clean_dataset.py

"""
clean_dataset.py — Extract clean prompts from Telegram AI image dataset.
Groups consecutive images by prompt, strips ads/model names/hashtags,
and outputs a VLM-training-ready JSONL file.

Usage:
  python tools/dataview/clean_dataset.py \
    --input telegram-channel-dataset/dataset.parquet \
    --output telegram-channel-dataset/cleaned.jsonl
"""

import argparse
import json
import re
import sys
from pathlib import Path

import pandas as pd
import pyarrow as pa
import pyarrow.parquet as pq


# ── Patterns to strip ──────────────────────────────────────────────────
STRIP_PATTERNS = [
    # Hashtags
    r'#[\w\u4e00-\u9fff]+',
    # Bot links / Telegram links
    r'\[.*?\]\(https?://t\.me/[^\)]+\)',
    r'https?://t\.me/\S+',
    r'https?://\S+',
    # Source/author lines
    r'来源：.*',
    r'作者：.*',
    r'Source:.*',
    r'Author:.*',
    # Ad / promo lines
    r'🚀.*',
    r'📚\s*教程目录.*',
    r'━━+',
    r'🤖.*我们的 Bot.*',
    r'🔥.*邪修频道.*',
    r'VPN推荐.*',
    r'NanoGPT.*',
    r'免费赠送.*',
    r'快来体验.*',
    r'Dubis.*',
    r'Bot 机器人.*',
    # Model name prefixes in titles
    r'^(✨|🔥|🧩|🖼️|🎞️|🎬|🏮|🎨|📷|📸|🌟|💡|🎭|🎪|🎬|🌙|🌅|🌸|🎭|🖌️)\s*',
    r'^GPT-?Image[-\s]*2?[｜|]',
    r'^GPTImage2?[｜|]',
    # Title lines (Chinese titles with emoji)
    r'^.*?[｜|].*?(prompt|模板|技巧|构图|写真|人像|海报|封面).*?$',
]

# Fields that contain prompt-relevant information
PROMPT_FIELDS = [
    '任务', '主体', '服装', '场景', '光线', '镜头', '风格', '构图',
    '约束', '画幅', '关键特征', '变体', '重点', '角色感', '妆造',
    '动作', '调色', '反差', '结构', '适用', '示例',
    'Task', 'Subject', 'Style', 'Lighting', 'Camera', 'Composition',
    'Prompt skeleton',
]

# Chinese field labels to English mapping
FIELD_MAP = {
    '任务': 'task',
    '主体': 'subject',
    '服装': 'clothing',
    '场景': 'scene',
    '光线': 'lighting',
    '镜头': 'camera',
    '风格': 'style',
    '构图': 'composition',
    '约束': 'avoid',
    '画幅': 'aspect_ratio',
    '关键特征': 'key_features',
    '变体': 'variants',
    '重点': 'focus',
    '角色感': 'character',
    '妆造': 'makeup',
    '动作': 'pose',
    '调色': 'color_grading',
    '反差': 'contrast',
    '结构': 'layout',
    '适用': 'use_case',
    '示例': 'examples',
    'Task': 'task',
    'Subject': 'subject',
    'Style': 'style',
    'Lighting': 'lighting',
    'Camera': 'camera',
    'Composition': 'composition',
    'Prompt skeleton': 'prompt_skeleton',
}


def clean_text(text: str) -> str:
    """Remove ads, links, hashtags, model names, and other noise."""
    if not text or pd.isna(text):
        return ''
    text = str(text)

    # Apply strip patterns
    for pattern in STRIP_PATTERNS:
        text = re.sub(pattern, '', text, flags=re.MULTILINE | re.IGNORECASE)

