import PyPDF2
import docx
import nltk, subprocess, os
from config import nltk_path, converter
from typing import List, Union
from pathlib import Path
import re
import pandas as pd
from nltk.tokenize import sent_tokenize
import spacy
from langdetect import detect
from milvus_process import update_mulvus_file
import fitz
from marker.output import text_from_rendered
try:
    nltk.data.find(nltk_path)
except LookupError:
    nltk.download('punkt')
try:
    nlp = spacy.load("zh_core_web_sm")
except OSError:
    pass
class DocumentProcessor:
    def __init__(self,):
        pass
    def get_file_len(self, file_path: Union[str, Path]) -> int:
        text = self.read_file(file_path)
        length = len(text)
        del text
        return length
    
    @staticmethod
    def convert_docx_to_doc(input_path):
        output_dir = os.path.dirname(input_path)
        command = [
            "soffice", "--headless", "--convert-to", "docx", input_path, "--outdir", output_dir
        ]
        subprocess.run(command, check=True)
        
    def _read_doc(self, file_path) -> str:
        """读取Word文档"""
        self.convert_docx_to_doc(file_path)
        old_file = Path(file_path)  # 原始 .doc 文件
        new_file = old_file.with_suffix(".docx")  # 转换后的 .docx 文件

        if old_file.exists():  # 确保旧文件存在
            old_file.unlink()  # 删除旧文件

        doc = docx.Document(new_file)  # 读取 .docx
        text = "\n".join([paragraph.text for paragraph in doc.paragraphs])
        return self.create_chunks(text)
    
    def read_file(self, file_path: Union[str, Path]) -> str:
        """
        读取不同格式的文档
        
        Args:
            file_path: 文件路径
            
        Returns:
            str: 提取的文本内容
        """
        file_path = Path(file_path)
        extension = file_path.suffix.lower()
        
        if extension == '.pdf':
            return self._read_pdf(file_path)
        elif extension == '.docx':
            return self._read_docx(file_path)
        elif extension == '.doc':
            return self._read_doc(file_path)
        elif extension == '.txt':
            return self._read_txt(file_path)
        elif extension == '.csv':
            return self._read_csv(file_path)
        elif extension == '.xlsx':
            return self._read_excel(file_path)
        else:
            raise ValueError(f"Unsupported file format: {extension}")
    
    def _read_pdf(self, file_path) -> str:
        """读取PDF文件"""
        rendered = converter(str(file_path))
        text, x, images = text_from_rendered(rendered)
        return self.create_chunks(text=text)
            
    def _read_docx(self, file_path: Path) -> str:
        """读取Word文档"""
        doc = docx.Document(file_path)
        text = "\n".join([paragraph.text for paragraph in doc.paragraphs])
        return self.create_chunks(text)
    
    def _read_txt(self, file_path: Path) -> str:
        """读取文本文件"""
        with open(file_path, 'r', encoding='utf-8') as file:
            return self.create_chunks(file.read())
    
    def _read_excel(self, file_path: Path) -> str:
        """读取Excel文件"""
        df = pd.read_excel(file_path, sheet_name=None)
        text = ""
        for sheet_name, sheet_df in df.items():
            text += f"\nSheet: {sheet_name}\n"
            text += sheet_df.to_csv(index=False, sep=' ', header=True)
        return self.create_chunks(text)

    def _read_csv(self, file_path: Path) -> str:
        """读取CSV文件"""
        df = pd.read_csv(file_path)
        return self.create_chunks(df.to_csv(index=False, sep=' ', header=True))

    
    def _clean_text(self, text: str) -> str:
        """
        清理文本
        - 移除多余的空白字符
        - 标准化换行符
        """
        # 替换多个空格为单个空格
        text = re.sub(r'\s+', ' ', text)
        # 标准化换行符
        text = text.replace('\r\n', '\n').replace('\r', '\n')
        # 移除空行
        text = '\n'.join(line.strip() for line in text.split('\n') if line.strip())
        return text.strip()
    
    def split_into_sentences(self, text: str) -> List[str]:
        """
        将文本分割成句子
        
        Args:
            text: 输入文本
            
        Returns:
            List[str]: 句子列表
        """
        # 使用NLTK进行句子分割
        sentences = sent_tokenize(text)
        return sentences
    def force_split_sentence(self, sentence: str, max_length: int) -> List[str]:
        """
        强制将超长句子按字符数切分
        
        Args:
            sentence (str): 输入的句子
            max_length (int): 最大长度
            
        Returns:
            List[str]: 切分后的句子片段列表
        """
        # 使用标点符号作为次要切分点
        punctuation = '。，；！？,.;!?'
        parts = []
        current_part = ''
        
        # 优先在标点符号处切分
        chars = list(sentence)
        for i, char in enumerate(chars):
            current_part += char
            
            # 如果当前部分达到最大长度或遇到标点符号
            if (len(current_part) >= max_length and char in punctuation) or \
            (len(current_part) >= max_length * 1.2):  # 允许略微超过max_length以寻找标点
                parts.append(current_part)
                current_part = ''
        
