"""Environment-driven settings.

A *backend* is one place to send LLM calls: a wire protocol + endpoint + model +
key. The default backend comes from the LLM_* env vars; tasks can opt into a
named backend (see `backend()`), so some demos can run on Anthropic-direct while
others run on an Oumi deployment.

Each backend has a provider (wire protocol):
- "openai": OpenAI Chat Completions. base_url ends in /v1/ (the SDK appends
  chat/completions). Works for Anthropic's OpenAI-compat endpoint and Oumi
  deployments served as OpenAI-compatible (e.g. tuned open-weight models).
- "anthropic": Anthropic Messages. base_url ends in /inference (the SDK appends
  /v1/messages). Required for Oumi deployments that proxy an Anthropic model --
  Oumi's /chat/completions rejects the "anthropic" provider.
"""

import os
from dataclasses import dataclass
from pathlib import Path

APP_VERSION = "0.1.0"

REPO_ROOT = Path(__file__).resolve().parent.parent
DATA_DIR = REPO_ROOT / "data"
STATIC_DIR = REPO_ROOT / "app" / "static"


@dataclass(frozen=True)
class Backend:
    provider: str  # "openai" | "anthropic"
    base_url: str
    model: str
    api_key: str
    name: str = "default"  # profile that actually served the call (traces tag this)


# Default backend (the whole app's backend unless a task names another).
DEFAULT_BACKEND = Backend(
    provider=os.environ.get("LLM_PROVIDER", "openai"),
    base_url=os.environ.get("LLM_BASE_URL", "https://api.anthropic.com/v1/"),
    model=os.environ.get("LLM_MODEL", "claude-haiku-4-5"),
    api_key=os.environ.get("LLM_API_KEY") or os.environ.get("ANTHROPIC_API_KEY", ""),
)


def backend(name: str | None = None) -> Backend:
    """Resolve a task's backend by name.

    `None`/empty -> the default backend. A named backend "foo" is read from
    LLM_FOO_PROVIDER / LLM_FOO_BASE_URL / LLM_FOO_MODEL / LLM_FOO_API_KEY
    (PROVIDER defaults to "openai"). If a named backend isn't fully configured
    (base_url / model / api_key all set), we fall back to the default — so a
    task pinned to "oumi" still runs in a dev env that hasn't set LLM_OUMI_*.
    The returned backend carries the name that actually served the call, so a
    silent fallback (e.g. a mistyped Space secret) is visible in the traces.
    """
    if not name:
        return DEFAULT_BACKEND
    p = f"LLM_{name.upper()}_"
    base_url = os.environ.get(p + "BASE_URL")
    model = os.environ.get(p + "MODEL")
    api_key = os.environ.get(p + "API_KEY")
    if not (base_url and model and api_key):
        return DEFAULT_BACKEND
    return Backend(
        provider=os.environ.get(p + "PROVIDER", "openai"),
        base_url=base_url,
        model=model,
        api_key=api_key,
        name=name.lower(),
    )


# Back-compat alias for the default model id (used by /api/health).
LLM_MODEL = DEFAULT_BACKEND.model

# Shared passcode gating /api/* (except /api/health). Empty string disables the gate.
APP_PASSCODE = os.environ.get("APP_PASSCODE", "")

MAX_TOOL_ITERATIONS = 6