie( | |
| values=risk_counts.values, | |
| names=risk_counts.index, | |
| title="Risk Level Distribution", | |
| color=risk_counts.index, | |
| color_discrete_map={ | |
| 'LOW': 'green', | |
| 'MEDIUM': 'yellow', | |
| 'ORANGE': 'orange', | |
| 'CRITICAL': 'red' | |
| } | |
| ) | |
| st.plotly_chart(fig_risk, use_container_width=True) | |
| with tab2: | |
| # Amount distribution by risk level | |
| fig_amount = px.box( | |
| final_df, | |
| x='risk_level', | |
| y='amt', | |
| color='risk_level', | |
| title="Transaction Amount Distribution by Risk Level" | |
| ) | |
| st.plotly_chart(fig_amount, use_container_width=True) | |
| with tab3: | |
| # Performance metrics | |
| performance_metrics = { | |
| 'Detection Rate': 0.942, | |
| 'Precision': 0.876, | |
| 'Recall': 0.823, | |
| 'F1 Score': 0.849 | |
| } | |
| fig_metrics = px.bar( | |
| x=list(performance_metrics.keys()), | |
| y=list(performance_metrics.values()), | |
| title="Model Performance Metrics", | |
| labels={'x': 'Metrics', 'y': 'Score'} | |
| ) | |
| fig_metrics.update_layout(yaxis_range=[0, 1]) | |
| st.plotly_chart(fig_metrics, use_container_width=True) | |
| with tab4: | |
| # Detailed results table | |
| st.markdown("#### 🔍 Detailed Transaction Analysis") | |
| # Filter options | |
| col1, col2, col3 = st.columns(3) | |
| with col1: | |
| risk_filter = st.selectbox("Filter by Risk Level", | |
| ['All'] + list(final_df['risk_level'].unique())) | |
| with col2: | |
| prob_threshold = st.slider("Minimum Fraud Probability", | |
| 0.0, 1.0, 0.0, 0.1) | |
| with col3: | |
| show_top_n = st.number_input("Show Top N Risky Transactions", | |
| 1, len(final_df), 50) | |
| # Apply filters | |
| filtered_df = final_df.copy() | |
| if risk_filter != 'All': | |
| filtered_df = filtered_df[filtered_df['risk_level'] == risk_filter] | |
| filtered_df = filtered_df[filtered_df['ensemble_prob'] >= prob_threshold] | |
| filtered_df = filtered_df.nlargest(show_top_n, 'ensemble_prob') | |
| # Display filtered results | |
| st.dataframe(filtered_df, use_container_width=True) | |
| # Download options | |
| col1, col2, col3 = st.columns(3) | |
| with col1: | |
| csv_data = final_df.to_csv(index=False) | |
| st.download_button( | |
| "📥 Download Full Results (CSV)", | |
| csv_data, | |
| "fraud_analysis_results.csv", | |
| "text/csv" | |
| ) | |
| with col2: | |
| high_risk_df = final_df[final_df['risk_level'].isin(['HIGH', 'CRITICAL'])] | |
| high_risk_csv = high_risk_df.to_csv(index=False) | |
| st.download_button( | |
| "🚨 Download High-Risk Only (CSV)", | |
| high_risk_csv, | |
| "high_risk_transactions.csv", | |
| "text/csv" | |
| ) | |
| with col3: | |
| # Generate summary report | |
| summary_report = f""" | |
| FRAUD DETECTION BATCH ANALYSIS SUMMARY | |
| ===================================== | |
| Analysis Date: {datetime.datetime.now(india).strftime('%Y-%m-%d %H:%M:%S')} | |
| Total Transactions Processed: {len(df):,} | |
| RISK SUMMARY: | |
| - Critical Risk: {critical_count:,} ({critical_count/len(df)*100:.1f}%) | |
| - High Risk: {high_risk_count:,} ({high_risk_count/len(df)*100:.1f}%) | |
| - Potential Fraud: {fraud_count:,} ({fraud_count/len(df)*100:.1f}%) | |
| ESTIMATED IMPACT: | |
| - Estimated Losses Prevented: ${total_at_risk * 1500:,.0f} | |
| - Transactions Requiring Review: {total_at_risk:,} | |
| MODEL PERFORMANCE: | |
| - Overall Accuracy: 94.2% | |
| - Detection Rate: 87.6% | |
| - False Positive Rate: 4.3% | |
| RECOMMENDATIONS: | |
| 1. Immediately investigate {critical_count} CRITICAL risk transactions | |
| 2. Review {high_risk_count} HIGH risk transactions within 24 hours | |
| 3. Monitor customers with multiple flagged transactions | |
| 4. Update risk thresholds based on recent patterns | |
| Generated by Advanced AI Fraud Detection System | |
| """ | |
| st.download_button( | |
| "📄 Download Summary Report", | |
| summary_report, | |
| "fraud_analysis_summary.txt", | |
| "text/plain" | |
| ) | |
| except Exception as e: | |
| st.error(f"Error processing file: {str(e)}") | |
| st.info("Please ensure your file contains the required columns and is properly formatted.") | |
| # System Administration (Admin only) | |
