359 lines
14 KiB
Python
359 lines
14 KiB
Python
"""
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AI Service — Groq API integration with financial context injection.
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Builds a anonymized summary of the user's finances and sends to Groq.
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No account names or personal details are sent — only aggregated numbers.
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"""
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import logging
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import json
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from datetime import date, timedelta
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from flask import current_app
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from sqlalchemy import func
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from app.extensions import db
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from app.models.transaction import Transaction
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from app.models.category import Category
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from app.models.account import Account
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from app.models.budget import Budget
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from app.models.goal import Goal
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from app.models.investment import Investment
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from app.models.ai_insight import AiInsight
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from app.services.budget_service import get_total_spent, get_total_budget
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log = logging.getLogger(__name__)
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# ── Context builder ───────────────────────────────────────────────────────────
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def build_context(days=90):
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"""
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Build an anonymized financial context string to inject into the AI prompt.
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Covers last `days` days of activity.
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"""
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today = date.today()
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since = today - timedelta(days=days)
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curr_month = today.strftime('%Y-%m')
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currency = current_app.config.get('APP_CURRENCY', 'USD')
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symbol = current_app.config.get('APP_CURRENCY_SYMBOL', '$')
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lines = [f"Financial data summary (currency: {currency}):"]
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# ── This month summary ────────────────────────────────────────────────────
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month_income = db.session.query(
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func.coalesce(func.sum(Transaction.amount), 0)
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).filter(
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Transaction.transaction_type == 'income',
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Transaction.date >= date(today.year, today.month, 1),
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Transaction.date <= today,
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).scalar()
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month_expense = db.session.query(
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func.coalesce(func.sum(Transaction.amount), 0)
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).filter(
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Transaction.transaction_type == 'expense',
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Transaction.date >= date(today.year, today.month, 1),
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Transaction.date <= today,
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).scalar()
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lines.append(f"\nCURRENT MONTH ({today.strftime('%B %Y')}):")
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lines.append(f" Income: {symbol}{float(month_income):,.2f}")
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lines.append(f" Expenses: {symbol}{float(month_expense):,.2f}")
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lines.append(f" Net: {symbol}{float(month_income) - float(month_expense):,.2f}")
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# ── Top spending categories (this month) ──────────────────────────────────
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top_cats = db.session.query(
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Category.name,
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func.sum(Transaction.amount).label('total')
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).join(Transaction, Transaction.category_id == Category.id)\
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.filter(
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Transaction.transaction_type == 'expense',
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Transaction.date >= date(today.year, today.month, 1),
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Transaction.date <= today,
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).group_by(Category.id)\
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.order_by(func.sum(Transaction.amount).desc())\
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.limit(6).all()
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if top_cats:
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lines.append("\nTOP SPENDING CATEGORIES (this month):")
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for cat, total in top_cats:
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lines.append(f" {cat}: {symbol}{float(total):,.2f}")
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# ── Budget status ─────────────────────────────────────────────────────────
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from app.services.budget_service import get_budget_summary
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budget_summary = get_budget_summary(curr_month)
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over_budget = [b for b in budget_summary if b['is_over']]
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near_budget = [b for b in budget_summary if b['has_budget'] and b['pct'] and b['pct'] >= 80 and not b['is_over']]
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total_budget = get_total_budget(curr_month)
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total_spent_month = get_total_spent(curr_month)
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if total_budget > 0:
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lines.append(f"\nBUDGET STATUS ({today.strftime('%B %Y')}):")
