refactor main.py and separate concerns

gen-ai/config.py

@@ -0,0 +1,15 @@
+import os
+from typing import Literal
+
+from dotenv import load_dotenv
+
+load_dotenv()
+
+LOGOS_BASE_URL = "https://logos.aet.cit.tum.de/v1"
+LOGOS_MODEL = "openai/gpt-oss-120b"
+OPENAI_MODEL = "gpt-4o-mini"
+LM_STUDIO_BASE_URL = os.getenv("LM_STUDIO_BASE_URL", "http://localhost:1234/v1")
+LM_STUDIO_MODEL = os.getenv("LM_STUDIO_MODEL", "local-model")
+DEFAULT_LLM_PROVIDER = "logos"
+
+Provider = Literal["logos", "openai", "local"]

gen-ai/llm.py

@@ -0,0 +1,106 @@
+import json
+import os
+import re
+
+from fastapi import HTTPException
+from openai import OpenAI, OpenAIError
+
+from config import (
+    DEFAULT_LLM_PROVIDER,
+    LM_STUDIO_BASE_URL,
+    LM_STUDIO_MODEL,
+    LOGOS_BASE_URL,
+    LOGOS_MODEL,
+    OPENAI_MODEL,
+    Provider,
+)
+from schemas import Ingredient
+
+# Re-exported so callers can `except OpenAIError` without importing openai directly.
+__all__ = [
+    "OpenAI",
+    "OpenAIError",
+    "NO_LLM_NOTE",
+    "CANNED_INGREDIENTS",
+    "get_client",
+    "openai_available",
+    "normalize_provider",
+    "create_chat_completion",
+    "parse_json_content",
+]
+
+NO_LLM_NOTE = "No LLM is currently available — showing a canned example response."
+
+# Shown when no LLM provider is configured/reachable, so the UI always has something to display.
+CANNED_INGREDIENTS = [
+    Ingredient(name="Spaghetti", quantity="400", unit="g", category="Pantry"),
+    Ingredient(name="Garlic", quantity="2", unit="piece", category="Produce"),
+    Ingredient(name="Olive oil", quantity="30", unit="ml", category="Pantry"),
+    Ingredient(name="Parmesan cheese", quantity="50", unit="g", category="Dairy"),
+    Ingredient(name="Salt", quantity="N/A", unit="N/A", category="Spices"),
+]
+
+
+def get_client(provider: Provider) -> OpenAI:
+    if provider == "openai":
+        api_key = os.getenv("OPENAI_API_KEY")
+        if not api_key:
+            raise HTTPException(status_code=500, detail="OPENAI_API_KEY is not configured")
+        return OpenAI(api_key=api_key)
+
+    if provider == "local":
+        # LM Studio's OpenAI-compatible server doesn't validate the key, but the
+        # client requires a non-empty string.
+        return OpenAI(api_key="lm-studio", base_url=LM_STUDIO_BASE_URL)
+
+    api_key = os.getenv("LOGOS_KEY")
+    if not api_key:
+        raise HTTPException(status_code=500, detail="LOGOS_KEY is not configured")
+    return OpenAI(api_key=api_key, base_url=LOGOS_BASE_URL)
+
+
+def openai_available() -> bool:
+    return bool(os.getenv("OPENAI_API_KEY"))
+
+
+def normalize_provider(provider: str | None) -> Provider:
+    if provider == "local":
+        return "local"
+    # OpenAI is opt-in and requires OPENAI_API_KEY; without it we always fall back to
+    # Logos, which is the hard dependency for this service.
+    if provider == "openai" and openai_available():
+        return "openai"
+    return DEFAULT_LLM_PROVIDER
+
+
+def create_chat_completion(provider: Provider, messages: list[dict[str, str]]):
+    if provider == "openai":
+        model = OPENAI_MODEL
+    elif provider == "local":
+        model = LM_STUDIO_MODEL
+    else:
+        model = LOGOS_MODEL
+
+    kwargs = {
+        "model": model,
+        "messages": messages,
+        # Low temperature keeps categorization deterministic so the same ingredient
+        # is not labelled differently across requests.
+        "temperature": 0,
+    }
+    if provider == "openai":
+        kwargs["response_format"] = {"type": "json_object"}
+    return get_client(provider).chat.completions.create(**kwargs)
+
+
+def parse_json_content(content: str | None) -> dict:
+    if not content:
+        raise ValueError("LLM response was empty")
+
+    try:
+        return json.loads(content)
+    except json.JSONDecodeError as e:
+        match = re.search(r"\{.*\}", content, re.DOTALL)
+        if not match:
+            raise ValueError(f"LLM response was not valid JSON: {e}") from None
+        return json.loads(match.group(0))