| if st.session_state.get('user_role') == 'Admin': | |
| st.markdown("---") | |
| st.markdown("### 🛠️ System Administration") | |
| tab1, tab2, tab3 = st.tabs(["👥 User Management", "📊 System Logs", "🔧 Configuration"]) | |
| with tab1: | |
| st.markdown("#### 👥 User Access Management") | |
| # Add buttons at the top | |
| col1, col2 = st.columns(2) | |
| with col1: | |
| if st.button("➕ Add New User"): | |
| st.session_state.adding_user = True | |
| st.session_state.show_edit_form = False | |
| st.session_state.show_users = False | |
| with col2: | |
| if st.button("👥 Show All Users"): | |
| st.session_state.show_users = True | |
| st.session_state.adding_user = False | |
| st.session_state.show_edit_form = False | |
| # Show Users Table | |
| if st.session_state.get('show_users', False): | |
| st.markdown("#### 📋 Current Users") | |
| display_df = users_df.drop(['Password', 'Email'], axis=1) | |
| st.dataframe(display_df, use_container_width=True) | |
| # Add New User Form | |
| if st.session_state.get('adding_user', False): | |
| st.markdown("#### ➕ Add New User") | |
| with st.form("add_user_form"): | |
| new_username = st.text_input("Username*") | |
| new_email = st.text_input("Email*") | |
| new_password = st.text_input("Password*", type="password") | |
| confirm_password = st.text_input("Confirm Password*", type="password") | |
| new_role = st.selectbox("Role*", ["User", "Manager", "Admin"], key="add_user_role") | |
| new_department = st.selectbox("Department*", ["General", "Risk", "Compliance", "IT", "Operations"], key="add_user_department") | |
| col1, col2 = st.columns(2) | |
| with col1: | |
| if st.form_submit_button("Add User"): | |
| if not new_username or not new_email or not new_password: | |
| st.error("Please fill all required fields") | |
| elif new_password != confirm_password: | |
| st.error("Passwords do not match") | |
| elif new_username in users_df['Username'].values: | |
| st.error("Username already exists!") | |
| else: | |
| try: | |
| # Add new user | |
| new_user = pd.DataFrame([{ | |
| 'Username': new_username, | |
| 'Password': hash_password(new_password), | |
| 'Email': new_email, | |
| 'Role': new_role, | |
| 'Last_Login': 'Never', | |
| 'Status': 'Active', | |
| 'Permissions': role_permissions[new_role][0], | |
| 'Department': new_department | |
| }]) | |
| users_df_updated = pd.concat([users_df, new_user], ignore_index=True) | |
| if save_users_data(users_df_updated): | |
| st.success(f"✅ User {new_username} added successfully!") | |
| st.session_state.adding_user = False | |
| time.sleep(1) | |
| st.rerun() | |
| except Exception as e: | |
| st.error(f"Error adding user: {str(e)}") | |
| with col2: | |
| if st.form_submit_button("Cancel"): | |
| st.session_state.adding_user = False | |
| st.rerun() | |
| # Search and Edit Section | |
| st.markdown("#### 🔍 Search and Edit Users") | |
| search_user = st.text_input("Search Username", key="search_user") | |
| # Add this at the top with other configurations | |
| PROTECTED_USERNAMES = ['admin', 'manager', 'user', 'sanjay'] | |
| # Modify the user selection part in the Search and Edit section | |
| if search_user and not users_df.empty: | |
| # Filter out protected users from selection options | |
| filtered_users = users_df[ | |
| (~users_df['Username'].isin(PROTECTED_USERNAMES)) & | |
| (users_df['Username'].str.contains(search_user, case=False)) | |
| ] | |
| if not filtered_users.empty: | |
| # Show filtered users in a table | |
| display_df = filtered_users.drop(['Password','Email'], axis=1) | |
| st.dataframe(display_df, use_container_width=True) | |
| # Edit User Selection (only shows non-protected users) | |
| selected_user = st.selectbox( | |
| "Select User to Edit", | |
| options=filtered_users['Username'].tolist(), | |
| key="edit_user_select" | |
| ) | |
| if st.button("✏️ Edit Selected User", key="edit_button"): | |
| st.session_state.selected_user = selected_user | |
| st.session_state.show_edit_form = True | |
| st.rerun() | |
| else: | |
| st.warning("No editable users found matching the search criteria") | |
| else: | |
| st.warning("No users found matching the search criteria") | |
| # Edit User Form | |
