"""Thin, minimal-surface model client for ai-draft.

Per NFR design (minimal-surface): this is the ONE narrow caller to the granted
model. It builds a minimal prompt from the DraftBrief (content context + tone +
per-type max-length note), calls the cloud model, and parses the response into a
DraftArticle. It is injectable so tests can substitute a stub (no real network
in CI).
"""

from __future__ import annotations

from typing import Protocol

from .model import DraftArticle


class ModelClient(Protocol):
    """Interface for the single sanctioned external call."""

    def generate(self, brief_text: str, article_type: str, article_id: str) -> DraftArticle: ...


class HttpModelClient:
    """Default client calling the granted cloud model endpoint.

    Endpoint/alias is supplied by the user via a local config (infrastructure
    design Q3=A, never committed / hard-coded). Implementation detail for
    code-generation: a thin HTTP POST of `brief_text` to the configured
    endpoint, response parsed locally into a DraftArticle. The concrete HTTP
    transport is wired in code-generation; this class documents the contract.
    """

    def __init__(self, model: str = "deepseek-v4-flash:cloud", endpoint: str = "") -> None:
        self.model = model
        self.endpoint = endpoint

    def generate(self, brief_text: str, article_type: str, article_id: str) -> DraftArticle:
        # Placeholder transport — the real HTTP call is implemented in
        # code-generation. Kept as an interface-level stub per the design-stage
        # constraint (≤15 lines, no complete HTTP implementation here).
        raise NotImplementedError(
            "HttpModelClient transport is wired in code-generation; tests use the stub."
        )


def build_model_prompt(brief, article_type: str, max_length: int) -> str:
    """Assemble the minimal prompt (Q4=A, BR2.2/BR3.1/BR3.2): content context +
    tone + per-type max-length note. No secrets, only the brief."""
    prompt = (
        f"Write a {article_type} article.\n"
        f"Context: {brief.content_context}\n"
        f"Tone: {brief.tone_instruction}\n"
        f"Max length: {max_length} characters."
    )
    return prompt.strip()