backend/worker/worker.py

from __future__ import annotations

import os
import time
import uuid
from dataclasses import dataclass
from pathlib import Path
from typing import List, Tuple

from dotenv import load_dotenv

from .gmail_client import GmailClient
from .openai_classifier import classify_with_openai
from .pdf_render import render_pdf_to_pngs

# Force load repo_root/backend/.env (single source of truth)
REPO_ROOT = Path(__file__).resolve().parents[2]
load_dotenv(REPO_ROOT / "backend" / ".env", override=True)


@dataclass
class Settings:
    creds_path: Path
    token_path: Path

    label_incoming: str
    label_known: str
    label_unknown: str
    label_train: str

    # Rep email for UNKNOWN detection
    rep_notify_to: str
    notify_from: str

    # OpenAI
    openai_api_key: str
    openai_model: str

    poll_seconds: int
    max_messages_per_poll: int

    render_pages: int
    render_dpi: int

    trainer_base_url: str


def load_settings() -> Settings:
    base = Path(__file__).resolve().parents[1]  # backend/
    creds = Path(os.environ.get("GMAIL_CREDENTIALS_JSON", str(base / "credentials.json")))
    token = Path(os.environ.get("GMAIL_TOKEN_JSON", str(base / "token.json")))

    openai_api_key = (os.environ.get("OPENAI_API_KEY_TEST") or os.environ.get("OPENAI_API_KEY") or "").strip()
    openai_model = (os.environ.get("OPENAI_MODEL") or "gpt-4o-mini").strip()

    return Settings(
        creds_path=creds,
        token_path=token,

        label_incoming=os.environ.get("PDF_PIPELINE_LABEL_INCOMING", "PDF_PIPELINE/INCOMING"),
        label_known=os.environ.get("PDF_PIPELINE_LABEL_KNOWN", "PDF_PIPELINE/KNOWN"),
        label_unknown=os.environ.get("PDF_PIPELINE_LABEL_UNKNOWN", "PDF_PIPELINE/UNKNOWN"),
        label_train=os.environ.get("PDF_PIPELINE_LABEL_TRAIN", "PDF_PIPELINE/TRAIN"),

        notify_from=(os.environ.get("PDF_PIPELINE_NOTIFY_FROM") or "").strip(),
        rep_notify_to=(os.environ.get("PDF_PIPELINE_NOTIFY_TO") or "").strip(),

        openai_api_key=openai_api_key,
        openai_model=openai_model,

        poll_seconds=int(os.environ.get("PDF_PIPELINE_POLL_SECONDS", "20")),
        max_messages_per_poll=int(os.environ.get("PDF_PIPELINE_MAX_PER_POLL", "5")),

        render_pages=int(os.environ.get("PDF_PIPELINE_RENDER_PAGES", "2")),
        render_dpi=int(os.environ.get("PDF_PIPELINE_RENDER_DPI", "200")),

        trainer_base_url=(os.environ.get("PDF_TRAINER_BASE_URL") or "http://localhost:5173").strip(),
    )


def _safe_name(s: str) -> str:
    return "".join(c if c.isalnum() or c in ("-", "_", ".", " ") else "_" for c in s).strip()


def _write_pipeline_pdf(root_worker_dir: Path, filename: str, pdf_bytes: bytes) -> Tuple[str, Path]:
    """
    Persist PDF for the trainer to fetch later.
    Returns (pdf_id, pdf_path_on_disk).
    """
    uploads_dir = root_worker_dir / "uploads"
    uploads_dir.mkdir(parents=True, exist_ok=True)

    pdf_id = uuid.uuid4().hex
    pdf_path = uploads_dir / f"{pdf_id}.pdf"
    name_path = uploads_dir / f"{pdf_id}.name.txt"

    pdf_path.write_bytes(pdf_bytes)
    name_path.write_text(filename, encoding="utf-8")

    return pdf_id, pdf_path


def _process_train_label(gmail: GmailClient, s: Settings, root: Path) -> None:
    """
    TRAIN behavior:
      - Pull unread PDFs from TRAIN label
      - Store into uploads/ and print trainer link
      - Mark read
      - Do NOT classify
      - Do NOT move labels
    """
    msgs = gmail.search_unread_pdf_messages(s.label_train, max_results=s.max_messages_per_poll)
    if not msgs:
        return

    for m in msgs:
        msg_full = gmail.get_message_full(m.msg_id)
        pdf_atts = gmail.list_pdf_attachments(msg_full)

        if not pdf_atts:
            gmail.move_message(m.msg_id, add_labels=[], remove_labels=[], mark_read=True)
            continue

        for (filename, att_id) in pdf_atts:
            filename = _safe_name(filename or "attachment.pdf")
            pdf_bytes = gmail.download_attachment(m.msg_id, att_id)

            pdf_id, stored_pdf_path = _write_pipeline_pdf(root, filename, pdf_bytes)
            trainer_link = f"{s.trainer_base_url.rstrip('/')}/?pdf_id={pdf_id}"

            gmail.move_message(m.msg_id, add_labels=[], remove_labels=[], mark_read=True)

            print(
                f"[worker][TRAIN] stored PDF msg={m.msg_id} file={filename} "
                f"pdf_id={pdf_id} stored={stored_pdf_path}"
            )
            print(f"[worker][TRAIN] open: {trainer_link}")


def main():
    s = load_settings()

