preprocess_data.py

#!/usr/bin/env python3
"""
parse_data.py — scrape and preprocess the Old Church Slavonic corpus pages
from dic.feb-web.ru/slavonic/corpus/ into a clean CSV.

This script is a "cleaned up" version of the exploratory notebook `parse_data.ipynb`:
- no duplicated function definitions
- one coherent CLI
- optional de-duplication of produced text segments

Typical usage
-------------
1) Scrape pages into folders:
    python parse_data.py scrape --out scraped_sections

2) Build a CSV dataset:
    python parse_data.py build --in-dir scraped_sections --out-csv ocs.csv --unit line --dedupe text_source

Dependencies
------------
pip install requests beautifulsoup4 pandas
"""

import argparse
import csv
import logging
import re
from dataclasses import dataclass
from pathlib import Path
from typing import  Iterator, Optional, Tuple
from urllib.parse import urlparse, urljoin

import requests
from bs4 import BeautifulSoup
import pandas as pd



LOG = logging.getLogger("parse_data")


def setup_logging(verbosity: int) -> None:
    level = logging.WARNING
    if verbosity == 1:
        level = logging.INFO
    elif verbosity >= 2:
        level = logging.DEBUG
    logging.basicConfig(
        level=level,
        format="%(asctime)s | %(levelname)s | %(message)s",
        datefmt="%H:%M:%S",
    )



DEFAULT_BASE_URL = "http://dic.feb-web.ru/slavonic/corpus/"
DEFAULT_MAX_NAME_LEN = 100


def truncate_name(name: str, max_length: int = DEFAULT_MAX_NAME_LEN) -> str:
    name = name.strip()
    if len(name) <= max_length:
        return name
    return name[:max_length].rstrip() + "…"


def safe_filename(name: str) -> str:
    name = name.replace("/", "-").replace("\\", "-")
    name = re.sub(r"[\x00-\x1f]+", " ", name).strip()
    return name


def get_folder_name_from_url(href: str, base_url: str) -> str:
    if href.startswith("/"):
        full_url = base_url.rstrip("/") + href
    else:
        full_url = urljoin(base_url, href)

    parsed = urlparse(full_url)
    parts = [p for p in parsed.path.strip("/").split("/") if p]

    if "corpus" in parts:
        idx = parts.index("corpus")
        after = parts[idx + 1 :]
    else:
        after = parts

    if len(after) >= 2:
        folder = after[1]
    elif len(after) == 1:
        folder = after[0]
    else:
        folder = "unknown"

    return truncate_name(folder)


def make_session(timeout_s: int = 20) -> requests.Session:
    sess = requests.Session()
    try:
        from requests.adapters import HTTPAdapter
        from urllib3.util.retry import Retry

        retry = Retry(
            total=5,
            backoff_factor=0.5,
            status_forcelist=(429, 500, 502, 503, 504),
            allowed_methods=("GET",),
            raise_on_status=False,
        )
        adapter = HTTPAdapter(max_retries=retry)
        sess.mount("http://", adapter)
        sess.mount("https://", adapter)
    except Exception:
        pass

    sess.headers.update({"User-Agent": "parse_data/1.0 (+https://openai.com)"})
    sess.request_timeout = timeout_s
    return sess


def fetch_html(session: requests.Session, url: str) -> BeautifulSoup:
    timeout = getattr(session, "request_timeout", 20)
    resp = session.get(url, timeout=timeout)
    resp.raise_for_status()
    return BeautifulSoup(resp.content, "html.parser")


def extract_text_blocks(session: requests.Session, url: str) -> str:
    try:
        soup = fetch_html(session, url)
        blocks = soup.find_all("p")
        text = "\n".join(b.get_text(strip=True) for b in blocks)
        return text
    except Exception as e:
        LOG.warning("Failed to extract text from %s: %s", url, e)
        return ""


def discover_tree_links(session: requests.Session, base_url: str) -> list[Tuple[str, str]]:
    soup = fetch_html(session, base_url)
    frame = soup.find("frame", {"name": "tree"})
    if not frame or not frame.get("src"):
        raise RuntimeError("Could not find <frame name='tree'> on the base page")

    frame_src = str(frame["src"])
    frame_url = urljoin(base_url, frame_src)

    tree = fetch_html(session, frame_url)
    links = []
    for a in tree.find_all("a", href=True):
        href = str(a["href"])
        title = a.get_text(strip=True) or href
        links.append((href, truncate_name(title)))
    return links