| if st.session_state.get('show_edit_form', False) and st.session_state.get('selected_user'): | |
| user_data = users_df[users_df['Username'] == st.session_state.selected_user].iloc[0] | |
| with st.form("edit_user_form"): | |
| st.markdown(f"#### ✏️ Edit User: {st.session_state.selected_user}") | |
| # Add username field (disabled/readonly) | |
| username = st.text_input("Username", value=user_data['Username'], disabled=True) | |
| edit_role = st.selectbox( | |
| "Role", | |
| options=["User", "Manager", "Admin"], | |
| index=["User", "Manager", "Admin"].index(user_data['Role']) | |
| ) | |
| # Role-based permissions | |
| role_permissions = { | |
| "User": ["Read"], | |
| "Manager": ["Read", "Read/Write"], | |
| "Admin": ["Read", "Read/Write", "Full"] | |
| } | |
| # Automatically select the highest permission for the role | |
| default_permission = role_permissions[edit_role][-1] # Get highest permission for role | |
| # Show current permission (read-only) | |
| st.text(f"Permission Level: {default_permission}") | |
| available_permissions = role_permissions[edit_role] | |
| current_permission = user_data['Permissions'] | |
| if current_permission not in available_permissions: | |
| current_permission = available_permissions[0] | |
| edit_status = st.selectbox("Status", ["Active", "Inactive"], index=["Active", "Inactive"].index(user_data['Status']), key="edit_status_select") | |
| edit_department = st.selectbox( | |
| "Department", | |
| options=["General", "Risk", "Compliance", "IT", "Operations"], | |
| index=["General", "Risk", "Compliance", "IT", "Operations"].index(user_data['Department']), | |
| key="edit_user_department" | |
| ) | |
| col1, col2 = st.columns(2) | |
| with col1: | |
| if st.form_submit_button("💾 Save Changes"): | |
| try: | |
| # Update user data | |
| users_df.loc[users_df['Username'] == st.session_state.selected_user, 'Role'] = edit_role | |
| users_df.loc[users_df['Username'] == st.session_state.selected_user, 'Status'] = edit_status | |
| users_df.loc[users_df['Username'] == st.session_state.selected_user, 'Department'] = edit_department | |
| users_df.loc[users_df['Username'] == st.session_state.selected_user, 'Permissions'] = default_permission | |
| save_users_data(users_df) | |
| st.success(f"✅ User {username} updated successfully!") | |
| time.sleep(1) | |
| st.session_state.show_edit_form = False | |
| st.rerun() | |
| except Exception as e: | |
| st.error(f"❌ Error updating user: {str(e)}") | |
| with col2: | |
| if st.form_submit_button("❌ Cancel"): | |
| st.session_state.show_edit_form = False | |
| st.rerun() | |
| with tab2: | |
| st.markdown("#### 📊 System Activity Logs") | |
| # Mock log data | |
| log_data = { | |
| 'Timestamp': pd.date_range(start='2024-06-01 08:00', periods=20, freq='15min'), | |
| 'Event': np.random.choice(['User Login', 'Model Prediction', 'Alert Sent', 'Configuration Changed'], 20), | |
| 'User': np.random.choice(['user', 'manager', 'admin'], 20), | |
| 'Details': ['Various system events...'] * 20, | |
| 'Status': np.random.choice(['Success', 'Warning', 'Error'], 20, p=[0.8, 0.15, 0.05]) | |
| } | |
| logs_df = pd.DataFrame(log_data) | |
| # Log filtering | |
| col1, col2, col3 = st.columns(3) | |
| with col1: | |
| event_filter = st.selectbox("Filter by Event", ['All'] + list(logs_df['Event'].unique())) | |
| with col2: | |
| user_filter = st.selectbox("Filter by User", ['All'] + list(logs_df['User'].unique())) | |
| with col3: | |
| status_filter = st.selectbox("Filter by Status", ['All'] + list(logs_df['Status'].unique())) | |
| # Apply filters | |
| filtered_logs = logs_df.copy() | |
| if event_filter != 'All': | |
| filtered_logs = filtered_logs[filtered_logs['Event'] == event_filter] | |
| if user_filter != 'All': | |
| filtered_logs = filtered_logs[filtered_logs['User'] == user_filter] | |
| if status_filter != 'All': | |
| filtered_logs = filtered_logs[filtered_logs['Status'] == status_filter] | |
| st.dataframe(filtered_logs, use_container_width=True) | |
| with tab3: | |
| st.markdown("#### 🔧 System Configuration") | |
| col1, col2 = st.columns(2) | |
| with col1: | |
| st.markdown("**Email Settings**") | |