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lines.append(f" Total budget: {symbol}{total_budget:,.2f}")
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lines.append(f" Total spent: {symbol}{total_spent_month:,.2f} ({round(total_spent_month/total_budget*100,1)}%)")
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if over_budget:
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lines.append(f" OVER BUDGET: {', '.join(b['category'].name for b in over_budget)}")
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if near_budget:
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lines.append(f" Near limit (80%+): {', '.join(b['category'].name + ' ' + str(b['pct']) + '%' for b in near_budget)}")
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# ── Accounts net worth ────────────────────────────────────────────────────
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accounts = Account.query.filter_by(is_active=True).all()
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total_assets = sum(float(a.balance) for a in accounts if float(a.balance) > 0 and a.account_type != 'credit_card')
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total_liab = sum(abs(float(a.balance)) for a in accounts if float(a.balance) < 0)
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net_worth = total_assets - total_liab
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lines.append(f"\nNET WORTH: {symbol}{net_worth:,.2f}")
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lines.append(f" Assets: {symbol}{total_assets:,.2f}")
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lines.append(f" Liabilities: {symbol}{total_liab:,.2f}")
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# ── Goals ────────────────────────────────────────────────────────────────
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active_goals = Goal.query.filter_by(is_completed=False).all()
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if active_goals:
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lines.append("\nACTIVE SAVINGS GOALS:")
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for g in active_goals:
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lines.append(
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f" {g.name}: {symbol}{float(g.current_amount):,.2f} / {symbol}{float(g.target_amount):,.2f} "
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f"({g.progress_percent}%)"
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+ (f" — target {g.target_date.strftime('%b %Y')}" if g.target_date else "")
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)
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# ── Investments ───────────────────────────────────────────────────────────
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investments = Investment.query.filter_by(is_active=True).all()
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if investments:
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total_inv_value = sum(i.current_value for i in investments)
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total_inv_gain = sum(i.unrealized_gain for i in investments)
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lines.append(f"\nINVESTMENT PORTFOLIO:")
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lines.append(f" Total value: {symbol}{total_inv_value:,.2f}")
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lines.append(f" Unrealized P&L: {symbol}{total_inv_gain:,.2f}")
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# ── Recent 20 transactions ────────────────────────────────────────────────
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recent = Transaction.query\
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.filter(
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Transaction.transaction_type.in_(['income', 'expense']),
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Transaction.date >= since,
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)\
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.order_by(Transaction.date.desc())\
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.limit(20).all()
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if recent:
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lines.append(f"\nRECENT TRANSACTIONS (last {days} days, newest first):")
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for txn in recent:
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cat = txn.category.name if txn.category else 'Uncategorized'
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sign = '+' if txn.transaction_type == 'income' else '-'
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lines.append(
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f" {txn.date.strftime('%b %d')} | {cat} | {sign}{symbol}{float(txn.amount):,.2f} | {txn.description}"
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)
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return '\n'.join(lines)
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# ── Groq chat (streaming) ─────────────────────────────────────────────────────
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def stream_chat(user_message, context=None):
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"""
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Generator that yields SSE-formatted chunks from Groq streaming API.
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Yields: 'data: <chunk>\n\n' or 'data: [DONE]\n\n'
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"""
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api_key = current_app.config.get('GROQ_API_KEY', '')
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model = current_app.config.get('GROQ_MODEL', 'llama-3.3-70b-versatile')
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if not api_key:
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yield 'data: AI assistant is not configured. Please set GROQ_API_KEY in your .env file.\n\n'
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yield 'data: [DONE]\n\n'
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return
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if context is None:
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try:
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context = build_context()
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except Exception as e:
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log.error(f'[ai] context build failed: {e}')
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context = '(financial context unavailable)'
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system_prompt = (
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"You are a concise, helpful personal finance assistant. "
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"Answer questions based on the financial data provided. "
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"Be specific with numbers. Keep answers focused and practical. "
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"Do not make up data that isn't in the context. "
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"If something isn't in the data, say so briefly."
