curo1305
4c35d7a2a4
All JSON config files (AI settings, doc settings, appearance, themes) now live in the 'config' bucket of storage-service instead of a shared Docker volume. - backend/core/config_storage.py: new async HTTP helpers for config bucket r/w - backend/core/app_config.py: fully async rewrite; all load_*/save_*/seed_* functions use config_storage instead of filesystem - backend/routers/settings.py: all asyncio.to_thread() wrappers removed; direct await calls throughout; update_theme reads via load_theme_by_id() - backend/main.py: await seed_builtin_themes() directly (no to_thread) - ai-service: remove CONFIG_PATH, add STORAGE_SERVICE_URL; config_reader now fetches from storage-service via httpx - doc-service: config_reader rewritten to fetch/write via storage-service - docker-compose: remove app_config volume; add storage-service depends_on for ai-service; remove DATA_DIR and CONFIG_PATH from doc-service Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
410 lines
14 KiB
Python
410 lines
14 KiB
Python
"""
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Per-service runtime config helpers.
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All config files are stored in the 'config' bucket of the storage-service.
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Every function is async — callers must await them.
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Key layout in the config bucket:
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ai_service_config.json
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doc_service_config.json
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appearance_config.json
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themes/{id}.json
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"""
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import copy
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import logging
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import re
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from copy import deepcopy
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from pydantic import BaseModel
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from app.core import config_storage
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logger = logging.getLogger(__name__)
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# ── AI service config schemas ──────────────────────────────────────────────────
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class AnthropicConfig(BaseModel):
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api_key: str = ""
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model: str = "claude-haiku-4-5-20251001"
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class OllamaConfig(BaseModel):
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base_url: str = "http://host.docker.internal:11434/v1"
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model: str = "llama3.2"
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api_key: str = "ollama"
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class LMStudioConfig(BaseModel):
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base_url: str = "http://host.docker.internal:1234/v1"
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model: str = "local-model"
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api_key: str = "lm-studio"
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class AIServiceConfig(BaseModel):
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provider: str = "lmstudio"
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timeout_seconds: int = 60
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max_retries: int = 2
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anthropic: AnthropicConfig = AnthropicConfig()
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ollama: OllamaConfig = OllamaConfig()
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lmstudio: LMStudioConfig = LMStudioConfig()
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# ── Doc service config schemas ─────────────────────────────────────────────────
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_DOC_SYSTEM_PROMPT_DEFAULT = (
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"You are a financial document analysis assistant. "
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"Given the text extracted from a PDF document, return ONLY a JSON object "
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"with no markdown, no code fences, and no explanation."
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)
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_DOC_USER_TEMPLATE_DEFAULT = (
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'Analyze the following document text and return a JSON object with exactly these keys:\n'
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'title (a short, descriptive human-readable title for this document, e.g. "ACME Corp Invoice April 2026", "Office Supplies Receipt", "Q1 Flower Delivery Order"),\n'
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'document_type (one of: invoice, bill, receipt, order, expense, revenue, unknown),\n'
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'total_amount (string or null),\n'
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'currency (string or null),\n'
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'vendor_name (string or null),\n'
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'customer_name (string or null),\n'
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'billing_address (string or null),\n'
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'customer_address (string or null),\n'
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'invoice_number (string or null),\n'
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'invoice_date (string or null),\n'
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'due_date (string or null),\n'
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'tags (array of short keyword strings describing the document),\n'
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'line_items (array of objects, each with keys: description, amount),\n'
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'suggested_categories (array of 2 to 5 short category name strings a user might want to file this document under, e.g. "Utilities", "Travel", "Software Subscriptions", "Client Invoices").\n'
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'\n'
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'Document text:\n'
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'{text}'
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)
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class DocumentsConfig(BaseModel):
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max_pdf_bytes: int = 20 * 1024 * 1024
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class DocServiceSystemPrompts(BaseModel):
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system: str = _DOC_SYSTEM_PROMPT_DEFAULT
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user_template: str = _DOC_USER_TEMPLATE_DEFAULT
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class DocServiceConfig(BaseModel):
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documents: DocumentsConfig = DocumentsConfig()
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system_prompts: DocServiceSystemPrompts = DocServiceSystemPrompts()
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# ── Masking ────────────────────────────────────────────────────────────────────
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def _mask_key(key: str) -> str:
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if not key or len(key) <= 8:
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return "••••"
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return key[:7] + "••••"
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def _mask_ai_config(data: dict) -> dict:
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masked = copy.deepcopy(data)
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for provider in ("anthropic", "ollama", "lmstudio"):
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if provider in masked and "api_key" in masked[provider]:
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masked[provider]["api_key"] = _mask_key(masked[provider]["api_key"])
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return masked
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# ── Load / Save ────────────────────────────────────────────────────────────────
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async def load_service_config(service: str) -> dict:
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data = await config_storage.read_json(f"{service}_config.json")
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if data is None:
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if service == "ai_service":
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return AIServiceConfig().model_dump()
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if service == "doc_service":
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return DocServiceConfig().model_dump()
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return {}
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return data
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async def save_service_config(service: str, data: dict) -> None:
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await config_storage.write_json(f"{service}_config.json", data)
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# AI service helpers
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async def load_ai_service_config() -> AIServiceConfig:
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raw = await load_service_config("ai_service")
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return AIServiceConfig.model_validate(raw)
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async def save_ai_service_config(config: AIServiceConfig) -> None:
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await save_service_config("ai_service", config.model_dump())
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async def load_ai_service_config_masked() -> dict:
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raw = await load_service_config("ai_service")
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return _mask_ai_config(raw)
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# Doc service helpers
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async def load_doc_service_config() -> DocServiceConfig:
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raw = await load_service_config("doc_service")
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return DocServiceConfig.model_validate(raw)
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async def save_doc_service_config(config: DocServiceConfig) -> None:
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await save_service_config("doc_service", config.model_dump())
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async def load_doc_service_config_masked() -> dict:
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return await load_service_config("doc_service")
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def _merge_api_key(new_key: str, existing_key: str) -> str:
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"""If new_key is empty or a masked value, keep the existing key."""
