extract_data.py

import os
import re
import string
import json
import logging
import shutil
from datetime import datetime
from tqdm import tqdm

import fitz
from datasets import Dataset, load_dataset
logger = logging.getLogger(__name__)
##########################################
###### FILL MANUALLY #####################
#name of parquet files
source = "kb_books"
#how many years should go in one parquet file (do not change!)
n_chunks = 1
#how many docs in 1 parquet
n_batch = 5
#paths
input_path = os.path.join("..","..","kb-books","raw")
output_path = os.path.join(".","data")
logs = os.path.join(".","log")
#first year to process
start_year = 1751
#last year to process
stop_year = 1752
#misc folders in data
unwanted_folders = ("README.txt","logs")
#demo run for testing, if true, only first page is read
demo = False
#location of reference filenames for public domain documents
ref_pd_location = os.path.join(".","pd_check","public_domain_files.txt")
with open(ref_pd_location, 'r') as pd_files:
    ref_pd_list = pd_files.read().splitlines()
############################################
def find_author_json(data: dict[str,dict[str,dict[str,str]]]) -> str:
    """
    A function for finding the author(s) from various possible locations in the json metadata.
    """
    try:
        author = data.get("pnx").get("addata")["au"]
    except KeyError:
        author = []
    try:
        add_author = data.get("pnx").get("addata")["addau"]
    except KeyError:
        add_author = []

    authors = list(set(author)) + list(set(add_author))
    authors = "; ".join(authors)

    if len(authors) < 1:
        try:
            authors = data.get("pnx").get("sort")["author"]
            authors = "; ".join(authors)
        except KeyError:
            pass
    
    if len(authors) < 1:
        try:
            authors = data.get("pnx").get("display")["creator"]
            authors = "; ".join(authors)
        except KeyError:
            authors = "missing"

    return (authors)
    

def find_title_json(data: dict[str,dict[str,dict[str,str]]]) -> str:
    """
    A function for finding the title from various possible locations in the json metadata.
    """
    try:
        title = data.get("pnx").get("display")["title"][0]
    except KeyError:
        title = []
    if len(title) < 1:
        try:
            title = data.get("pnx").get("addata")["btitle"][0]
        except KeyError:
            pass
    else:
        pass
    if len(title) < 1:
        try:
            title = data.get("pnx").get("sort")["title"][0]
        except KeyError:
            pass
    if len(title) < 1:
        title = "missing"
    return(title)


def find_digitalization(data: dict[str,dict[str,dict[str,str]]]) -> str:
    """
    A function for finding the digitalization date from various possible locations in the json metadata.
    """
    try:
        digit = data.get("pnx").get("display")["creationdate"][0]
        #last 4 digit number in string
        digit = re.findall(r"\d{4}$",digit)[0]
    except KeyError:
        digit = []
    if len(digit) < 1:
        try:
            digit = data.get("pnx").get("addata")["date"][1]
            digit = re.findall(r"\d{4}$",digit)[0]
        except KeyError:
            digit = "missing"
    return(digit)

def find_source(data: dict[str,dict[str,dict[str,str]]]) -> str:
    """
    A function for finding source of the document from the json metadata.
    """
    try:
        doc_source = data.get("pnx").get("display")["lds50"]
        #last 4 digit number in string
        doc_source = [i for i in doc_source if "Digi" not in i][0]
    except (KeyError, IndexError):
        doc_source = "missing"
    return doc_source

#filter alive and missing people
def dead_70_yrs_ago(ds):
    """filter for the scraped authors to find ones who have died 70 years ago"""
    birth_miss = False
    death_miss = False
    try:
        birth_yr = int(ds["born"])
        if birth_yr > 1955:
            birth = False
        else:
            birth = True
    except ValueError:
        birth = False
        birth_miss = True

    try:
        death_yr = int(ds["died"])
        if death_yr > 1955:
            death = False
        else:
            death = True
    except ValueError:
        death = False
        death_miss = True

    if ((death and birth) or 
        (death and birth_miss) or
        (death_miss and birth_yr < 1833)
        ):
        filtered = True
    else:
        filtered = False

    return filtered

def extract_meta_data(pdf_file:str) -> dict[str,str|int]:
    """
    A function for extracting meta data from the json files
    includes:
    - author(s)
    - title
    - published
    - digitalized
    - source
    """
    try:
        #load in json
        json_file = pdf_file[:-3] + "json"
        f = open(json_file)
        data = json.load(f)
        #do stuff
        authors = find_author_json(data)
        title = find_title_json(data)
        digitalized = find_digitalization(data)
        doc_source = find_source(data)
        #close
        f.close()
    except BaseException:
        authors = "missing"
        title = "missing"
        digitalized = "missing"
        doc_source = "missing"
    return authors, title, digitalized, doc_source

