"""AI-backed sentence explanations for the mobile clients."""

from dataclasses import dataclass
import logging
from urllib.parse import urlsplit

import requests


logger = logging.getLogger(__name__)


class AITutorError(RuntimeError):
    """Raised when the configured AI service cannot answer a request."""


@dataclass(frozen=True)
class AITutor:
    endpoint: str
    api_key: str
    model: str
    timeout_seconds: float

    @property
    def available(self) -> bool:
        return bool(self.endpoint and self.model)

    @property
    def request_url(self) -> str:
        """Resolve either a full chat URL or an OpenAI-compatible base URL."""
        parsed = urlsplit(self.endpoint)
        path = parsed.path.rstrip("/")
        if parsed.query or path.endswith("/chat/completions"):
            return self.endpoint
        if path.endswith("/v1") or path.endswith("/v1/openai"):
            return self.endpoint + "/chat/completions"
        if not path:
            return self.endpoint + "/v1/chat/completions"
        return self.endpoint

    def explain(
        self,
        sentence_text: str,
        *,
        course_title: str = "",
        sentence_index: int | None = None,
        language: str = "",
    ) -> str:
        if not self.available:
            raise AITutorError("AI explanation is not configured.")

        context = course_title.strip() or "口语训练课程"
        position = f"第 {sentence_index + 1} 句" if sentence_index is not None else "当前句子"
        language_note = f"句子语言：{language.strip()}。" if language.strip() else ""
        system_prompt = (
            "你是一位耐心的外语口语老师。请用简体中文讲解用户提供的影视/课程句子。"
            "先给出自然的中文翻译，再解释关键词、常用搭配或语法要点，并简要说明这句话适合怎样跟读。"
            "回答使用简明清晰的分段短文，总长度不超过 300 字，不要编造视频里没有的信息。"
        )
        user_prompt = (
            f"课程：{context}\n位置：{position}\n{language_note}句子：{sentence_text}"
        )
        headers = {"Content-Type": "application/json"}
        if self.api_key:
            headers["Authorization"] = f"Bearer {self.api_key}"
        try:
            response = requests.post(
                self.request_url,
                headers=headers,
                json={
                    "model": self.model,
                    "messages": [
                        {"role": "system", "content": system_prompt},
                        {"role": "user", "content": user_prompt},
                    ],
                    "temperature": 0.3,
                    "stream": False,
                },
                timeout=self.timeout_seconds,
            )
        except requests.RequestException as exc:
            logger.exception(
                "AI explanation request failed: endpoint=%s model=%s",
                self.request_url,
                self.model,
            )
            raise AITutorError("AI service request failed.") from exc

        if response.status_code < 200 or response.status_code >= 300:
            logger.warning(
                "AI explanation returned HTTP %s: endpoint=%s model=%s body=%s",
                response.status_code,
                self.request_url,
                self.model,
                response.text[:1000],
            )
            raise AITutorError(f"AI service returned HTTP {response.status_code}.")
        try:
            body = response.json()
            explanation = body["choices"][0]["message"]["content"]
        except (KeyError, IndexError, TypeError, ValueError) as exc:
            logger.warning(
                "AI explanation returned an unexpected body: endpoint=%s body=%s",
                self.request_url,
                response.text[:1000],
            )
            raise AITutorError("AI service returned an unexpected response.") from exc
        if not isinstance(explanation, str) or not explanation.strip():
            logger.warning(
                "AI explanation returned empty content: endpoint=%s body=%s",
                self.request_url,
                response.text[:1000],
            )
            raise AITutorError("AI service returned an empty explanation.")
        return explanation.strip()