src/fetch_uvb_data.py

"""
Fetch data from HuggingFace dataset undertheseanlp/UVB-v0.1
- Get 8,000 high-quality sentences from fiction books
- Get 8,000 high-quality sentences from non-fiction books
"""

import re
from os.path import dirname, join

from datasets import load_dataset
from underthesea import sent_tokenize, text_normalize


# Fiction-related genres
FICTION_GENRES = {
    "Fiction", "Novels", "Romance", "Fantasy", "Science Fiction",
    "Mystery", "Thriller", "Horror", "Historical Fiction", "Literary Fiction",
    "Adventure", "Crime", "Suspense", "Drama", "Short Stories"
}

# Non-fiction related genres
NON_FICTION_GENRES = {
    "Non Fiction", "Nonfiction", "History", "Biography", "Autobiography",
    "Self Help", "Psychology", "Philosophy", "Science", "Politics",
    "Economics", "Business", "Education", "Travel", "Memoir",
    "Essays", "Reference", "Health", "Religion", "Spirituality"
}


def clean_text(text):
    """Remove formatting and clean text."""
    # Normalize Unicode using underthesea
    text = text_normalize(text)
    # Remove markdown headers
    text = re.sub(r'^#+\s+', '', text, flags=re.MULTILINE)
    # Remove bold/italic markers
    text = re.sub(r'\*+', '', text)
    # Remove horizontal rules
    text = re.sub(r'^-+$', '', text, flags=re.MULTILINE)
    # Remove links
    text = re.sub(r'\[([^\]]+)\]\([^)]+\)', r'\1', text)
    # Remove multiple newlines
    text = re.sub(r'\n{2,}', '\n', text)
    # Remove leading/trailing whitespace per line
    lines = [line.strip() for line in text.split('\n')]
    text = '\n'.join(lines)
    return text


def is_high_quality_sentence(sent):
    """Check if sentence is high quality for UD annotation."""
    sent = sent.strip()

    if not sent:
        return False, sent

    # Length constraints
    if len(sent) < 30:  # Minimum length for meaningful sentence
        return False, sent
    if len(sent) > 250:  # Maximum length
        return False, sent

    # Word count constraints
    words = sent.split()
    if len(words) < 5:  # At least 5 words
        return False, sent
    if len(words) > 40:  # Max 40 words
        return False, sent

    # Must start with uppercase letter (proper sentence)
    if not sent[0].isupper():
        return False, sent

    # Must end with proper punctuation
    if not sent.rstrip()[-1] in '.!?…"»':
        return False, sent

    # Skip if mostly uppercase (headers, titles)
    if sum(1 for c in sent if c.isupper()) > len(sent) * 0.3:
        return False, sent

    # Must contain Vietnamese characters
    if not re.search(r'[àáảãạăắằẳẵặâấầẩẫậèéẻẽẹêếềểễệìíỉĩịòóỏõọôốồổỗộơớờởỡợùúủũụưứừửữựỳýỷỹỵđ]', sent, re.IGNORECASE):
        return False, sent

    # Skip sentences with too many numbers (tables, lists)
    num_digits = sum(1 for c in sent if c.isdigit())
    if num_digits > len(sent) * 0.15:
        return False, sent

    # Skip sentences with special patterns
    if re.match(r'^(Chương|Phần|Mục|Điều|\d+\.|\([a-z]\))', sent):
        return False, sent

    # Skip sentences with URLs or emails
    if re.search(r'(http|www\.|@|\.com|\.vn)', sent, re.IGNORECASE):
        return False, sent

    # Skip sentences with excessive punctuation
    punct_count = sum(1 for c in sent if c in '.,;:!?-–—()[]{}""\'\'«»')
    if punct_count > len(words) * 1.5:
        return False, sent

    # Skip incomplete sentences (ending with ellipsis in middle)
    if '...' in sent[:-5]:
        return False, sent

    # Skip dialogue-heavy sentences (too many quotes)
    quote_count = sent.count('"') + sent.count('"') + sent.count('"')
    if quote_count > 4:
        return False, sent

    return True, sent


def classify_book(genres):
    """Classify book as fiction or non-fiction based on genres."""
    if not genres:
        return None

    genres_set = set(genres)

    is_fiction = bool(genres_set & FICTION_GENRES)
    is_non_fiction = bool(genres_set & NON_FICTION_GENRES)