def simplify_name(author:str) -> str:
    """ 
    function for simplifying repeated single author name separated by ;
    eg. "Holck, J.; af J. Holck." -> Holck, J.
    """
    simp = author
    if ";" in author:
        only_uppercase = [re.findall(r"[A-Z][a-z]*",i) for i in author.split(";")]
        if len(only_uppercase)==2:
            if sorted(only_uppercase[0]) == sorted(only_uppercase[1]):
                simp = re.findall(r"^[^;]*",author)[0]
            else:
                pass
        else:
            pass
    else:
        pass
    return simp

def separate_names(author:str) -> list[list[list[str]],int]:
    """
    function for separating different authors and their
    - separates by ";"
    - matches strings starting with uppercase letters
    """
    authors = re.findall(r"([^;]*)",author)
    authors = list(filter(None,authors))
    authors = [re.findall(r"([A-Z]\w*)",i) for i in authors] 
    n_author = len(authors)
    return authors, n_author

def check_copyright(pub_year:int,
                    cover_page_text: str,
                    filename: str,
                    ref_filenames: list[str]
                    ) -> bool:
    """
    Function for checking public domain status based on:
    - year published,
    - if the digitalising party claims it is copyright free
    - if the filename can be matched to a name from an outside source
    """
    if pub_year < 1833:
        public_domain = True
    elif ("free of copyright" in cover_page_text):
        public_domain = True
    elif filename in ref_filenames:
        public_domain = True
    else:
        public_domain = False

    return public_domain

def convert_pdf_to_dataset(file_name: str,
                           path_to_file: str,
                           demo: bool = False) -> Dataset:
    
    """Converts pdf to image and a dataset with rows by page, based on:
    https://thepythoncode.com/article/convert-pdf-files-to-images-in-python
    """
    #create path, create id
    input_file=os.path.join(path_to_file,file_name)
    #whitespaces to underscores, remove: punctuation, alma, pdf 
    doc_id = re.sub(" ","_",file_name)
    doc_id = ''.join(filter(lambda x: x not in string.punctuation, doc_id))
    doc_id = re.sub(r"alma|pdf","",doc_id)
    #get metadata
    pub_year = file_name[:4]
    #get metadata (from json)
    author, title, digitalized, doc_source = extract_meta_data(input_file)
    # Open the document
    pdfIn = fitz.open(input_file)
    data_list = []
    # Iterate throughout the pages, set range for full doc or demo test runs
    if demo:
        page_range = 1
    else:
        page_range = pdfIn.page_count

    for pg in range(page_range):
        page = pdfIn[pg]
        #get page text
        page_text = page.get_text()
        #meta data from frontpage if still missing
        if pg == 0:
            #remove \n for easier regexing
            try:
                text_solid = re.sub("\n","",page_text)
            except TypeError:
                #if no text on frontpage
                text_solid = "missing"

            if author == "missing":
                try:
                    author = re.search(r"(?<=Author\(s\):)(.*?)(?=Titel)",text_solid)[0]
                    #trying to clean it a bit
                    author = simplify_name(author)
                    #author, n_author = separate_names(author)
                except TypeError:
                    #in case no cover page
                    author = "missing"
                finally:
                    #in case cover page present, but still no author
                    if len(author) == 0:
                       author = "missing"
                    else:
                        pass 

            if title == "missing":
                try:
                    title = re.search(r"(?<=Title:)(.*?)(?=Udgivet)",text_solid)[0]
                except TypeError:
                    title = "missing"
            #now that all possible meta data is gathered after first page, see copyright status
            copyright_free = check_copyright(int(pub_year),
                                            text_solid,
                                            file_name,
                                            ref_pd_list)       
  
        else:
            #on other pages
            pass

        if not copyright_free:
            #if public domain was not confirmed, end looking through pages
            break

        #create page_image
        rotate = int(0)
        # 2, 2 (text should be readable)
        zoom_x = 2
        zoom_y = 2
        # Pre-rotate is to rotate if needed.
        mat = fitz.Matrix(zoom_x, zoom_y).prerotate(rotate)
        pix = page.get_pixmap(matrix=mat, alpha=False)
        page_img = pix.pil_image()
        page_id = f"{doc_id}_p{pg+1}"
        #assemble data_doc
        if type(author) == list:
            author = "; ".join(author)
        else:
            pass
            
        meta_data ={"doc_id" : doc_id,
                    "page_id" : page_id,
                    "page_image" : page_img,
                    "page_text": page_text,
                    "author": author,
                    "title" : title,
                    "published": pub_year,
                    "digitalized": digitalized,
                    "source": doc_source,
                    "file_name": file_name}
        data_list.append(meta_data)

    pdfIn.close()

    if copyright_free:
        ds = Dataset.from_list(data_list)
    else:
        ds = "missing"
    return ds

def make_year_list(start_year: int, stop_year: int) -> list[str]:
    """make a list of file names based on years"""
    year_list = list(range(start_year, stop_year + 1))
    year_list = [str(i) for i in year_list]
    return year_list

