import io

import pdfplumber
import docx

from app.clients.llm.base import LLMClient
from app.clients.storage.base import StorageClient
from app.core.config import get_config
from app.core.exceptions import UnsupportedFileTypeError
from app.core.json_utils import extract_json
from app.core.logging import get_logger
from app.models.text_models import (
    SourceOffset,
    TextExtractRequest,
    TextExtractResponse,
    TripleItem,
)

logger = get_logger(__name__)

_SUPPORTED_EXTENSIONS = {".txt", ".pdf", ".docx"}

_DEFAULT_PROMPT = (
    "请从以下文本中提取知识三元组，以 JSON 数组格式返回，每条包含字段："
    "subject（主语）、predicate（谓语）、object（宾语）、"
    "source_snippet（原文证据片段）、source_offset（{{start, end}} 字符偏移）。\n\n"
    "文本内容：\n{text}"
)


def _file_extension(file_name: str) -> str:
    idx = file_name.rfind(".")
    return file_name[idx:].lower() if idx != -1 else ""


def _parse_txt(data: bytes) -> str:
    return data.decode("utf-8", errors="replace")


def _parse_pdf(data: bytes) -> str:
    with pdfplumber.open(io.BytesIO(data)) as pdf:
        pages = [page.extract_text() or "" for page in pdf.pages]
    return "\n".join(pages)


def _parse_docx(data: bytes) -> str:
    doc = docx.Document(io.BytesIO(data))
    return "\n".join(p.text for p in doc.paragraphs)


async def extract_triples(
    req: TextExtractRequest,
    llm: LLMClient,
    storage: StorageClient,
) -> TextExtractResponse:
    ext = _file_extension(req.file_name)
    if ext not in _SUPPORTED_EXTENSIONS:
        raise UnsupportedFileTypeError(f"不支持的文件格式: {ext}")

    cfg = get_config()
    bucket = cfg["storage"]["buckets"]["source_data"]
    model = req.model or cfg["models"]["default_text"]

    data = await storage.download_bytes(bucket, req.file_path)

    if ext == ".txt":
        text = _parse_txt(data)
    elif ext == ".pdf":
        text = _parse_pdf(data)
    else:
        text = _parse_docx(data)

    prompt_template = req.prompt_template or _DEFAULT_PROMPT
    prompt = prompt_template.format(text=text) if "{text}" in prompt_template else prompt_template + "\n\n" + text

    messages = [{"role": "user", "content": prompt}]
    raw = await llm.chat(model, messages)

    logger.info("text_extract", extra={"file": req.file_name, "model": model})

    items_raw = extract_json(raw)
    items = [
        TripleItem(
            subject=item["subject"],
            predicate=item["predicate"],
            object=item["object"],
            source_snippet=item["source_snippet"],
            source_offset=SourceOffset(
                start=item["source_offset"]["start"],
                end=item["source_offset"]["end"],
            ),
        )
        for item in items_raw
    ]
    return TextExtractResponse(items=items)