Initial commit — Movie Night media discovery app

AI-powered web app that recommends unwatched movies from a Jellyfin library based on natural language mood input. Jellyfin auth, modular LLM backend (Claude/OpenAI/Ollama), two-tier pre-filter + AI ranking, mobile-responsive dark theme UI with poster cards and deep links. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
2026-03-14 19:20:56 -07:00
commit 3d5de06b44
30 changed files with 1881 additions and 0 deletions
@@ -0,0 +1,100 @@
+import json
+import logging
+import time
+
+from fastapi import APIRouter, HTTPException, Request
+
+from app.config import settings
+from app.database import 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)
+
+    return MoodResponse(
+        recommendations=recommendations,
+        meta={
+            "total_unwatched": total_unwatched,
+            "candidates_evaluated": len(candidates),
+            "processing_time_ms": elapsed_ms,
+            "mood": body.mood,
+        },
+    )