forecasting/backend/api/ai_insights.py

"""
AI Insights API Endpoints

Serves pre-computed daily insights to the dashboard and allows manual generation.
"""
import logging
from datetime import datetime, timezone

from fastapi import APIRouter, Depends, HTTPException, Query
from sqlalchemy import text
from sqlalchemy.ext.asyncio import AsyncSession

from database import get_db
from auth import get_current_user

logger = logging.getLogger(__name__)
router = APIRouter()

# Rate limit: minimum minutes between manual generations
MANUAL_RATE_LIMIT_MINUTES = 5


@router.get("/latest")
async def get_latest_insight(
    db: AsyncSession = Depends(get_db),
    current_user: dict = Depends(get_current_user)
):
    """Get the most recent AI insight for the dashboard card."""
    result = await db.execute(
        text("""
            SELECT id, generated_at, insight_type, content, model,
                   input_tokens, output_tokens, triggered_by
            FROM ai_insights
            ORDER BY generated_at DESC
            LIMIT 1
        """)
    )
    row = result.fetchone()
    if not row:
        return None

    return {
        "id": row.id,
        "generated_at": row.generated_at.isoformat(),
        "insight_type": row.insight_type,
        "content": row.content,
        "model": row.model,
        "input_tokens": row.input_tokens,
        "output_tokens": row.output_tokens,
        "triggered_by": row.triggered_by,
    }


@router.get("/history")
async def get_insight_history(
    limit: int = Query(default=20, ge=1, le=100),
    offset: int = Query(default=0, ge=0),
    db: AsyncSession = Depends(get_db),
    current_user: dict = Depends(get_current_user)
):
    """Get historical AI insights with pagination."""
    result = await db.execute(
        text("""
            SELECT id, generated_at, insight_type, content, model,
                   input_tokens, output_tokens, triggered_by
            FROM ai_insights
            ORDER BY generated_at DESC
            LIMIT :limit OFFSET :offset
        """),
        {"limit": limit, "offset": offset}
    )

    count_result = await db.execute(text("SELECT COUNT(*) FROM ai_insights"))
    total = count_result.scalar()

    insights = []
    for row in result.fetchall():
        insights.append({
            "id": row.id,
            "generated_at": row.generated_at.isoformat(),
            "insight_type": row.insight_type,
            "content": row.content,
            "model": row.model,
            "input_tokens": row.input_tokens,
            "output_tokens": row.output_tokens,
            "triggered_by": row.triggered_by,
        })

    return {"insights": insights, "total": total}


@router.get("/usage")
async def get_usage_stats(
    db: AsyncSession = Depends(get_db),
    current_user: dict = Depends(get_current_user)
):
    """Get token usage statistics for the current month."""
    result = await db.execute(
        text("""
            SELECT
                COUNT(*) as generation_count,
                COALESCE(SUM(input_tokens), 0) as total_input_tokens,
                COALESCE(SUM(output_tokens), 0) as total_output_tokens
            FROM ai_insights
            WHERE generated_at >= DATE_TRUNC('month', CURRENT_DATE)
        """)
    )
    row = result.fetchone()

    # Also get today's usage for budget display
    today_result = await db.execute(
        text("""
            SELECT COALESCE(SUM(input_tokens + output_tokens), 0) as today_total
            FROM ai_insights
            WHERE generated_at >= CURRENT_DATE
        """)
    )
    today_row = today_result.fetchone()

    return {
        "month": {
            "generations": row.generation_count,
            "input_tokens": row.total_input_tokens,
            "output_tokens": row.total_output_tokens,
            "total_tokens": row.total_input_tokens + row.total_output_tokens,
        },
        "today_tokens": today_row.today_total if today_row else 0,
    }


@router.post("/generate")
async def generate_insight_manual(
    db: AsyncSession = Depends(get_db),
    current_user: dict = Depends(get_current_user)
):
    """
    Manually trigger AI insight generation.
    Rate-limited to prevent accidental spam.
    """
    # Check rate limit
    result = await db.execute(
        text("""
            SELECT generated_at FROM ai_insights
            WHERE triggered_by = 'manual'
            ORDER BY generated_at DESC
            LIMIT 1
        """)
    )
    last_manual = result.fetchone()

    if last_manual:
        elapsed = datetime.now(timezone.utc) - last_manual.generated_at.replace(tzinfo=timezone.utc)
        if elapsed.total_seconds() < MANUAL_RATE_LIMIT_MINUTES * 60:
            remaining = MANUAL_RATE_LIMIT_MINUTES - (elapsed.total_seconds() / 60)
            raise HTTPException(
                status_code=429,
                detail=f"Rate limited. Please wait {remaining:.0f} more minutes."
            )

    # Run generation
    from jobs.ai_insights import generate_insight

    result = await generate_insight(db, triggered_by="manual")

    if result.get("success"):
        return {
            "success": True,
            "content": result["content"],
            "input_tokens": result["input_tokens"],
            "output_tokens": result["output_tokens"],
            "model": result["model"],
        }
    else:
        raise HTTPException(
            status_code=400,
            detail=result.get("error", "Failed to generate insight")
        )