"""Per-file summarisation for project folder integration."""
from __future__ import annotations

import base64
import io
from dataclasses import dataclass

from langchain_core.messages import HumanMessage, SystemMessage
from pypdf import PdfReader
from docx import Document as DocxDocument

from app.core.langfuse_client import (
    compile_prompt,
    extract_usage,
    get_langfuse,
    get_prompt_or_fallback,
)
from app.core.llm import get_llm

_TEXT_FALLBACK = (
    "You are summarising a file for an AI assistant that helps the user manage a project.\n"
    "Produce a single sentence (<=30 words, <=200 chars) that captures the file's purpose "
    "and most important detail.\nFile extension: {ext}\nFile name: {name}\nContent (truncated if long):\n{content}"
)
_IMAGE_FALLBACK = (
    "You are summarising an image attached to a project folder.\n"
    "Produce a single sentence (<=30 words, <=200 chars) describing what the image shows "
    "and any obvious purpose (logo, screenshot, diagram, photo of a whiteboard, etc.)."
)
_MAX_INPUT_CHARS = 6000


@dataclass
class IndexResult:
    summary: str
    tokens_used: int


async def _llm_text(messages: list) -> object:
    """Make the LLM call for text summarisation.

    Defined as a standalone async function so tests can patch it cleanly
    without needing to mock the LLM object itself.
    """
    llm = get_llm(model="gpt-4o-mini", temperature=0.2)
    return await llm.ainvoke(messages)


async def _llm_vision(messages: list) -> object:
    """Make the LLM call for vision (image) summarisation.

    Accepts the message list and returns the response directly, mirroring
    the ``_llm_text`` caller pattern so tests can patch it at the module level.
    """
    llm = get_llm(model="gpt-4o-mini", temperature=0.2)
    return await llm.ainvoke(messages)


async def summarize_image(*, image_b64: str, mime: str, file_name: str | None = None) -> IndexResult:
    """Return a compact summary of an image file using vision.

    Parameters
    ----------
    image_b64:
        Base64-encoded image bytes.
    mime:
        MIME type of the image, e.g. ``"image/png"``.
    file_name:
        Optional file name, attached to the Langfuse trace as input metadata.
    """
    template, prompt_obj = get_prompt_or_fallback("folder_file_summary_image", _IMAGE_FALLBACK)
    messages = [
        SystemMessage(content=template),
        HumanMessage(content=[
            {"type": "text", "text": "Summarise this image."},
            {"type": "image_url", "image_url": {"url": f"data:{mime};base64,{image_b64}"}},
        ]),
    ]
    lf = get_langfuse()
    if lf is not None:
        with lf.start_as_current_observation(
            as_type="generation",
            name="folder-summarize-image",
            model="gpt-4o-mini",
            prompt=prompt_obj,
            input={"file_name": file_name, "mime": mime},
        ) as gen:
            response = await _llm_vision(messages)
            usage = extract_usage(response)
            gen.update(output=response.content, usage_details=usage)
    else:
        response = await _llm_vision(messages)
        usage = extract_usage(response)
    summary = (response.content or "").strip()[:500]
    return IndexResult(summary=summary, tokens_used=usage.get("total", 0))


async def summarize_text(*, content: str, ext: str, name: str) -> IndexResult:
    """Return a compact summary of a text file.

    Parameters
    ----------
    content:
        Raw text content of the file (will be truncated to _MAX_INPUT_CHARS).
    ext:
        File extension including the leading dot, e.g. ``".md"``.
    name:
        File name, e.g. ``"kickoff.md"``.
    """
    template, prompt_obj = get_prompt_or_fallback("folder_file_summary_text", _TEXT_FALLBACK)
    truncated = content[:_MAX_INPUT_CHARS]
    compiled = compile_prompt(template, prompt_obj, ext=ext, name=name, content=truncated)
    messages = [
        SystemMessage(content=compiled),
        HumanMessage(content="Summarise this file."),
    ]
    lf = get_langfuse()
    if lf is not None:
        with lf.start_as_current_observation(
            as_type="generation",
            name="folder-summarize-text",
            model="gpt-4o-mini",
            prompt=prompt_obj,
            input={"file_name": name, "ext": ext, "content_chars": len(truncated)},
        ) as gen:
            response = await _llm_text(messages)
            usage = extract_usage(response)
            gen.update(output=response.content, usage_details=usage)
    else:
        response = await _llm_text(messages)
        usage = extract_usage(response)
    summary = (response.content or "").strip()[:500]
    return IndexResult(summary=summary, tokens_used=usage.get("total", 0))


def _extract_pdf_text(pdf_b64: str) -> str:
    buf = io.BytesIO(base64.b64decode(pdf_b64))
    reader = PdfReader(buf)
    parts: list[str] = []
    for page in reader.pages:
        try:
            parts.append(page.extract_text() or "")
        except Exception:
            continue
    return "\n".join(parts).strip()


def _extract_docx_text(docx_b64: str) -> str:
    buf = io.BytesIO(base64.b64decode(docx_b64))
    doc = DocxDocument(buf)
    return "\n".join(p.text for p in doc.paragraphs if p.text).strip()


async def summarize_pdf(*, pdf_b64: str, name: str) -> IndexResult:
    """Return a compact summary of a PDF file.

    Parameters
    ----------
    pdf_b64:
        Base64-encoded PDF bytes.
    name:
        File name, e.g. ``"report.pdf"``.
    """
    text = _extract_pdf_text(pdf_b64)
    if not text:
        return IndexResult(summary="Could not extract text", tokens_used=0)
    return await summarize_text(content=text, ext=".pdf", name=name)


async def summarize_docx(*, docx_b64: str, name: str) -> IndexResult:
    """Return a compact summary of a DOCX file.

    Parameters
    ----------
    docx_b64:
        Base64-encoded DOCX bytes.
    name:
        File name, e.g. ``"spec.docx"``.
    """
    text = _extract_docx_text(docx_b64)
    if not text:
        return IndexResult(summary="Could not extract text", tokens_used=0)
    return await summarize_text(content=text, ext=".docx", name=name)