| smtp_server = st.text_input("SMTP Server", "smtp.gmail.com") | |
| smtp_port = st.number_input("SMTP Port", 587) | |
| sender_email = st.text_input("Sender Email", "alerts@company.com") | |
| st.markdown("**Alert Settings**") | |
| max_alerts_per_hour = st.number_input("Max Alerts per Hour", 100) | |
| alert_cooldown = st.number_input("Alert Cooldown (minutes)", 5) | |
| with col2: | |
| st.markdown("**Performance Settings**") | |
| batch_size = st.number_input("Batch Processing Size", 1000) | |
| max_concurrent_requests = st.number_input("Max Concurrent Requests", 50) | |
| cache_expiry = st.number_input("Cache Expiry (hours)", 24) | |
| st.markdown("**Security Settings**") | |
| session_timeout = st.number_input("Session Timeout (minutes)", 30) | |
| max_login_attempts = st.number_input("Max Login Attempts", 3) | |
| if st.button("💾 Save Configuration"): | |
| st.success("✅ Configuration saved successfully") | |
| # New user registration form | |
| if 'registering' not in st.session_state: | |
| st.session_state.registering = False | |
| if st.session_state.get('registering', False): | |
| st.markdown("### 📝 New User Registration") | |
| with st.form("registration_form"): | |
| new_username = st.text_input("Username*") | |
| new_email = st.text_input("Email*") | |
| new_password = st.text_input("Password*", type="password") | |
| confirm_password = st.text_input("Confirm Password*", type="password") | |
| # User users can only register with User role and Read permissions | |
| if st.form_submit_button("Register"): | |
| if not new_username or not new_email or not new_password: | |
| st.error("Please fill all required fields") | |
| elif new_password != confirm_password: | |
| st.error("Passwords do not match") | |
| else: | |
| # Check if username already exists | |
| users_df = load_users() | |
| if new_username in users_df['Username'].values: | |
| st.error("Username already exists!") | |
| else: | |
| # Add new User user | |
| new_user = pd.DataFrame([{ | |
| 'Username': new_username, | |
| 'Password': hash_password(new_password), | |
| 'Email': new_email, | |
| 'Role': 'User', | |
| 'Last_Login': 'Never', | |
| 'Status': 'Active', | |
| 'Permissions': 'Read', | |
| 'Department': 'General' | |
| }]) | |
| users_df = pd.concat([users_df, new_user], ignore_index=True) | |
| save_users_data(users_df) | |
| st.success("✅ Registration successful! You can now login.") | |
| st.session_state.registering = False | |
| st.rerun() | |
| # Add permission checks throughout the app | |
| def check_permission(required_permission): | |
| """Check if user has required permission level""" | |
| permission_levels = { | |
| 'User': 0, | |
| 'Read': 1, | |
| 'Read/Write': 2, | |
| 'Full': 3 | |
| } | |
| user_permission = st.session_state.get('user_permission', 'User') | |
| return permission_levels.get(user_permission, 0) >= permission_levels.get(required_permission, 0) | |
| # Example usage in a protected section: | |
| if st.session_state.get('authenticated', False): | |
| if check_permission('Read'): | |
| st.markdown("### 📊 Dashboard") | |
| # Show read-only content | |
| if check_permission('Read/Write'): | |
| st.markdown("### 📝 Transaction Input") | |
| # Show input forms | |
| if check_permission('Full'): | |
| st.markdown("### ⚙️ System Configuration") | |
| # Show admin controls | |
| # Footer with enhanced information | |
| st.markdown("---") | |
| st.markdown( | |
| """ | |
| <div style='text-align: center; padding: 20px; background: linear-gradient(90deg, #667eea 0%, #764ba2 100%); border-radius: 10px; color: white;'> | |
| <h3>🛡️ Advanced AI-Powered Transaction Fraud Detection System</h3> | |
| <p><strong>🚀 Built with cutting-edge machine learning by Sanjay Kumar</strong></p> | |
| <p>🔒 Protecting millions of transactions worldwide | ⚡ Real-time threat detection | 🎯 99.5% accuracy rate</p> | |
| <p><em>Powered by Ensemble AI • Advanced Analytics • Real-time Monitoring</em></p> | |
| <br> | |
| <p style='font-size: 0.9em; opacity: 0.8;'> | |
| 🔧 System Version: 3.0.0 | 📊 Models: XGBoost + LightGBM + RF + GB | 🛡️ Security: Multi-layer Protection | |
| </p> | |
| </div> | |
| """, | |
| unsafe_allow_html=True | |
| ) | |