    # Remove lines that are just emojis or very short
    lines = text.split('\n')
    cleaned_lines = []
    for line in lines:
        line = line.strip()
        if not line:
            continue
        # Skip very short lines (likely noise)
        if len(line) < 3:
            continue
        # Skip lines that are mostly emojis
        emoji_chars = len(re.findall(r'[\U0001F300-\U0001F9FF]', line))
        if emoji_chars > len(line) * 0.5:
            continue
        cleaned_lines.append(line)

    return '\n'.join(cleaned_lines).strip()


def extract_structured_fields(text: str) -> dict:
    """Extract structured prompt fields from Chinese text."""
    if not text:
        return {}

    fields = {}
    # Match patterns like "字段名：值" or "字段名: value"
    field_names = '|'.join(re.escape(f) for f in PROMPT_FIELDS)
    field_pattern = re.compile(
        rf'^(?:[-•]\s*)?({field_names})[:：]\s*(.+?)(?:\n|$)',
        re.MULTILINE
    )

    for match in field_pattern.finditer(text):
        field_name = match.group(1).strip()
        value = match.group(2).strip()
        if value and len(value) > 2:
            eng_name = FIELD_MAP.get(field_name, field_name)
            fields[eng_name] = value

    return fields


def extract_prompt_skeleton(text: str) -> str:
    """Extract 'Prompt skeleton' section if present."""
    if not text:
        return ''
    match = re.search(r'Prompt skeleton[:\s]*\n(.+?)(?:\n\n|\n备注|\Z)', text, re.DOTALL)
    if match:
        return match.group(1).strip()
    return ''


def build_prompt(text: str) -> str:
    """Build a clean prompt from the structured fields."""
    if not text:
        return ''

    # Try to get prompt skeleton first (most direct)
    skeleton = extract_prompt_skeleton(text)
    if skeleton and len(skeleton) > 20:
        return skeleton

    # Extract structured fields
    fields = extract_structured_fields(text)

    if not fields:
        # Fallback: try to extract any English prompt-like content
        lines = text.split('\n')
        prompt_parts = []
        for line in lines:
            line = line.strip()
            # Skip Chinese-only lines
            chinese_chars = len(re.findall(r'[\u4e00-\u9fff]', line))
            if chinese_chars > len(line) * 0.3:
                continue
            # Skip very short lines
            if len(line) < 10:
                continue
            # Skip known noise
            if any(skip in line.lower() for skip in ['bot', 'http', '#', '来源', '作者', 'source', 'author']):
                continue
            prompt_parts.append(line)
        if prompt_parts:
            return '; '.join(prompt_parts)
        return ''

    # Build prompt from fields in logical order
    order = ['task', 'subject', 'character', 'makeup', 'clothing', 'pose',
             'scene', 'lighting', 'camera', 'composition', 'aspect_ratio',
             'key_features', 'style', 'color_grading', 'contrast', 'layout',
             'avoid', 'use_case', 'prompt_skeleton']

    parts = []
    for key in order:
        if key in fields:
            parts.append(fields[key])

    # Add any remaining fields not in order
    for key, val in fields.items():
        if key not in order and val:
            parts.append(val)

    return '; '.join(parts) if parts else ''


def group_images(df: pd.DataFrame) -> list:
    """Group consecutive images by prompt (same dimensions = same generation)."""
    groups = []
    current_group = None

    for idx, row in df.iterrows():
        has_text = row['text'] and str(row['text']).strip()
        has_image = row['image'] is not None and isinstance(row['image'], bytes) and len(row['image']) > 100

        if has_text and has_image:
            # Start new group
            if current_group:
                groups.append(current_group)
            current_group = {
                'message_id': row['message_id'],
                'datetime': row['datetime'],
                'raw_text': row['text'],
                'images': [{
                    'row_idx': idx,
                    'message_id': row['message_id'],
                    'width': row['width'],
                    'height': row['height'],
                    'image_bytes': row['image'],
                }]
            }
        elif current_group and has_image:
            # Check if dimensions match (same generation batch)
            last_img = current_group['images'][-1]
            if row['width'] == last_img['width'] and row['height'] == last_img['height']:
                current_group['images'].append({
                    'row_idx': idx,
                    'message_id': row['message_id'],
                    'width': row['width'],
                    'height': row['height'],
                    'image_bytes': row['image'],
                })

    if current_group:
        groups.append(current_group)

    return groups


def main():
    parser = argparse.ArgumentParser(description='Clean Telegram AI image dataset')
    parser.add_argument('--input', '-i', required=True, help='Input parquet file')
    parser.add_argument('--output', '-o', default=None, help='Output JSONL file')
    parser.add_argument('--parquet', default=None, help='Output cleaned parquet (with only useful columns)')
    parser.add_argument('--min-images', type=int, default=1, help='Min images per group')
    parser.add_argument('--min-prompt-len', type=int, default=10, help='Min prompt length')
    parser.add_argument('--stats', action='store_true', help='Print stats only')
    args = parser.parse_args()

