aimakerspace/text_utils.py

import os
from typing import List, Union
from pdfminer.high_level import extract_text
import io
from chainlit.types import AskFileResponse
import re


class TextFileLoader:
    def __init__(self, path: str, encoding: str = "utf-8"):
        self.documents = []
        self.path = path
        self.encoding = encoding

    def load(self):
        if os.path.isdir(self.path):
            self.load_directory()
        elif os.path.isfile(self.path) and self.path.endswith(".txt"):
            self.load_file()
        else:
            raise ValueError(
                "Provided path is neither a valid directory nor a .txt file."
            )

    def load_file(self):
        with open(self.path, "r", encoding=self.encoding) as f:
            self.documents.append(f.read())

    def load_directory(self):
        for root, _, files in os.walk(self.path):
            for file in files:
                if file.endswith(".txt"):
                    with open(
                        os.path.join(root, file), "r", encoding=self.encoding
                    ) as f:
                        self.documents.append(f.read())

    def load_documents(self):
        self.load()
        return self.documents
    
class PDFFileLoader(TextFileLoader):
    def __init__(self, path: str, encoding: str = "utf-8", content=None, files: list[AskFileResponse] = None):
        super().__init__(path, encoding)
        self.content = content
        self.files = files
        
    def load(self):
        if isinstance(self.files, List):
            for file in self.files:
                if file.content and file.path.endswith(".pdf"):
                    self.content = file.content
                    self.load_content()
        elif os.path.isdir(self.path):
            self.load_directory()
        elif os.path.isfile(self.path) and self.path.endswith(".pdf"):
            print("loading file ...")
            self.load_file()
        elif self.content and self.path.endswith(".pdf"):
            print("loading content ...")
            self.load_content()
        else:
            raise ValueError(
                "Provided path is neither a valid directory nor a .pdf file."
            )
        
    def load_content(self):
        """Load pdf already in memory"""
        text = extract_text(io.BytesIO(self.content))
        text = self.clean_text(text)
        self.documents.append(text)

    def clean_text(self, text):
        """Clean text by removing special characters."""
        # remove all \n
        text = text.replace('\n', ' ')
        text = re.sub(' +', ' ', text)
        # remove page number, we find it because it appears before '\x0c', use regex to find it
        text = re.sub(r'\d+ \x0c', '\x0c', text)
        # remove all '\x0c'
        text = text.replace('\x0c', ' ')
        return text

    def load_file(self):
        text = extract_text(pdf_file=self.path, codec=self.encoding)
        self.documents.append(text)

    def load_directory(self):
        for root, _, files in os.walk(self.path):
            for file in files:
                if file.endswith(".pdf"):
                    self.documents.append(
                        extract_text(os.path.join(root, file), encoding=self.encoding)
                    )



class CharacterTextSplitter:
    def __init__(
        self,
        chunk_size: int = 1000,
        chunk_overlap: int = 200,
    ):
        assert (
            chunk_size > chunk_overlap
        ), "Chunk size must be greater than chunk overlap"

        self.chunk_size = chunk_size
        self.chunk_overlap = chunk_overlap

    def split(self, text: str) -> List[str]:
        chunks = []
        for i in range(0, len(text), self.chunk_size - self.chunk_overlap):
            chunks.append(text[i : i + self.chunk_size])
        return chunks

    def split_texts(self, texts: List[str]) -> List[str]:
        chunks = []
        for text in texts:
            chunks.extend(self.split(text))
        return chunks


if __name__ == "__main__":
    loader = TextFileLoader("data/KingLear.txt")
    loader.load()
    splitter = CharacterTextSplitter()
    chunks = splitter.split_texts(loader.documents)
    print(len(chunks))
    print(chunks[0])
    print("--------")
    print(chunks[1])
    print("--------")
    print(chunks[-2])
    print("--------")
    print(chunks[-1])