def scrape(
    base_url: str,
    out_dir: Path,
    max_name_length: int = DEFAULT_MAX_NAME_LEN,
    skip_existing: bool = True,
    timeout_s: int = 20,
) -> None:
    global DEFAULT_MAX_NAME_LEN
    DEFAULT_MAX_NAME_LEN = max_name_length

    out_dir.mkdir(parents=True, exist_ok=True)
    session = make_session(timeout_s=timeout_s)

    links = discover_tree_links(session, base_url)
    LOG.info("Found %d links", len(links))

    for href, section_name in links:
        full_url = urljoin(base_url, href)

        folder_name = get_folder_name_from_url(href, base_url)
        folder_path = out_dir / folder_name
        folder_path.mkdir(parents=True, exist_ok=True)

        file_name = safe_filename(truncate_name(f"{section_name}.txt")).strip()
        if not file_name.endswith(".txt"):
            file_name += ".txt"
        file_path = folder_path / file_name

        if skip_existing and file_path.exists():
            LOG.debug("Skip existing: %s", file_path)
            continue

        LOG.debug("Processing: %s -> %s (folder=%s)", section_name, full_url, folder_name)
        page_text = extract_text_blocks(session, full_url)

        if page_text.strip():
            file_path.write_text(page_text, encoding="utf-8")
        else:
            LOG.info("Empty extraction for: %s (%s)", section_name, full_url)

    LOG.warning("Scraping completed. Output: %s", out_dir)


_RE_UPPER_CYR = re.compile(r"\b[А-ЯЁҐІЇЄѢЪѲѴ]+(?:\s+[А-ЯЁҐІЇЄѢЪѲѴ]+)*\b")
_RE_BRACKETS = re.compile(r"\[.*?\]|\(.*?\)")
_RE_ZACH = re.compile(r"зач̑.*?$", flags=re.MULTILINE)
_RE_VECHARA = re.compile(r"Въ\s[а-яА-Я҃]+?\sве́чера.*?:")
_RE_STIH = re.compile(r"Сті́хъ:.*?$", flags=re.MULTILINE)

_RE_HEADERS = re.compile(r"Ча́сть\s*[а-яА-Я҃\d]+|Глава̀\s*[а-яА-Я҃\s\d]+|Кѡндакъ\s*(\d+|[а-я]+)\.?$")


def normalize_markers(text: str) -> str:
    return re.sub(r"рл҃г\.", "рл҃г. ", text)


def remove_unwanted_sections(text: str) -> str:
    text = _RE_HEADERS.sub("", text)
    text = _RE_BRACKETS.sub(" ", text)
    text = _RE_ZACH.sub("", text)
    text = _RE_VECHARA.sub("", text)
    text = _RE_STIH.sub("", text)
    return text


def remove_capitalized_words(text: str) -> str:
    text = _RE_UPPER_CYR.sub("", text)
    return re.sub(r"\s+", " ", text).strip()


def clean_whitespace(text: str) -> str:
    return re.sub(r"\s+", " ", text).strip()


def iter_units(text: str, unit: str) -> Iterator[str]:
    unit = unit.lower()
    if unit == "file":
        yield text
        return

    if unit == "line":
        for ln in text.splitlines():
            ln = ln.strip()
            if ln:
                yield ln
        return

    if unit == "sentence":
        parts = re.split(r"(?<=[\.\!\?\:\;·…])\s+", text)
        for p in parts:
            p = p.strip()
            if p:
                yield p
        return

    raise ValueError(f"Unknown unit: {unit!r}. Use one of: file, line, sentence.")


@dataclass(frozen=True)
class BuildConfig:
    in_dir: Path
    out_csv: Path
    unit: str = "line"
    min_chars: int = 20
    dedupe: str = "text_source" 
    encoding: str = "utf-8"


def build_dataset(cfg: BuildConfig) -> None:
    cfg.out_csv.parent.mkdir(parents=True, exist_ok=True)

    seen: Optional[set[Tuple[str, str]]] = set() if cfg.dedupe != "none" else None

    written = 0
    skipped_short = 0
    skipped_dupe = 0

    with cfg.out_csv.open("w", encoding=cfg.encoding, newline="") as f_out:
        w = csv.writer(f_out)
        w.writerow(["Text", "Source"])

        for source_dir in sorted(p for p in cfg.in_dir.iterdir() if p.is_dir()):
            source = source_dir.name