    # Validate settings
    if not s.rep_notify_to:
        raise RuntimeError("Missing PDF_PIPELINE_NOTIFY_TO (rep email for UNKNOWN detection)")
    if not s.notify_from:
        raise RuntimeError("Missing PDF_PIPELINE_NOTIFY_FROM (OAuth Gmail account email)")
    if not s.trainer_base_url:
        raise RuntimeError("Missing PDF_TRAINER_BASE_URL (base URL for trainer link)")
    if not s.openai_api_key:
        raise RuntimeError("Missing OPENAI_API_KEY_TEST (or OPENAI_API_KEY) in backend/.env")

    gmail = GmailClient(s.creds_path, s.token_path)

    # Ensure labels exist
    gmail.ensure_label(s.label_incoming)
    gmail.ensure_label(s.label_known)
    gmail.ensure_label(s.label_unknown)
    gmail.ensure_label(s.label_train)

    root = Path(__file__).resolve().parents[0]  # backend/worker
    tmp_dir = root / "tmp"
    tmp_dir.mkdir(parents=True, exist_ok=True)

    print(f"[worker] Watching label: {s.label_incoming}")
    print(f"[worker] Known label:   {s.label_known}")
    print(f"[worker] Unknown label: {s.label_unknown}")
    print(f"[worker] Train label:   {s.label_train}")
    print(f"[worker] Rep notify to: {s.rep_notify_to}")
    print(f"[worker] OpenAI model:  {s.openai_model}")

    while True:
        try:
            # 1) TRAIN lane
            _process_train_label(gmail, s, root)

            # 2) Main pipeline (INCOMING -> KNOWN/UNKNOWN)
            msgs = gmail.search_unread_pdf_messages(s.label_incoming, max_results=s.max_messages_per_poll)
            if not msgs:
                time.sleep(s.poll_seconds)
                continue

            for m in msgs:
                msg_full = gmail.get_message_full(m.msg_id)
                pdf_atts = gmail.list_pdf_attachments(msg_full)

                if not pdf_atts:
                    # Remove INCOMING + mark read so it doesn't loop forever
                    gmail.move_message(m.msg_id, add_labels=[], remove_labels=[s.label_incoming], mark_read=True)
                    continue

                any_unknown = False
                unknown_payloads: List[Tuple[str, bytes]] = []

                # Classify all PDF attachments for this message
                for (filename, att_id) in pdf_atts:
                    filename = _safe_name(filename or "attachment.pdf")
                    pdf_bytes = gmail.download_attachment(m.msg_id, att_id)

                    stamp = str(int(time.time()))
                    pdf_path = tmp_dir / f"{stamp}_{m.msg_id}_{filename}"
                    pdf_path.write_bytes(pdf_bytes)

                    img_dir = tmp_dir / f"{stamp}_{m.msg_id}_{pdf_path.stem}"
                    rendered = render_pdf_to_pngs(pdf_path, img_dir, pages=s.render_pages, dpi=s.render_dpi)
                    image_paths = [str(r.path) for r in rendered]

                    result = classify_with_openai(
                        image_paths,
                        api_key=s.openai_api_key,
                        model=s.openai_model,
                    )

                    template_id = (result.get("template_id") or "UNKNOWN").strip()
                    conf = float(result.get("confidence") or 0.0)

                    if template_id == "UNKNOWN":
                        any_unknown = True
                        unknown_payloads.append((filename, pdf_bytes))
                        print(f"[worker] UNKNOWN attachment conf={conf:.3f} msg={m.msg_id} file={filename}")
                    else:
                        print(
                            f"[worker] KNOWN attachment template={template_id} conf={conf:.3f} "
                            f"msg={m.msg_id} file={filename}"
                        )

                # Apply Gmail label ONCE per message
                if any_unknown:
                    gmail.move_message(
                        m.msg_id,
                        add_labels=[s.label_unknown],
                        remove_labels=[s.label_incoming],
                        mark_read=True,
                    )
                else:
                    gmail.move_message(
                        m.msg_id,
                        add_labels=[s.label_known],
                        remove_labels=[s.label_incoming],
                        mark_read=True,
                    )

                # Notify rep for each unknown PDF attachment
                if any_unknown:
                    for (filename, pdf_bytes) in unknown_payloads:
                        pdf_id, stored_pdf_path = _write_pipeline_pdf(root, filename, pdf_bytes)
                        trainer_link = f"{s.trainer_base_url.rstrip('/')}/?pdf_id={pdf_id}"

                        subject = "Action required: Unknown PDF format (template not found)"
                        body = (
                            "Hi,\n\n"
                            "We received a PDF that does not match any existing templates in the system.\n\n"
                            "Please open the PDF Trainer using the link below and create or update the template configuration:\n"
                            f"{trainer_link}\n\n"
                            "The original PDF is attached for reference.\n\n"
                            "Thank you,\n"
                            "Inserio Automation\n"
                        )

                        attachments: List[Tuple[str, bytes]] = []
                        if len(pdf_bytes) < 20 * 1024 * 1024:
                            attachments.append((filename, pdf_bytes))
                        else:
                            body += "\nNote: The PDF was too large to attach.\n"

                        gmail.send_email(
                            to_email=s.rep_notify_to,
                            from_email=s.notify_from,
                            subject=subject,
                            body_text=body,
                            attachments=attachments,
                        )

                        print(
                            f"[worker] UNKNOWN: emailed rep {s.rep_notify_to} msg={m.msg_id} file={filename} "
                            f"pdf_id={pdf_id} stored={stored_pdf_path}"
                        )

        except Exception as e:
            print(f"[worker] ERROR: {e}")

        time.sleep(s.poll_seconds)


if __name__ == "__main__":
    main()