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/anthropic.py

@@ -0,0 +1,48 @@
+import json
+import logging
+
+import anthropic
+
+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.anthropic")
+
+
+class AnthropicProvider(LLMProvider):
+    def __init__(self):
+        self.client = anthropic.AsyncAnthropic(api_key=settings.llm_api_key)
+        self.model = settings.llm_model or "claude-sonnet-4-6"
+
+    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 Anthropic ({self.model}) with {len(candidates)} candidates")
+
+        response = await self.client.messages.create(
+            model=self.model,
+            max_tokens=2048,
+            system=system,
+            messages=[{"role": "user", "content": user_msg}],
+        )
+
+        text = response.content[0].text.strip()
+
+        # Parse JSON response
+        try:
+            data = json.loads(text)
+            return data.get("recommendations", [])
+        except json.JSONDecodeError:
+            # Try to extract JSON from the response
+            start = text.find("{")
+            end = text.rfind("}") + 1
+            if start >= 0 and end > start:
+                try:
+                    data = json.loads(text[start:end])
+                    return data.get("recommendations", [])
+                except json.JSONDecodeError:
+                    pass
+            logger.error(f"Failed to parse LLM response: {text[:200]}")
+            return []