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)
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messages = [
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{"role": "system", "content": system_prompt + "\n\n" + context},
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{"role": "user", "content": user_message},
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]
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try:
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import requests
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resp = requests.post(
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'https://api.groq.com/openai/v1/chat/completions',
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headers={
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'Authorization': f'Bearer {api_key}',
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'Content-Type': 'application/json',
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},
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json={
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'model': model,
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'messages': messages,
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'max_tokens': 1000,
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'stream': True,
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},
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stream=True,
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timeout=60,
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)
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resp.raise_for_status()
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full_text = []
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for line in resp.iter_lines():
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if not line:
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continue
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line = line.decode('utf-8')
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if not line.startswith('data: '):
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continue
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data = line[6:]
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if data == '[DONE]':
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break
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try:
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chunk = json.loads(data)
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delta = chunk['choices'][0]['delta'].get('content', '')
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if delta:
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full_text.append(delta)
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# Escape newlines for SSE
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safe = delta.replace('\n', '<br>')
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yield f'data: {safe}\n\n'
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except (json.JSONDecodeError, KeyError, IndexError):
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continue
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# Store in DB
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if full_text:
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_save_insight(
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content=''.join(full_text),
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insight_type='chat_response',
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prompt_summary=user_message[:500],
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model=model,
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)
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yield 'data: [DONE]\n\n'
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except requests.exceptions.Timeout:
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yield 'data: Request timed out. Please try again.\n\n'
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yield 'data: [DONE]\n\n'
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except requests.exceptions.HTTPError as e:
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if e.response.status_code == 429:
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yield 'data: Rate limit reached. Please wait a moment and try again.\n\n'
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elif e.response.status_code == 401:
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yield 'data: Invalid Groq API key. Please check your .env configuration.\n\n'
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else:
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yield f'data: API error ({e.response.status_code}). Please try again.\n\n'
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yield 'data: [DONE]\n\n'
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except Exception as e:
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log.error(f'[ai] stream error: {e}')
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yield 'data: AI assistant is temporarily unavailable.\n\n'
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yield 'data: [DONE]\n\n'
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# ── Daily insight (non-streaming) ─────────────────────────────────────────────
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def generate_daily_insight():
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"""
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Generate and store a daily summary insight (called by cron script).
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Returns the insight text or None on failure.
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"""
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api_key = current_app.config.get('GROQ_API_KEY', '')
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model = current_app.config.get('GROQ_MODEL', 'llama-3.3-70b-versatile')
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if not api_key:
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log.warning('[ai] GROQ_API_KEY not set, skipping daily insight')
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return None
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today = date.today()
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# Don't regenerate if already done today
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existing = AiInsight.query.filter_by(
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insight_date=today,
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insight_type='daily_summary'
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).first()
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if existing:
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return existing.content
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try:
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context = build_context(days=30)
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except Exception as e:
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log.error(f'[ai] context build failed: {e}')
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return None
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prompt = (
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"Based on this month's financial data, give me a brief daily summary "
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"(3-5 sentences). Cover: spending vs income, any budget alerts, "
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"and one actionable tip. Be specific with numbers."
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)
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try:
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import requests
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resp = requests.post(
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'https://api.groq.com/openai/v1/chat/completions',
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headers={
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'Authorization': f'Bearer {api_key}',
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'Content-Type': 'application/json',
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},
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json={
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'model': model,
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'messages': [
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{"role": "system", "content": "You are a concise personal finance assistant."},
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{"role": "system", "content": context},
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{"role": "user", "content": prompt},
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],
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'max_tokens': 400,
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'stream': False,
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},
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timeout=30,
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)
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resp.raise_for_status()
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data = resp.json()
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content = data['choices'][0]['message']['content']
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tokens = data.get('usage', {}).get('total_tokens')
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_save_insight(
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content=content,
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insight_type='daily_summary',
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prompt_summary='Daily auto-summary',
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model=model,
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tokens=tokens,
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)
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return content
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except Exception as e:
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log.error(f'[ai] daily insight failed: {e}')
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return None
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# ── Helper ────────────────────────────────────────────────────────────────────
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def _save_insight(content, insight_type, prompt_summary=None, model=None, tokens=None):
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try:
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insight = AiInsight(
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insight_date=date.today(),
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insight_type=insight_type,
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content=content,
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prompt_summary=prompt_summary,
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model_used=model,
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tokens_used=tokens,
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)
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db.session.add(insight)
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db.session.commit()
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except Exception as e:
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db.session.rollback()
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log.error(f'[ai] save insight failed: {e}')
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def get_latest_daily_insight():
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"""Return today's daily insight if it exists, else the most recent one."""
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today = date.today()
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insight = AiInsight.query.filter_by(
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insight_date=today,
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insight_type='daily_summary'
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).first()
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if not insight:
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insight = AiInsight.query\
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.filter_by(insight_type='daily_summary')\
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.order_by(AiInsight.insight_date.desc())\
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.first()
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return insight
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