            for txt in sorted(source_dir.glob("*.txt")):
                raw = txt.read_text(encoding=cfg.encoding, errors="replace")

                raw = normalize_markers(raw)
                raw = remove_unwanted_sections(raw)
                raw = remove_capitalized_words(raw)

                for unit_text in iter_units(raw, cfg.unit):
                    unit_text = clean_whitespace(unit_text)
                    if len(unit_text) < cfg.min_chars:
                        skipped_short += 1
                        continue

                    if seen is not None:
                        key = (unit_text, source) if cfg.dedupe == "text_source" else (unit_text, "")
                        if key in seen:
                            skipped_dupe += 1
                            continue
                        seen.add(key)

                    w.writerow([unit_text, source])
                    written += 1

    LOG.warning(
        "Build complete: wrote=%d | skipped_short=%d | skipped_dupe=%d | out=%s",
        written, skipped_short, skipped_dupe, cfg.out_csv
    )


def combine_folder_csvs(folder_csv_dir: Path, out_csv: Path) -> None:
    if pd is None:
        raise RuntimeError("pandas is required for combine_folder_csvs. Install: pip install pandas")

    frames = []
    for p in sorted(folder_csv_dir.glob("*.csv")):
        frames.append(pd.read_csv(p))
    if not frames:
        raise RuntimeError(f"No CSV files found in {folder_csv_dir}")
    df = pd.concat(frames, ignore_index=True)
    df.reset_index(drop=True, inplace=True)
    out_csv.parent.mkdir(parents=True, exist_ok=True)
    df.to_csv(out_csv, index=False)
    LOG.warning("Combined %d CSVs into %s (rows=%d)", len(frames), out_csv, len(df))


def build_parser() -> argparse.ArgumentParser:
    p = argparse.ArgumentParser(description="Scrape and preprocess OCS corpus pages into CSV.")
    p.add_argument("-v", "--verbose", action="count", default=0, help="Increase verbosity (-v, -vv).")

    sub = p.add_subparsers(dest="cmd", required=True)

    ps = sub.add_parser("scrape", help="Scrape the corpus site into a folder structure.")
    ps.add_argument("--base-url", default=DEFAULT_BASE_URL, help="Base URL to scrape.")
    ps.add_argument("--out", dest="out_dir", default="scraped_sections", help="Output directory.")
    ps.add_argument("--max-name-length", type=int, default=DEFAULT_MAX_NAME_LEN, help="Max filename length.")
    ps.add_argument("--no-skip-existing", action="store_true", help="Re-download even if file exists.")
    ps.add_argument("--timeout", type=int, default=20, help="Request timeout seconds.")

    pb = sub.add_parser("build", help="Build a single CSV dataset from scraped folders.")
    pb.add_argument("--in-dir", default="scraped_sections", help="Input directory created by 'scrape'.")
    pb.add_argument("--out-csv", default="old_church_slavonic_dataset.csv", help="Output CSV path.")
    pb.add_argument("--unit", choices=["file", "line", "sentence"], default="line",
                    help="Dataset unit granularity.")
    pb.add_argument("--min-chars", type=int, default=20, help="Drop units shorter than this.")
    pb.add_argument("--dedupe", choices=["none", "text_source", "text"], default="text_source",
                    help="De-duplication strategy.")

    pc = sub.add_parser("combine", help="Combine per-folder CSVs into one (pandas).")
    pc.add_argument("--in-dir", default="preprocessed_for_generation", help="Directory with *.csv files.")
    pc.add_argument("--out-csv", default="old_church_slavonic_dataset.csv", help="Output CSV path.")

    return p


def main(argv: Optional[list[str]] = None) -> int:
    args = build_parser().parse_args(argv)
    setup_logging(args.verbose)

    if args.cmd == "scrape":
        scrape(
            base_url=args.base_url,
            out_dir=Path(args.out_dir),
            max_name_length=args.max_name_length,
            skip_existing=not args.no_skip_existing,
            timeout_s=args.timeout,
        )
        return 0

    if args.cmd == "build":
        cfg = BuildConfig(
            in_dir=Path(args.in_dir),
            out_csv=Path(args.out_csv),
            unit=args.unit,
            min_chars=args.min_chars,
            dedupe=args.dedupe,
        )
        build_dataset(cfg)
        return 0

    if args.cmd == "combine":
        combine_folder_csvs(Path(args.in_dir), Path(args.out_csv))
        return 0

    raise AssertionError("unreachable")


if __name__ == "__main__":
    raise SystemExit(main())