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>

app/services/llm/openai_provider.py

@@ -0,0 +1,44 @@
+import json
+import logging
+
+from openai import AsyncOpenAI
+
+from app.config import settings
+from app.models import Movie
+from app.services.llm.base import SYSTEM_PROMPT, LLMProvider, build_user_message
+
+logger = logging.getLogger("movie-night.llm.openai")
+
+
+class OpenAIProvider(LLMProvider):
+    def __init__(self):
+        kwargs = {"api_key": settings.llm_api_key}
+        if settings.llm_base_url:
+            kwargs["base_url"] = settings.llm_base_url
+        self.client = AsyncOpenAI(**kwargs)
+        self.model = settings.llm_model or "gpt-4o"
+
+    async def get_recommendations(self, mood: str, candidates: list[Movie], max_results: int = 6) -> list[dict]:
+        system = SYSTEM_PROMPT.format(max_results=max_results)
+        user_msg = build_user_message(mood, candidates)
+
+        logger.info(f"Calling OpenAI ({self.model}) with {len(candidates)} candidates")
+
+        response = await self.client.chat.completions.create(
+            model=self.model,
+            max_tokens=2048,
+            messages=[
+                {"role": "system", "content": system},
+                {"role": "user", "content": user_msg},
+            ],
+            response_format={"type": "json_object"},
+        )
+
+        text = response.choices[0].message.content.strip()
+
+        try:
+            data = json.loads(text)
+            return data.get("recommendations", [])
+        except json.JSONDecodeError:
+            logger.error(f"Failed to parse LLM response: {text[:200]}")
+            return []