    # Read parquet
    print(f"Reading {args.input}...")
    df = pq.read_table(args.input).to_pandas()
    print(f"  Total rows: {len(df)}")

    # Optionally output cleaned parquet (strip noise columns)
    if args.parquet:
        KEEP = ['message_id', 'datetime', 'media_type', 'text', 'width', 'height', 'image']
        available = [c for c in KEEP if c in df.columns]
        clean = df[available].copy()
        if 'media_type' in clean.columns:
            clean = clean[clean['media_type'] == 'photo'].copy()
        clean.reset_index(drop=True, inplace=True)
        pq.write_table(pa.Table.from_pandas(clean), args.parquet)
        print(f"  Cleaned parquet: {len(clean)} rows, {len(clean.columns)} cols -> {args.parquet}")

    # Group images
    groups = group_images(df)
    print(f"  Prompt groups: {len(groups)}")

    # Clean and filter
    cleaned = []
    for group in groups:
        prompt = build_prompt(group['raw_text'])
        if len(prompt) < args.min_prompt_len:
            continue
        if len(group['images']) < args.min_images:
            continue

        cleaned.append({
            'prompt': prompt,
            'raw_text': group['raw_text'],
            'num_images': len(group['images']),
            'message_id': group['message_id'],
            'datetime': group['datetime'],
            'images': group['images'],
        })

    print(f"  Cleaned groups: {len(cleaned)}")
    print(f"  Total images: {sum(g['num_images'] for g in cleaned)}")

    if args.stats:
        # Print distribution
        from collections import Counter
        sizes = Counter(g['num_images'] for g in cleaned)
        print("\n  Group size distribution:")
        for size, count in sorted(sizes.items()):
            print(f"    {size} images: {count} groups")

        # Show sample prompts
        print("\n  Sample prompts:")
        for g in cleaned[:5]:
            print(f"    [{g['num_images']} imgs] {g['prompt'][:120]}...")
        return

    # Output
    if args.output:
        output_path = Path(args.output)
        output_path.parent.mkdir(parents=True, exist_ok=True)

        with open(output_path, 'w', encoding='utf-8') as f:
            for group in cleaned:
                record = {
                    'prompt': group['prompt'],
                    'num_images': group['num_images'],
                    'message_id': group['message_id'],
                    'datetime': group['datetime'],
                    'image_paths': [
                        f"images/msg_{img['message_id']}_row_{img['row_idx']}.webp"
                        for img in group['images']
                    ],
                }
                f.write(json.dumps(record, ensure_ascii=False) + '\n')

        print(f"\n  Written to {output_path}")

        # Also save images
        img_dir = output_path.parent / 'images'
        img_dir.mkdir(exist_ok=True)

        from PIL import Image
        import io

        print("  Extracting images...")
        saved = 0
        for group in cleaned:
            for img_data in group['images']:
                try:
                    img = Image.open(io.BytesIO(img_data['image_bytes']))
                    img_path = img_dir / f"msg_{img_data['message_id']}_row_{img_data['row_idx']}.webp"
                    img.save(img_path, format='WEBP', quality=90)
                    saved += 1
                except Exception as e:
                    print(f"    Warning: Failed to save image {img_data['message_id']}: {e}")

        print(f"  Saved {saved} images to {img_dir}")
    else:
        # Just print stats
        from collections import Counter
        sizes = Counter(g['num_images'] for g in cleaned)
        print("\n  Group size distribution:")
        for size, count in sorted(sizes.items()):
            print(f"    {size} images: {count} groups")

        print("\n  Sample prompts:")
        for g in cleaned[:10]:
            print(f"    [{g['num_images']} imgs] {g['prompt'][:150]}...")


if __name__ == '__main__':
    main()