import csv
import json
import io
import tempfile
import re
import numpy as np
from datetime import datetime
from functools import lru_cache
from collections import Counter
from typing import List, Dict, Optional, Tuple
import nltk
from nltk.corpus import stopwords

from config import config
from models import handle_errors

# Initialize NLTK
try:
    nltk.download('stopwords', quiet=True)
    nltk.download('punkt', quiet=True)
    STOP_WORDS = set(stopwords.words('english'))
except:
    STOP_WORDS = {'the', 'a', 'an', 'and', 'or', 'but', 'in', 'on', 'at', 'to', 'for', 'of', 'with', 'by'}

# Simplified Text Processing
class TextProcessor:
    """Optimized text processing with multi-language support"""
    
    @staticmethod
    @lru_cache(maxsize=config.CACHE_SIZE)
    def clean_text(text: str, remove_punctuation: bool = True, remove_numbers: bool = False) -> str:
        """Clean text with language awareness"""
        text = text.strip()
        
        # Don't clean Chinese text aggressively
        if re.search(r'[\u4e00-\u9fff]', text):
            return text
        
        text = text.lower()
        
        if remove_numbers:
            text = re.sub(r'\d+', '', text)
        
        if remove_punctuation:
            text = re.sub(r'[^\w\s]', '', text)
        
        words = text.split()
        cleaned_words = [w for w in words if w not in STOP_WORDS and len(w) >= config.MIN_WORD_LENGTH]
        return ' '.join(cleaned_words)
    
    @staticmethod
    def parse_batch_input(text: str) -> List[str]:
        """Parse batch input from textarea"""
        lines = text.strip().split('\n')
        return [line.strip() for line in lines if line.strip()]

# Enhanced History Manager
class HistoryManager:
    """Enhanced history management with filtering"""
    def __init__(self):
        self._history = []
    
    def add(self, entry: Dict):
        """Add entry with timestamp"""
        entry['timestamp'] = datetime.now().isoformat()
        self._history.append(entry)
        if len(self._history) > config.MAX_HISTORY_SIZE:
            self._history = self._history[-config.MAX_HISTORY_SIZE:]
    
    def add_batch(self, entries: List[Dict]):
        """Add multiple entries"""
        for entry in entries:
            self.add(entry)
    
    def get_all(self) -> List[Dict]:
        return self._history.copy()
    
    def get_recent(self, n: int = 10) -> List[Dict]:
        return self._history[-n:] if self._history else []
    
    def filter_by(self, sentiment: str = None, language: str = None, 
                  min_confidence: float = None) -> List[Dict]:
        """Filter history by criteria"""
        filtered = self._history
        
        if sentiment:
            filtered = [h for h in filtered if h['sentiment'] == sentiment]
        if language:
            filtered = [h for h in filtered if h.get('language', 'en') == language]
        if min_confidence:
            filtered = [h for h in filtered if h['confidence'] >= min_confidence]
            
        return filtered
    
    def clear(self) -> int:
        count = len(self._history)
        self._history.clear()
        return count
    
    def size(self) -> int:
        return len(self._history)
    
    def get_stats(self) -> Dict:
        """Get comprehensive statistics"""
        if not self._history:
            return {}
        
        sentiments = [item['sentiment'] for item in self._history]
        confidences = [item['confidence'] for item in self._history]
        languages = [item.get('language', 'en') for item in self._history]
        
        return {
            'total_analyses': len(self._history),
            'positive_count': sentiments.count('Positive'),
            'negative_count': sentiments.count('Negative'),
            'neutral_count': sentiments.count('Neutral'),
            'avg_confidence': np.mean(confidences),
            'max_confidence': np.max(confidences),
            'min_confidence': np.min(confidences),
            'languages_detected': len(set(languages)),
            'most_common_language': Counter(languages).most_common(1)[0][0] if languages else 'en'
        }

# Universal Data Handler
class DataHandler:
    """Enhanced data operations"""
    
    @staticmethod
    @handle_errors(default_return=(None, "Export failed"))
    def export_data(data: List[Dict], format_type: str) -> Tuple[Optional[str], str]:
        """Export data with comprehensive information"""
        if not data:
            return None, "No data to export"
        
        temp_file = tempfile.NamedTemporaryFile(mode='w', delete=False, 
                                               suffix=f'.{format_type}', encoding='utf-8')
        
        if format_type == 'csv':
            writer = csv.writer(temp_file)
            writer.writerow(['Timestamp', 'Text', 'Sentiment', 'Confidence', 'Language', 
                           'Pos_Prob', 'Neg_Prob', 'Neu_Prob', 'Word_Count'])
            for entry in data:
                writer.writerow([
                    entry.get('timestamp', ''),
                    entry.get('text', ''),
                    entry.get('sentiment', ''),
                    f"{entry.get('confidence', 0):.4f}",
                    entry.get('language', 'en'),
                    f"{entry.get('pos_prob', 0):.4f}",
                    f"{entry.get('neg_prob', 0):.4f}",
                    f"{entry.get('neu_prob', 0):.4f}",
                    entry.get('word_count', 0)
                ])
        elif format_type == 'json':
            json.dump(data, temp_file, indent=2, ensure_ascii=False)
        
        temp_file.close()
        return temp_file.name, f"Exported {len(data)} entries"
    
    @staticmethod
    @handle_errors(default_return="")
    def process_file(file) -> str:
        """Process uploaded files"""
        if not file:
            return ""
        
        content = file.read().decode('utf-8')
        
        if file.name.endswith('.csv'):
            csv_file = io.StringIO(content)
            reader = csv.reader(csv_file)
            try:
                next(reader)  # Skip header
                texts = []
                for row in reader:
                    if row and row[0].strip():
                        text = row[0].strip().strip('"')
                        if text:
                            texts.append(text)
                return '\n'.join(texts)
            except:
                lines = content.strip().split('\n')[1:]
                texts = []
                for line in lines:
                    if line.strip():
                        text = line.strip().strip('"')
                        if text:
                            texts.append(text)
                return '\n'.join(texts)
        
        return content