backend/budget_allocator.py

import os
from dotenv import load_dotenv
from openai import OpenAI
from backend import expense_manager
load_dotenv()

client = OpenAI(
    api_key=os.environ.get("GROQ_API_KEY"),
    base_url="https://api.groq.com/openai/v1",
)

def suggest_budget(user_id: str, expenses=None):
    """
    Analyze user expenses and return a budget suggestion using Groq AI.

    Input: user_id and optional expense list
    Output: dictionary with numeric split + AI explanation
    Example:
    {
        "Spending": 0.6,
        "Savings": 0.3,
        "Investments": 0.1,
        "message": "You spend most on rent, consider lowering entertainment."
    }
    """
    if expenses is None:
        expenses = expense_manager.list_expenses(user_id)

    if not expenses:
        return {
            "Spending": 0.6,
            "Savings": 0.3,
            "Investments": 0.1,
            "message": "No expense data found — using default 60/30/10 split."
        }

    summary_lines = []
    for e in expenses:
        summary_lines.append(f"{e['name']}: {e['amount']} ({e['category']}) on {e['date']}")
    summary_text = "\n".join(summary_lines)

    prompt = f"""
You are FinSync AI, a financial budgeting assistant.

User's Expense Summary:
{summary_text}

Task:
1. Analyze how the user spends money.
2. Suggest an ideal percentage allocation among:
   - Spending (daily essentials, bills, rent)
   - Savings (emergency fund)
   - Investments (future growth)
3. Return result in strict JSON format like this:
{{
  "Spending": 0.55,
  "Savings": 0.30,
  "Investments": 0.15,
  "message": "Short reasoning or advice"
}}
"""

    try:
        response = client.responses.create(
            model=GROQ_MODEL,
            input=prompt,
            temperature=0.3
        )
        ai_text = response.output_text.strip()

        import json, re
        json_match = re.search(r"\{[\s\S]*\}", ai_text)
        if json_match:
            parsed = json.loads(json_match.group(0))
            for k in ["Spending", "Savings", "Investments"]:
                if k not in parsed:
                    parsed[k] = 0.0
            return parsed
        else:
            return {
                "Spending": 0.6,
                "Savings": 0.3,
                "Investments": 0.1,
                "message": ai_text or "AI could not parse response properly."
            }

    except Exception as e:
        return {
            "Spending": 0.6,
            "Savings": 0.3,
            "Investments": 0.1,
            "message": f"Groq API error: {e}"
        }