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if not new_key or "••••" in new_key:
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return existing_key
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return new_key
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# ── System prompts helpers ─────────────────────────────────────────────────────
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SYSTEM_PROMPT_SERVICES: dict[str, str] = {
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"doc_service": "Document Service",
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}
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async def load_all_system_prompts() -> dict:
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"""Return {service_id: {label, system, user_template, default_system, default_user_template}}."""
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result: dict = {}
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for service_id, label in SYSTEM_PROMPT_SERVICES.items():
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config = await load_service_config(service_id)
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prompts = config.get("system_prompts", {})
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defaults = _get_service_prompt_defaults(service_id)
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result[service_id] = {
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"label": label,
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"system": prompts.get("system", defaults["system"]),
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"user_template": prompts.get("user_template", defaults["user_template"]),
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"default_system": defaults["system"],
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"default_user_template": defaults["user_template"],
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}
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return result
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async def save_service_system_prompts(service_id: str, system: str, user_template: str) -> None:
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if service_id not in SYSTEM_PROMPT_SERVICES:
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raise ValueError(f"Unknown service: {service_id!r}")
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config = await load_service_config(service_id)
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config.setdefault("system_prompts", {})
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config["system_prompts"]["system"] = system
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config["system_prompts"]["user_template"] = user_template
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await save_service_config(service_id, config)
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def _get_service_prompt_defaults(service_id: str) -> dict:
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if service_id == "doc_service":
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d = DocServiceSystemPrompts()
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return {"system": d.system, "user_template": d.user_template}
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return {"system": "", "user_template": ""}
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# ── Appearance config ──────────────────────────────────────────────────────────
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class AppearanceConfig(BaseModel):
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theme: str = "default"
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default_mode: str = "system"
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async def load_appearance_config() -> AppearanceConfig:
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data = await config_storage.read_json("appearance_config.json")
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if data is None:
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return AppearanceConfig()
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return AppearanceConfig.model_validate(data)
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async def save_appearance_config(config: AppearanceConfig) -> None:
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await config_storage.write_json("appearance_config.json", config.model_dump())
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# ── Theme file management ──────────────────────────────────────────────────────
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# 9 required colour tokens per mode
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_REQUIRED_TOKENS = frozenset({
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"primary", "primary_hover", "accent", "accent_hover",
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"background", "surface", "border", "text_primary", "text_muted",
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})
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_RGB_RE = re.compile(r"^\d{1,3} \d{1,3} \d{1,3}$")
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_BUILTIN_THEMES: list[dict] = [
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{
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"id": "default",
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"label": "Default",
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"builtin": True,
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"light": {
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"primary": "37 99 235",
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"primary_hover": "29 78 216",
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"accent": "234 179 8",
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"accent_hover": "202 138 4",
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"background": "248 250 252",
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"surface": "255 255 255",
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"border": "226 232 240",
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"text_primary": "15 23 42",
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"text_muted": "100 116 139",
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},
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"dark": {
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"primary": "59 130 246",
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"primary_hover": "37 99 235",
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"accent": "250 204 21",
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"accent_hover": "234 179 8",
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"background": "15 23 42",
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"surface": "30 41 59",
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"border": "51 65 85",
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"text_primary": "203 213 225",
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"text_muted": "148 163 184",
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},
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},
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{
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"id": "pastel",
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"label": "Pastel",
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"builtin": True,
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"light": {
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"primary": "124 58 237",
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"primary_hover": "109 40 217",
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"accent": "236 72 153",
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"accent_hover": "219 39 119",
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"background": "253 244 255",
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"surface": "250 245 255",
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"border": "233 213 255",
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"text_primary": "30 27 75",
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"text_muted": "107 114 128",
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},
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"dark": {
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"primary": "167 139 250",
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"primary_hover": "196 181 253",
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"accent": "244 114 182",
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"accent_hover": "251 164 200",
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"background": "30 20 51",
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"surface": "45 27 78",
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"border": "76 53 117",
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"text_primary": "233 213 255",
