"""
Quality Filters Module
========================
Filter samples by word count, profanity, language,
and low-quality response detection.
"""

from dataclasses import dataclass, field
from typing import List, Optional
import re
import pandas as pd


@dataclass
class QualityFilterConfig:
    """Configuration for quality filters."""
    min_word_count: int = 0
    max_word_count: int = 0          # 0 = no limit
    profanity_filter: bool = False
    language_filter: bool = False
    allowed_languages: List[str] = field(default_factory=lambda: ["en"])
    remove_low_quality: bool = False
    min_quality_length: int = 20


# ---------------------------------------------------------------------------
# Profanity word list (small built-in set, extend as needed)
# ---------------------------------------------------------------------------
_PROFANITY_WORDS = {
    'fuck', 'shit', 'damn', 'ass', 'bitch', 'bastard', 'crap',
    'dick', 'piss', 'slut', 'whore', 'cock',
}

# Generic filler/placeholder responses that indicate low quality
_GENERIC_RESPONSES = [
    "i don't know",
    "i am not sure",
    "no comment",
    "n/a",
    "none",
    "null",
    "test",
    "asdf",
    "lorem ipsum",
    "placeholder",
    "todo",
    "tbd",
]


def _word_count(text: str) -> int:
    """Count words in a text string."""
    if not isinstance(text, str):
        return 0
    return len(text.split())


def filter_by_word_count(
    df: pd.DataFrame,
    col: str,
    min_words: int = 0,
    max_words: int = 0,
) -> pd.DataFrame:
    """Filter rows by word count in the given column."""
    df = df.copy()
    counts = df[col].apply(_word_count)

    if min_words > 0:
        df = df[counts >= min_words]
        counts = counts[df.index]

    if max_words > 0:
        df = df[counts <= max_words]

    return df.reset_index(drop=True)


def contains_profanity(text: str) -> bool:
    """Check if text contains any profanity words."""
    if not isinstance(text, str):
        return False
    words = set(re.findall(r'\b\w+\b', text.lower()))
    return bool(words & _PROFANITY_WORDS)


def filter_profanity(
    df: pd.DataFrame,
    col: str,
) -> pd.DataFrame:
    """Remove rows containing profanity in the given column."""
    mask = ~df[col].apply(contains_profanity)
    return df[mask].reset_index(drop=True)


def detect_language(text: str) -> str:
    """
    Detect the language of a text string.
    Returns ISO 639-1 code (e.g., 'en', 'fr', 'de').
    Falls back to 'unknown' if detection fails.
    """
    try:
        from langdetect import detect
        if not isinstance(text, str) or len(text.strip()) < 10:
            return 'unknown'
        return detect(text)
    except ImportError:
        return 'unknown'
    except Exception:
        return 'unknown'


def filter_by_language(
    df: pd.DataFrame,
    col: str,
    allowed_langs: List[str] = None,
) -> pd.DataFrame:
    """Keep only rows where the text is in one of the allowed languages."""
    if allowed_langs is None:
        allowed_langs = ['en']

    langs = df[col].apply(detect_language)
    mask = langs.isin(allowed_langs) | (langs == 'unknown')
    return df[mask].reset_index(drop=True)


def is_low_quality(text: str, min_len: int = 20) -> bool:
    """
    Check if a response is low-quality:
    - Too short
    - Matches generic/placeholder patterns
    """
    if not isinstance(text, str):
        return True
    text_stripped = text.strip()
    if len(text_stripped) < min_len:
        return True
    text_lower = text_stripped.lower()
    for phrase in _GENERIC_RESPONSES:
        if text_lower == phrase or text_lower.startswith(phrase):
            return True
    return False


def filter_low_quality(
    df: pd.DataFrame,
    col: str,
    min_len: int = 20,
) -> pd.DataFrame:
    """Remove low-quality responses."""
    mask = ~df[col].apply(lambda t: is_low_quality(t, min_len))
    return df[mask].reset_index(drop=True)


def apply_quality_filters(
    df: pd.DataFrame,
    col: str,
    config: QualityFilterConfig,
) -> pd.DataFrame:
    """Apply all enabled quality filters to a DataFrame."""
    if config.min_word_count > 0 or config.max_word_count > 0:
        df = filter_by_word_count(df, col, config.min_word_count, config.max_word_count)

    if config.profanity_filter:
        df = filter_profanity(df, col)

    if config.language_filter:
        df = filter_by_language(df, col, config.allowed_languages)

    if config.remove_low_quality:
        df = filter_low_quality(df, col, config.min_quality_length)

    return df