code/modules/data_loader.py

import os
import re
import requests
import pysrt
from langchain.document_loaders import (
    PyMuPDFLoader,
    Docx2txtLoader,
    YoutubeLoader,
    WebBaseLoader,
    TextLoader,
)
from langchain_community.document_loaders import UnstructuredMarkdownLoader
from llama_parse import LlamaParse
from langchain.schema import Document
import logging
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_experimental.text_splitter import SemanticChunker
from langchain_openai.embeddings import OpenAIEmbeddings

logger = logging.getLogger(__name__)


class PDFReader:
    def __init__(self):
        pass

    def get_loader(self, pdf_path):
        loader = PyMuPDFLoader(pdf_path)
        return loader

    def get_documents(self, loader):
        return loader.load()


class FileReader:
    def __init__(self):
        self.pdf_reader = PDFReader()

    def extract_text_from_pdf(self, pdf_path):
        text = ""
        with open(pdf_path, "rb") as file:
            reader = PyPDF2.PdfReader(file)
            num_pages = len(reader.pages)
            for page_num in range(num_pages):
                page = reader.pages[page_num]
                text += page.extract_text()
        return text

    def download_pdf_from_url(self, pdf_url):
        response = requests.get(pdf_url)
        if response.status_code == 200:
            with tempfile.NamedTemporaryFile(delete=False, suffix=".pdf") as temp_file:
                temp_file.write(response.content)
                temp_file_path = temp_file.name
            return temp_file_path
        else:
            print("Failed to download PDF from URL:", pdf_url)
            return None

    def read_pdf(self, temp_file_path: str):
        # parser = LlamaParse(
        #     api_key="",
        #     result_type="markdown",
        #     num_workers=4,
        #     verbose=True,
        #     language="en",
        # )
        # documents = parser.load_data(temp_file_path)

        # with open("temp/output.md", "a") as f:
        #     for doc in documents:
        #         f.write(doc.text + "\n")

        # markdown_path = "temp/output.md"
        # loader = UnstructuredMarkdownLoader(markdown_path)
        # loader = PyMuPDFLoader(temp_file_path)  # This loader preserves more metadata
        # return loader.load()
        loader = self.pdf_reader.get_loader(temp_file_path)
        documents = self.pdf_reader.get_documents(loader)
        return documents

    def read_txt(self, temp_file_path: str):
        loader = TextLoader(temp_file_path, autodetect_encoding=True)
        return loader.load()

    def read_docx(self, temp_file_path: str):
        loader = Docx2txtLoader(temp_file_path)
        return loader.load()

    def read_srt(self, temp_file_path: str):
        subs = pysrt.open(temp_file_path)
        text = ""
        for sub in subs:
            text += sub.text
        return [Document(page_content=text)]

    def read_youtube_transcript(self, url: str):
        loader = YoutubeLoader.from_youtube_url(
            url, add_video_info=True, language=["en"], translation="en"
        )
        return loader.load()

    def read_html(self, url: str):
        loader = WebBaseLoader(url)
        return loader.load()


class ChunkProcessor:
    def __init__(self, config):
        self.config = config
        self.document_chunks_full = []
        self.document_names = []

        if config["splitter_options"]["use_splitter"]:
            if config["splitter_options"]["split_by_token"]:
                self.splitter = RecursiveCharacterTextSplitter.from_tiktoken_encoder(
                    chunk_size=config["splitter_options"]["chunk_size"],
                    chunk_overlap=config["splitter_options"]["chunk_overlap"],
                    separators=config["splitter_options"]["chunk_separators"],
                    disallowed_special=(),
                )
            else:
                self.splitter = RecursiveCharacterTextSplitter(
                    chunk_size=config["splitter_options"]["chunk_size"],
                    chunk_overlap=config["splitter_options"]["chunk_overlap"],
                    separators=config["splitter_options"]["chunk_separators"],
                    disallowed_special=(),
                )
        else:
            self.splitter = None
        logger.info("ChunkProcessor instance created")

    def remove_delimiters(self, document_chunks: list):
        for chunk in document_chunks:
            for delimiter in self.config["splitter_options"]["delimiters_to_remove"]:
                chunk.page_content = re.sub(delimiter, " ", chunk.page_content)
        return document_chunks

    def remove_chunks(self, document_chunks: list):
        front = self.config["splitter_options"]["front_chunk_to_remove"]
        end = self.config["splitter_options"]["last_chunks_to_remove"]
        for _ in range(front):
            del document_chunks[0]
        for _ in range(end):
            document_chunks.pop()
        logger.info(f"\tNumber of pages after skipping: {len(document_chunks)}")
        return document_chunks

    def process_chunks(self, documents):
        if self.splitter:
            document_chunks = self.splitter.split_documents(documents)
        else:
            document_chunks = documents

        if self.config["splitter_options"]["remove_leftover_delimiters"]:
            document_chunks = self.remove_delimiters(document_chunks)
        if self.config["splitter_options"]["remove_chunks"]:
            document_chunks = self.remove_chunks(document_chunks)

        return document_chunks

    def get_chunks(self, file_reader, uploaded_files, weblinks):
        self.document_chunks_full = []
        self.document_names = []

        for file_index, file_path in enumerate(uploaded_files):
            file_name = os.path.basename(file_path)
            file_type = file_name.split(".")[-1].lower()

            try:
                if file_type == "pdf":
                    documents = file_reader.read_pdf(file_path)
                elif file_type == "txt":
                    documents = file_reader.read_txt(file_path)
                elif file_type == "docx":
                    documents = file_reader.read_docx(file_path)
                elif file_type == "srt":
                    documents = file_reader.read_srt(file_path)
                else:
                    logger.warning(f"Unsupported file type: {file_type}")
                    continue

                document_chunks = self.process_chunks(documents)
                self.document_names.append(file_name)
                self.document_chunks_full.extend(document_chunks)

            except Exception as e:
                logger.error(f"Error processing file {file_name}: {str(e)}")

        self.process_weblinks(file_reader, weblinks)

        logger.info(
            f"Total document chunks extracted: {len(self.document_chunks_full)}"
        )
        return self.document_chunks_full, self.document_names

    def process_weblinks(self, file_reader, weblinks):
        if weblinks[0] != "":
            logger.info(f"Splitting weblinks: total of {len(weblinks)}")

            for link_index, link in enumerate(weblinks):
                try:
                    logger.info(f"\tSplitting link {link_index+1} : {link}")
                    if "youtube" in link:
                        documents = file_reader.read_youtube_transcript(link)
                    else:
                        documents = file_reader.read_html(link)

                    document_chunks = self.process_chunks(documents)
                    self.document_names.append(link)
                    self.document_chunks_full.extend(document_chunks)
                except Exception as e:
                    logger.error(
                        f"Error splitting link {link_index+1} : {link}: {str(e)}"
                    )


class DataLoader:
    def __init__(self, config):
        self.file_reader = FileReader()
        self.chunk_processor = ChunkProcessor(config)

    def get_chunks(self, uploaded_files, weblinks):
        return self.chunk_processor.get_chunks(
            self.file_reader, uploaded_files, weblinks
        )