"""
文字內容分析模組
"""

import re
import jieba
from typing import Dict, List, Optional
import logging
from collections import Counter

logger = logging.getLogger(__name__)

class TextAnalyzer:
    """文字內容分析器"""
    
    def __init__(self):
        """初始化文字分析器"""
        # 初始化jieba分詞
        jieba.initialize()
        
        # 情感詞典（簡化版）
        self.positive_words = {
            "好", "棒", "讚", "優秀", "完美", "喜歡", "愛", "開心", "快樂", "高興",
            "滿意", "驚喜", "感動", "溫暖", "美好", "精彩", "出色", "傑出", "優秀"
        }
        
        self.negative_words = {
            "壞", "差", "爛", "討厭", "恨", "生氣", "憤怒", "失望", "難過", "痛苦",
            "糟糕", "惡劣", "可惡", "討厭", "煩人", "無聊", "討厭", "噁心", "恐怖"
        }
        
        # 停用詞
        self.stop_words = {
            "的", "了", "在", "是", "我", "有", "和", "就", "不", "人", "都", "一",
            "一個", "上", "也", "很", "到", "說", "要", "去", "你", "會", "著", "沒有",
            "看", "好", "自己", "這", "那", "他", "她", "它", "們", "我們", "你們"
        }
    
    def analyze(self, text: str, analysis_type: str = "comprehensive") -> Dict:
        """
        分析文字內容
        
        Args:
            text: 要分析的文字
            analysis_type: 分析類型
            
        Returns:
            分析結果字典
        """
        try:
            results = {
                "original_text": text,
                "analysis_type": analysis_type,
                "word_count": len(text),
                "char_count": len(text.replace(" ", "")),
                "sentiment": self._analyze_sentiment(text),
                "keywords": self._extract_keywords(text),
                "topics": self._extract_topics(text),
                "summary": ""
            }
            
            # 根據分析類型添加特定分析
            if analysis_type in ["comprehensive", "sentiment"]:
                results["sentiment_score"] = self._calculate_sentiment_score(text)
            
            if analysis_type in ["comprehensive", "content_classification"]:
                results["content_category"] = self._classify_content(text)
                results["language"] = self._detect_language(text)
            
            if analysis_type in ["comprehensive", "keyword_extraction"]:
                results["named_entities"] = self._extract_named_entities(text)
            
            # 生成總結
            results["summary"] = self._generate_summary(results)
            
            logger.info(f"文字分析完成: {analysis_type}")
            return results
            
        except Exception as e:
            logger.error(f"文字分析失敗: {e}")
            return {"error": str(e)}
    
    def _analyze_sentiment(self, text: str) -> str:
        """分析情感傾向"""
        words = jieba.lcut(text)
        
        positive_count = sum(1 for word in words if word in self.positive_words)
        negative_count = sum(1 for word in words if word in self.negative_words)
        
        if positive_count > negative_count:
            return "正面"
        elif negative_count > positive_count:
            return "負面"
        else:
            return "中性"
    
    def _calculate_sentiment_score(self, text: str) -> float:
        """計算情感分數 (-1 到 1)"""
        words = jieba.lcut(text)
        
        positive_count = sum(1 for word in words if word in self.positive_words)
        negative_count = sum(1 for word in words if word in self.negative_words)
        total_words = len(words)
        
        if total_words == 0:
            return 0.0
        
        score = (positive_count - negative_count) / total_words
        return max(-1.0, min(1.0, score))
    
    def _extract_keywords(self, text: str, top_k: int = 10) -> List[str]:
        """提取關鍵詞"""
        words = jieba.lcut(text)
        
        # 過濾停用詞和短詞
        filtered_words = [
            word for word in words 
            if len(word) > 1 and word not in self.stop_words
        ]
        
        # 計算詞頻
        word_freq = Counter(filtered_words)
        
        # 返回最常見的詞
        return [word for word, freq in word_freq.most_common(top_k)]
    
    def _extract_topics(self, text: str) -> List[str]:
        """提取主題（簡化版）"""
        # 這裡使用簡單的關鍵詞提取作為主題
        keywords = self._extract_keywords(text, top_k=5)
        
        # 可以根據需要添加更複雜的主題建模
        return keywords
    
    def _classify_content(self, text: str) -> str:
        """內容分類"""
        # 簡化的內容分類
        if any(word in text for word in ["新聞", "報導", "消息", "事件"]):
            return "新聞"
        elif any(word in text for word in ["評論", "觀點", "看法", "認為"]):
            return "評論"
        elif any(word in text for word in ["問題", "求助", "請教", "怎麼辦"]):
            return "問答"
        elif any(word in text for word in ["分享", "推薦", "介紹", "推薦"]):
            return "分享"
        else:
            return "一般"
    
    def _detect_language(self, text: str) -> str:
        """檢測語言"""
        # 簡單的中文檢測
        chinese_chars = len(re.findall(r'[\u4e00-\u9fff]', text))
        total_chars = len(text.replace(" ", ""))
        
        if total_chars == 0:
            return "未知"
        
        chinese_ratio = chinese_chars / total_chars
        
        if chinese_ratio > 0.5:
            return "中文"
        else:
            return "其他"
    
    def _extract_named_entities(self, text: str) -> List[str]:
        """提取命名實體（簡化版）"""
        # 簡單的實體提取
        entities = []
        
        # 提取可能的姓名（2-4個中文字符）
        names = re.findall(r'[\u4e00-\u9fff]{2,4}', text)
        entities.extend(names)
        
        # 提取可能的組織名稱
        org_patterns = [
            r'[\u4e00-\u9fff]+公司',
            r'[\u4e00-\u9fff]+大學',
            r'[\u4e00-\u9fff]+政府',
            r'[\u4e00-\u9fff]+協會'
        ]
        
        for pattern in org_patterns:
            orgs = re.findall(pattern, text)
            entities.extend(orgs)
        
        return list(set(entities))
    
    def _generate_summary(self, results: Dict) -> str:
        """生成分析總結"""
        summary_parts = []
        
        summary_parts.append(f"文字長度: {results['char_count']} 字符")
        summary_parts.append(f"情感傾向: {results['sentiment']}")
        
        if 'sentiment_score' in results:
            score = results['sentiment_score']
            summary_parts.append(f"情感分數: {score:.2f}")
        
        if results['keywords']:
            summary_parts.append(f"主要關鍵詞: {', '.join(results['keywords'][:5])}")
        
        if 'content_category' in results:
            summary_parts.append(f"內容類型: {results['content_category']}")
        
        return " | ".join(summary_parts)