    if is_fiction and not is_non_fiction:
        return "fiction"
    elif is_non_fiction and not is_fiction:
        return "non-fiction"
    elif is_fiction and is_non_fiction:
        # Prefer the dominant one
        fiction_count = len(genres_set & FICTION_GENRES)
        non_fiction_count = len(genres_set & NON_FICTION_GENRES)
        return "fiction" if fiction_count > non_fiction_count else "non-fiction"

    return None


def extract_sentences_from_book(content, max_sentences=500):
    """Extract high-quality sentences from book content."""
    content = clean_text(content)
    sentences = sent_tokenize(content)

    valid_sentences = []
    for sent in sentences:
        is_valid, cleaned_sent = is_high_quality_sentence(sent)
        if is_valid:
            valid_sentences.append(cleaned_sent)
            if len(valid_sentences) >= max_sentences:
                break

    return valid_sentences


def fetch_and_process():
    print("Loading UVB-v0.1 dataset from HuggingFace...")
    ds = load_dataset("undertheseanlp/UVB-v0.1", split="train")

    print(f"Total books in dataset: {len(ds)}")

    # Classify books
    fiction_books = []
    non_fiction_books = []

    for book in ds:
        genres = book.get("genres", [])
        rating = book.get("goodreads_rating", 0) or 0
        num_ratings = book.get("goodreads_num_ratings", 0) or 0

        # Quality filter: prefer books with good ratings
        quality_score = rating * min(num_ratings / 100, 10)  # Weight by rating count

        book_type = classify_book(genres)
        book_info = {
            "title": book["title"],
            "content": book["content"],
            "rating": rating,
            "num_ratings": num_ratings,
            "quality_score": quality_score,
            "genres": genres
        }

        if book_type == "fiction":
            fiction_books.append(book_info)
        elif book_type == "non-fiction":
            non_fiction_books.append(book_info)

    print(f"Fiction books: {len(fiction_books)}")
    print(f"Non-fiction books: {len(non_fiction_books)}")

    # Sort by quality score (higher is better)
    fiction_books.sort(key=lambda x: x["quality_score"], reverse=True)
    non_fiction_books.sort(key=lambda x: x["quality_score"], reverse=True)

    # Extract sentences from fiction books
    print("\nExtracting sentences from fiction books...")
    fiction_sentences = []
    for i, book in enumerate(fiction_books):
        if len(fiction_sentences) >= 8000:
            break
        sentences = extract_sentences_from_book(book["content"])
        for sent in sentences:
            if len(fiction_sentences) >= 8000:
                break
            fiction_sentences.append(sent)
        print(f"  [{i+1}/{len(fiction_books)}] {book['title'][:50]} - {len(sentences)} sentences (total: {len(fiction_sentences)})")

    # Extract sentences from non-fiction books
    print("\nExtracting sentences from non-fiction books...")
    non_fiction_sentences = []
    for i, book in enumerate(non_fiction_books):
        if len(non_fiction_sentences) >= 8000:
            break
        sentences = extract_sentences_from_book(book["content"])
        for sent in sentences:
            if len(non_fiction_sentences) >= 8000:
                break
            non_fiction_sentences.append(sent)
        print(f"  [{i+1}/{len(non_fiction_books)}] {book['title'][:50]} - {len(sentences)} sentences (total: {len(non_fiction_sentences)})")

    print(f"\nFiction sentences collected: {len(fiction_sentences)}")
    print(f"Non-fiction sentences collected: {len(non_fiction_sentences)}")

    # Combine all sentences
    all_sentences = fiction_sentences[:8000] + non_fiction_sentences[:8000]
    print(f"Total sentences: {len(all_sentences)}")

    # Save to output file
    output_dir = dirname(dirname(__file__))
    output_file = join(output_dir, "sentences_uvb.txt")

    with open(output_file, "w", encoding="utf-8") as f:
        for i, sent in enumerate(all_sentences, 1):
            source = "fiction" if i <= len(fiction_sentences[:8000]) else "non-fiction"
            f.write(f"{i}\t{source}\t{sent}\n")

    print(f"\nSaved to: {output_file}")

    # Print samples
    print("\nSample fiction sentences:")
    for i, sent in enumerate(fiction_sentences[:3], 1):
        print(f"  {i}. {sent[:100]}...")

    print("\nSample non-fiction sentences:")
    for i, sent in enumerate(non_fiction_sentences[:3], 1):
        print(f"  {i}. {sent[:100]}...")


if __name__ == "__main__":
    fetch_and_process()