movie-night/app/routers/mood.py

import json
import logging
import time
from datetime import datetime, timezone

from fastapi import APIRouter, HTTPException, Request

from app.config import settings
from app.database import get_db, get_unwatched_movies
from app.models import MoodRequest, MoodResponse, Recommendation
from app.routers.auth import get_current_user
from app.services.jellyfin import get_deep_link, get_poster_url
from app.services.llm import get_llm_provider
from app.services.prefilter import prefilter_candidates

logger = logging.getLogger("movie-night.mood")

router = APIRouter()


@router.post("/mood", response_model=MoodResponse)
async def get_mood_recommendations(request: Request, body: MoodRequest):
    user = await get_current_user(request)

    if not body.mood.strip():
        raise HTTPException(status_code=400, detail="Please describe what you're in the mood for")

    start_time = time.time()

    # Build list of user IDs to check watch state against
    user_ids = [user["id"]] + body.additional_user_ids

    # Get unwatched movies from cache
    unwatched_raw = await get_unwatched_movies(user_ids)
    total_unwatched = len(unwatched_raw)

    if total_unwatched == 0:
        return MoodResponse(
            recommendations=[],
            meta={
                "total_unwatched": 0,
                "candidates_evaluated": 0,
                "processing_time_ms": 0,
                "mood": body.mood,
            },
        )

    # Pre-filter to narrow candidates
    candidates = prefilter_candidates(
        unwatched_raw, body.mood, max_candidates=settings.max_candidates
    )
    logger.info(f"Pre-filtered {total_unwatched} movies to {len(candidates)} candidates for mood: '{body.mood}'")

    # Get recommendations from LLM
    try:
        llm = get_llm_provider()
        raw_recs = await llm.get_recommendations(
            body.mood, candidates, max_results=settings.max_recommendations
        )
    except Exception as e:
        logger.error(f"LLM error: {e}")
        raise HTTPException(status_code=502, detail="Failed to get recommendations from AI. Please try again.")

    # Validate and enrich recommendations
    candidate_map = {m.jellyfin_id: m for m in candidates}
    recommendations = []

    for rec in raw_recs:
        jf_id = rec.get("jellyfin_id")
        if not jf_id or jf_id not in candidate_map:
            logger.warning(f"LLM returned invalid jellyfin_id: {jf_id}")
            continue

        movie = candidate_map[jf_id]
        recommendations.append(
            Recommendation(
                jellyfin_id=movie.jellyfin_id,
                title=movie.title,
                year=movie.year,
                genres=movie.genres,
                community_rating=movie.community_rating,
                runtime_minutes=movie.runtime_minutes,
                content_rating=movie.content_rating,
                poster_url=get_poster_url(movie.jellyfin_id),
                deep_link=get_deep_link(movie.jellyfin_id),
                reasoning=rec.get("reasoning", "A great match for your mood!"),
                match_score=rec.get("match_score", 0.5),
            )
        )

    elapsed_ms = int((time.time() - start_time) * 1000)

    meta = {
        "total_unwatched": total_unwatched,
        "candidates_evaluated": len(candidates),
        "processing_time_ms": elapsed_ms,
        "mood": body.mood,
    }

    # Save to search history
    try:
        db = await get_db()
        try:
            await db.execute(
                "INSERT INTO search_history (user_id, mood, results, meta, created_at) VALUES (?, ?, ?, ?, ?)",
                (
                    user["id"],
                    body.mood,
                    json.dumps([r.model_dump() for r in recommendations]),
                    json.dumps(meta),
                    datetime.now(timezone.utc).isoformat(),
                ),
            )
            await db.commit()
        finally:
            await db.close()
    except Exception as e:
        logger.warning(f"Failed to save search history: {e}")

    return MoodResponse(recommendations=recommendations, meta=meta)


@router.get("/history")
async def get_search_history(request: Request):
    user = await get_current_user(request)
    db = await get_db()
    try:
        cursor = await db.execute(
            "SELECT id, mood, results, meta, created_at FROM search_history WHERE user_id = ? ORDER BY created_at DESC LIMIT 20",
            (user["id"],),
        )
        rows = await cursor.fetchall()
        return [
            {
                "id": row["id"],
                "mood": row["mood"],
                "results": json.loads(row["results"]),
                "meta": json.loads(row["meta"]) if row["meta"] else {},
                "created_at": row["created_at"],
            }
            for row in rows
        ]
    finally:
        await db.close()


@router.delete("/history/{entry_id}")
async def delete_history_entry(entry_id: int, request: Request):
    user = await get_current_user(request)
    db = await get_db()
    try:
        await db.execute(
            "DELETE FROM search_history WHERE id = ? AND user_id = ?",
            (entry_id, user["id"]),
        )
        await db.commit()
        return {"ok": True}
    finally:
        await db.close()