import base64
import logging

import httpx
from fastapi import HTTPException

from services.ai_client import get_openai_client
from services.media_resolver import resolve_media
from services.runtime_settings import get_ai_settings

log = logging.getLogger(__name__)


async def _get_ai_client():
    return await get_openai_client()


async def parse_media(kind: str, key: str) -> dict:
    """
    Parse one chatlog media object into text.

    kind: voice, image, or video.
    key: chatlog media key.
    """
    if kind not in {"voice", "image", "video"}:
        raise HTTPException(400, "不支持的媒体类型")
    if not key:
        raise HTTPException(400, "媒体 key 不能为空")

    ai = await get_ai_settings()
    if not ai.get("ai_api_key"):
        raise HTTPException(503, "AI 服务未配置，请在设置页填写 AI API Key")
    if kind == "voice" and not ai.get("voice_model"):
        raise HTTPException(503, "语音模型未配置，请在设置页填写语音模型名称，例如 paraformer-v2")
    if kind in ("image", "video") and not ai.get("vision_model"):
        raise HTTPException(503, "视觉模型未配置，请在设置页填写视觉模型名称，例如 qwen-vl-plus")

    media = await resolve_media(kind, key)
    if kind == "voice":
        return {"text": await _parse_voice(media.bytes, media.content_type)}
    return {"text": await _parse_visual(kind, media.bytes, media.content_type)}


async def _parse_voice(media_bytes: bytes, content_type: str) -> str:
    b64_audio = base64.b64encode(media_bytes).decode()
    audio_ct = content_type.lower()
    if "silk" in audio_ct or "x-silk" in audio_ct:
        audio_mime = "audio/silk"
    elif "amr" in audio_ct:
        audio_mime = "audio/amr"
    elif "ogg" in audio_ct or "opus" in audio_ct:
        audio_mime = "audio/ogg"
    elif "wav" in audio_ct:
        audio_mime = "audio/wav"
    else:
        audio_mime = "audio/mpeg"

    data_uri = f"data:{audio_mime};base64,{b64_audio}"
    _, ai = await _get_ai_client()
    asr_headers = {
        "Authorization": f"Bearer {ai['ai_api_key']}",
        "Content-Type": "application/json",
    }

    async with httpx.AsyncClient(timeout=60) as http:
        submit = await http.post(
            "https://dashscope.aliyuncs.com/api/v1/services/audio/asr/transcription",
            headers={**asr_headers, "X-DashScope-Async": "enable"},
            json={
                "model": ai["voice_model"],
                "input": {"file_urls": [data_uri]},
                "parameters": {"language_hints": ["zh", "en"]},
            },
            timeout=30,
        )
        submit_data = submit.json()
        if submit.status_code not in (200, 201):
            raise HTTPException(500, f"提交识别任务失败: {submit_data.get('message', submit_data)}")

        task_id = submit_data.get("output", {}).get("task_id")
        if not task_id:
            raise HTTPException(500, f"未获取到 task_id: {submit_data}")

        for _ in range(30):
            import asyncio

            await asyncio.sleep(1)
            poll = await http.get(
                f"https://dashscope.aliyuncs.com/api/v1/tasks/{task_id}",
                headers=asr_headers,
                timeout=10,
            )
            poll_data = poll.json()
            status = poll_data.get("output", {}).get("task_status", "")
            if status == "SUCCEEDED":
                results = poll_data.get("output", {}).get("results", [])
                log.info("[media_parser] ASR SUCCEEDED results: %s", results)
                if not results:
                    return "（识别结果为空）"
                trans_url = results[0].get("transcription_url", "")
                if trans_url:
                    trans_resp = await http.get(trans_url, timeout=10)
                    trans_data = trans_resp.json()
                    log.info("[media_parser] transcription_url content: %s", str(trans_data)[:500])
                    transcripts = trans_data.get("transcripts", [])
                    text = transcripts[0].get("text", "") if transcripts else ""
                else:
                    text = results[0].get("transcription", "")
                return text or "（识别结果为空）"
            if status in ("FAILED", "CANCELLED"):
                raise HTTPException(500, f"识别任务失败: {poll_data.get('output', {}).get('message', status)}")

    raise HTTPException(500, "语音识别超时（30秒）")


async def _parse_visual(kind: str, media_bytes: bytes, content_type: str) -> str:
    b64 = base64.b64encode(media_bytes).decode()
    ct = content_type.lower()
    if "png" in ct:
        mime = "image/png"
    elif "webp" in ct:
        mime = "image/webp"
    else:
        mime = "image/jpeg"
    data_url = f"data:{mime};base64,{b64}"
    prompt = "请用中文简洁描述这张图片的内容。" if kind == "image" else "请用中文简洁描述这个视频截图的内容。"

    client, ai = await _get_ai_client()
    resp_ai = await client.chat.completions.create(
        model=ai["vision_model"],
        messages=[
            {
                "role": "user",
                "content": [
                    {"type": "image_url", "image_url": {"url": data_url}},
                    {"type": "text", "text": prompt},
                ],
            }
        ],
        max_tokens=300,
    )
    return resp_ai.choices[0].message.content or ""