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"text_muted": "196 181 253",
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},
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},
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{
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"id": "high-contrast",
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"label": "High Contrast",
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"builtin": True,
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"light": {
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"primary": "30 58 138",
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"primary_hover": "30 64 175",
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"accent": "220 38 38",
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"accent_hover": "185 28 28",
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"background": "255 255 255",
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"surface": "255 255 255",
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"border": "156 163 175",
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"text_primary": "0 0 0",
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"text_muted": "75 85 99",
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},
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"dark": {
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"primary": "96 165 250",
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"primary_hover": "147 197 253",
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"accent": "248 113 113",
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"accent_hover": "252 165 165",
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"background": "0 0 0",
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"surface": "10 10 10",
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"border": "55 65 81",
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"text_primary": "255 255 255",
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"text_muted": "156 163 175",
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},
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},
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{
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"id": "ocean",
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"label": "Ocean Blue",
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"builtin": True,
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"light": {
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"primary": "29 78 216",
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"primary_hover": "30 58 138",
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"accent": "8 145 178",
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"accent_hover": "14 116 144",
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"background": "239 246 255",
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"surface": "219 234 254",
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"border": "147 197 253",
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"text_primary": "30 58 138",
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"text_muted": "59 130 246",
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},
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"dark": {
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"primary": "96 165 250",
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"primary_hover": "147 197 253",
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"accent": "34 211 238",
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"accent_hover": "103 232 249",
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"background": "10 22 40",
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"surface": "15 36 68",
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"border": "29 78 216",
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"text_primary": "219 234 254",
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"text_muted": "147 197 253",
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},
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},
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]
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async def seed_builtin_themes() -> None:
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"""Write built-in theme files to storage-service if they are not already there."""
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existing_keys = await config_storage.list_keys(prefix="themes/")
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existing_ids = {k.removeprefix("themes/").removesuffix(".json") for k in existing_keys}
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for theme in _BUILTIN_THEMES:
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if theme["id"] not in existing_ids:
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await config_storage.write_json(f"themes/{theme['id']}.json", theme)
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logger.info("Built-in themes seeded (%d themes)", len(_BUILTIN_THEMES))
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async def load_all_themes() -> list[dict]:
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"""Return all themes from storage-service, built-ins first then custom by label."""
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keys = await config_storage.list_keys(prefix="themes/")
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themes: list[dict] = []
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for key in keys:
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data = await config_storage.read_json(key)
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if data:
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themes.append(data)
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builtin_ids = [t["id"] for t in _BUILTIN_THEMES]
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def sort_key(t: dict) -> tuple:
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tid = t.get("id", "")
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try:
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return (0, builtin_ids.index(tid))
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except ValueError:
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return (1, t.get("label", tid).lower())
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return sorted(themes, key=sort_key)
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async def load_theme_by_id(theme_id: str) -> dict | None:
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"""Return a single theme dict, or None if not found."""
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return await config_storage.read_json(f"themes/{theme_id}.json")
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async def save_theme(theme: dict) -> None:
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"""Write a theme to storage-service."""
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await config_storage.write_json(f"themes/{theme['id']}.json", theme)
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async def delete_theme(theme_id: str) -> None:
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"""Delete a custom theme. Raises ValueError for built-ins, KeyError if not found."""
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data = await config_storage.read_json(f"themes/{theme_id}.json")
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if data is None:
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raise FileNotFoundError(theme_id)
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if data.get("builtin"):
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raise ValueError("Cannot delete a built-in theme")
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await config_storage.delete_key(f"themes/{theme_id}.json")
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def validate_theme_tokens(colors: dict) -> list[str]:
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"""Return a list of validation error messages, empty if valid."""
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errors = []
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missing = _REQUIRED_TOKENS - set(colors.keys())
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if missing:
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errors.append(f"Missing tokens: {', '.join(sorted(missing))}")
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for key, val in colors.items():
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if key in _REQUIRED_TOKENS and not _RGB_RE.match(str(val)):
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errors.append(f"Token '{key}' must be an RGB triplet like '37 99 235', got: {val!r}")
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return errors
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