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a282d4b | 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 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 | import os
import sys
import uuid
import random
from datetime import datetime, timedelta
# Add parent directory to path so we can import from app
sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))
from app.database.database import SessionLocal, engine, SQLALCHEMY_DATABASE_URL
from app.database.models import (
Base, User, Account, Transaction, Subscription,
Goal, Investment, AIInsight, FraudLog, Notification, AnalyticsSnapshot
)
# Create tables
Base.metadata.create_all(bind=engine)
def seed_data():
db = SessionLocal()
print(f"Seeding into: {SQLALCHEMY_DATABASE_URL}")
# Check if we already have users
if db.query(User).count() > 0:
print("Database already seeded.")
db.close()
return
print("Seeding database...")
personas = ["Saver", "Investor", "Impulsive Spender", "Minimalist", "Risk Taker"]
merchants = ["Swiggy", "Amazon", "Netflix", "Uber", "Fuel", "Salary", "SIP",
"Starbucks", "Apple", "Walmart"]
categories = ["Food", "Shopping", "Entertainment", "Transport", "Income",
"Investment", "Groceries", "Tech", "Utilities"]
for persona in personas:
try:
user = User(
email=f"{persona.lower().replace(' ', '_')}@example.com",
password_hash="hashed_password",
profile_data={"name": f"{persona} User", "phone": "+1234567890"},
financial_personality=persona,
ai_personalization_settings={"theme": "dark", "notifications": "all"}
)
db.add(user)
db.flush() # get user.id without committing
# ── Accounts ──────────────────────────────────────────────────────
checking = Account(
user_id=user.id, type="checking",
balance=random.uniform(1000.0, 10000.0), currency="USD"
)
savings = Account(
user_id=user.id, type="savings",
balance=random.uniform(5000.0, 50000.0), currency="USD"
)
db.add_all([checking, savings])
db.flush()
# ── Subscriptions ─────────────────────────────────────────────────
# Active subscription (high usage)
db.add(Subscription(
user_id=user.id, merchant="Netflix", amount=15.99,
billing_cycle="monthly", active=True,
ai_usage_detection={"usage_frequency": "high", "recommendation": "keep"}
))
# Unused subscription — triggers unused detection in subscriptions.py
db.add(Subscription(
user_id=user.id, merchant="Spotify", amount=9.99,
billing_cycle="monthly", active=True,
ai_usage_detection={"usage_frequency": "low", "recommendation": "cancel"}
))
# Duplicate subscription — triggers duplicate detection in subscriptions.py
# (second Netflix entry for the same user)
db.add(Subscription(
user_id=user.id, merchant="Netflix", amount=15.99,
billing_cycle="monthly", active=True,
ai_usage_detection={"usage_frequency": "medium", "recommendation": "review"}
))
# ── Goals ─────────────────────────────────────────────────────────
db.add(Goal(
user_id=user.id, title="Emergency Fund",
target_amount=10000.0,
current_amount=random.uniform(1000.0, 5000.0),
target_date=datetime.utcnow() + timedelta(days=365),
ai_generated_plan={"monthly_saving_required": 500.0, "risk": "low"}
))
# ── Investments ───────────────────────────────────────────────────
db.add(Investment(
user_id=user.id, asset_name="S&P 500", type="stock",
amount_invested=random.uniform(1000.0, 10000.0),
current_value=random.uniform(1100.0, 12000.0),
portfolio_allocation=50.0,
ai_risk_analysis={"risk_level": "medium", "recommendation": "hold"}
))
# ── Transactions ──────────────────────────────────────────────────
start_date = datetime.utcnow() - timedelta(days=90)
# Monthly salary (3 months)
for i in range(3):
tx_date = start_date + timedelta(days=i * 30)
db.add(Transaction(
account_id=checking.id, amount=5000.0, type="credit",
category="Income", timestamp=tx_date, merchant="Salary",
tags=["salary", "income"],
ai_generated_metadata={"is_recurring": True, "confidence": 0.99},
spending_emotion_label="neutral"
))
# Regular expense transactions
for _ in range(30):
tx_date = start_date + timedelta(days=random.randint(0, 89))
amount = random.uniform(10.0, 500.0)
merchant = random.choice(merchants)
if merchant == "Salary":
continue
# Persona-based spending adjustments
if user.financial_personality == "Saver" and amount > 200:
amount = random.uniform(10.0, 100.0)
elif user.financial_personality == "Impulsive Spender":
amount = random.uniform(50.0, 800.0)
tx = Transaction(
account_id=checking.id, amount=amount, type="debit",
category=random.choice(categories),
timestamp=tx_date, merchant=merchant,
tags=["expense"],
ai_generated_metadata={"category_confidence": 0.9},
spending_emotion_label=random.choice(["happy", "regret", "neutral", "essential"])
)
db.add(tx)
db.flush()
# Seed a fraud log for ~5% of transactions
if random.random() < 0.05:
db.add(FraudLog(
transaction_id=tx.id,
risk_score=random.uniform(0.7, 0.99),
suspicious_activity_details="Unusual location and high amount for this merchant.",
status="pending"
))
# Late-night transaction — ensures behavior.py late-night detection fires
late_night_date = start_date + timedelta(days=random.randint(1, 80),
hours=23, minutes=random.randint(0, 59))
db.add(Transaction(
account_id=checking.id,
amount=random.uniform(50.0, 300.0),
type="debit",
category="Entertainment",
timestamp=late_night_date,
merchant="Online Store",
tags=["late-night", "impulse"],
ai_generated_metadata={"category_confidence": 0.85},
spending_emotion_label="regret"
))
# ── Supporting records ────────────────────────────────────────────
db.add(AIInsight(
user_id=user.id, type="cashflow",
content=f"You are spending 20% more on {random.choice(categories)} this month."
))
db.add(Notification(
user_id=user.id, title="Weekly Summary",
message="Your weekly financial summary is ready.", type="insight"
))
db.add(AnalyticsSnapshot(
user_id=user.id, date=datetime.utcnow(),
total_balance=checking.balance + savings.balance,
total_spending=2000.0, total_savings=3000.0,
financial_score=random.uniform(60.0, 95.0),
trends_json={"spending_trend": "down", "savings_trend": "up"}
))
db.commit()
print(f" ✓ Seeded user: {persona}")
except Exception as e:
db.rollback()
print(f" ✗ Failed to seed user '{persona}': {e}")
db.close()
print("Database seeded successfully!")
if __name__ == "__main__":
seed_data()
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