        # 处理剩余部分
        if current_part:
            # 如果剩余部分仍然过长，强制按长度切分
            while len(current_part) > max_length:
                parts.append(current_part[:max_length] + '...')
                current_part = '...' + current_part[max_length:]
            parts.append(current_part)
        
        return parts

    def split_text_nltk(self, text: str, chunk_size: int = 1500, overlap_size: int = 100) -> List[str]:
        """
        使用NLTK进行中文文本分割，支持文本块重叠和超长句子处理
        
        Args:
            text (str): 输入的中文文本
            chunk_size (int): 每个chunk的近似字符数
            overlap_size (int): 相邻chunk之间的重叠字符数
            
        Returns:
            List[str]: 分割后的文本块列表
        """
        text = self._clean_text(text)
        sentences = nltk.sent_tokenize(text)
        chunks = self.process_sentences(sentences=sentences, chunk_size=chunk_size, overlap_size=overlap_size)
        return chunks

    def split_text_spacy(self, text: str, chunk_size: int = 500, overlap_size: int = 100) -> List[str]:
        """
        使用SpaCy进行中文文本分割，支持文本块重叠和超长句子处理
        
        Args:
            text (str): 输入的中文文本
            chunk_size (int): 每个chunk的近似字符数
            overlap_size (int): 相邻chunk之间的重叠字符数
            
        Returns:
            List[str]: 分割后的文本块列表
        """
        text = self._clean_text(text)
        doc = nlp(text)
        chunks = []
        sentences = [sent.text for sent in doc.sents]
        chunks = self.process_sentences(sentences=sentences, chunk_size=chunk_size, overlap_size=overlap_size)
        return chunks


    def process_sentences(self, sentences, chunk_size: int = 500, overlap_size: int = 100):
        chunks = []
        current_chunk = []
        current_length = 0
        
        for sentence in sentences:
            # 处理超长句子
            if len(sentence) > chunk_size:
                # 先处理当前chunk中已有的内容
                if current_chunk:
                    chunks.append("".join(current_chunk))
                    current_chunk = []
                    current_length = 0
                
                # 强制切分超长句子
                sentence_parts = self.force_split_sentence(sentence, chunk_size)
                for part in sentence_parts:
                    chunks.append(part)
                continue
            
            # 正常处理普通长度的句子
            if current_length + len(sentence) <= chunk_size:
                current_chunk.append(sentence)
                current_length += len(sentence)
            else:
                if current_chunk:
                    chunks.append("".join(current_chunk))
                    
                # 处理重叠
                overlap_chars = 0
                overlap_sentences = []
                for prev_sentence in reversed(current_chunk):
                    if overlap_chars + len(prev_sentence) <= overlap_size:
                        overlap_sentences.insert(0, prev_sentence)
                        overlap_chars += len(prev_sentence)
                    else:
                        break
                
                current_chunk = overlap_sentences + [sentence]
                current_length = sum(len(s) for s in current_chunk)
        
        if current_chunk:
            chunks.append("".join(current_chunk))
        
        return chunks
    def create_chunks(self, text: str, chunk_size=300, overlap_size=100) -> List[str]:
        is_chinese = self.is_chinese_text(text)

        if is_chinese:
            # print('检测为中文文章, 采用spacy')
            chunks = self.split_text_spacy(text,chunk_size=chunk_size,overlap_size=overlap_size)
        else:
            # print('检测为外文文章, 采用nltk')
            chunks = self.split_text_spacy(text,chunk_size=chunk_size,overlap_size=overlap_size)
        return chunks

        
    def is_chinese_text(self, text: str) -> bool:
        """
        判断文本是否主要为中文
        
        Args:
            text (str): 输入文本
            
        Returns:
            bool: 如果是中文文本返回True，否则返回False
        """
        try:
            chinese_chars = len(re.findall(r'[\u4e00-\u9fff]', text))
            total_chars = len(re.findall(r'\w', text)) + chinese_chars
            char_ratio = chinese_chars / max(total_chars, 1)
            if char_ratio > 0.1:
                return True
            # 使用langdetect进行语言检测
            lang = detect(text)
            # 如果检测失败，使用备用方法
            if not lang:
                raise Exception("Language detection failed")
                
            return lang == 'zh-cn' or lang == 'zh-tw' or lang == 'zh'
        
        except Exception:
            return char_ratio > 0.1
    def process_document(self, file_path: Union[str, Path], chunk_size=1000, overlap_size=250) -> List[str]:
        """
        处理文档的主方法
        
        Args:
            file_path: 文档路径
            
        Returns:
            List[str]: 处理后的文本块列表
        """
        # 读取文档
        text = self.read_file(file_path)
        chunks = self.create_chunks(text=text, chunk_size=chunk_size, overlap_size=overlap_size)
        # return chunks
        return chunks

if __name__ == '__main__':
    import asyncio
    processor = DocumentProcessor()

    # 处理文档
    chunks = processor.read_file("./tests/test.pdf")
    # 打印结果
    # for i, chunk in enumerate(chunks):
    #     print(f"Chunk {i+1}:")
    #     print(chunk)
    #     print(len(chunk))
    #     print("-" * 50)
    status = asyncio.run(update_mulvus_file(client_id='test', file_name='test.pdf',chunks=chunks))