#source filter for ADL (they are not scanned pdfs)
adl_filter = lambda ds: ds["source"] != "ADLFBI"

def split(a, n):
    "splits list into n roughly equal parts"
    k, m = divmod(len(a), n)
    return (a[i*k+min(i, m):(i+1)*k+min(i+1, m)] for i in range(n))

def remove(path):
    """ param <path> could either be relative or absolute. """
    if os.path.isfile(path) or os.path.islink(path):
        os.remove(path)  # remove the file
    elif os.path.isdir(path):
        shutil.rmtree(path)  # remove dir and all contains
    else:
        raise ValueError("file {} is not a file or dir.".format(path))
    
def reorganize_data(output_path: str, shard_size: str = "5"):
    """ Loads the temporary data folders in the data path and creates 5GB shards for each year, deletes temporary files
    """
    folders = os.listdir(output_path)
    temp_folders = [i for i in folders if "_t" in i]
    if len(temp_folders) == 0:
        print("DATA ORGANIZED")
        return
    print("REORGANIZING DATA...")
    for t_fold in tqdm(temp_folders):
        #load all separate parquets into 1 ds
        data_path = os.path.join(output_path,t_fold)
        data_set = load_dataset(data_path, split = "train")
        #save it to appropriately size chunks
        year_str = t_fold[:-2]
        new_data_path = os.path.join(output_path,year_str)
        data_set.save_to_disk(new_data_path, max_shard_size="5GB")
        #delete temp_folder
        try :
            remove(data_path)
        except PermissionError as e:
            print(f"{e}")
    

def main():
    sub_folders = os.listdir(input_path)
    for u in unwanted_folders:
        sub_folders.remove(u)
    #select years to process
    year_list = make_year_list(start_year,stop_year)
    sub_folders = sorted([i for i in sub_folders if i in year_list])
    #chunking because there's a lot of data
    chunks = [sub_folders[i:i + n_chunks] for i in range(0, len(sub_folders), n_chunks)]

    logger.info(f"Extracting from PDFs...{sub_folders[0]}-{sub_folders[-1]}")
    for ch in tqdm(chunks):
        problem_list =[]
        for sfolder in ch:
            #sub folder path e.g /raw/1750
            sfp = os.path.join(input_path,sfolder)
            files = [i for i in os.listdir(sfp) if ".pdf" in i]
            #further chunking because even 1 year is too much memory-wise
            #batched_files = list(split(files,10))
            #limit files in 1 parquet
            batched_files = [files[i:i + n_batch] for i in range(0, len(files), n_batch)]
            for batch_nr, batch in enumerate(batched_files):
                ds=[]
                for i in batch:
                    try:  
                        temporary_ds = convert_pdf_to_dataset(i,sfp,demo)
                        if temporary_ds is None:
                            pass
                        else:
                            print(temporary_ds[0]["file_name"])
                            for j in temporary_ds:
                                ds.append(j)

                    except BaseException as e:
                        logger.info(f"FILE ERROR: {os.path.join(sfp,i)}")
                        logger.info(f"ERROR: {e}")
                        problem_list.append(i)

                logger.info(f"Assembling Dataset: {ch[0]}-{ch[-1]}, BATCH:{batch_nr}")
                #if no viable data was saved, do not make a parquet
                if len(ds) == 0:
                    continue
                ds = Dataset.from_list(ds)
                #filter out certain files
                ds = ds.filter(adl_filter)
                ds = ds.remove_columns("source")
                
                #make subfolders by year _t for temporary, will be reorganized
                save_path = os.path.join(output_path,f"{sfolder}_t",f"{source}_{ch[0]}-{ch[-1]}_{batch_nr}.parquet")
                ds.to_parquet(save_path)

            logger.info(f"FOLDER DONE: {sfolder}")
 

            if len(problem_list) >= 1: 
                with open(os.path.join(logs,f"problems_{ch[0]}-{ch[-1]}.txt"), 'w') as outfile:
                    outfile.write('\n'.join(str(i) for i in problem_list))
            else:
                pass
    #reorganize the data after running everything
    ds = None
    temporary_ds = None
    del ds
    del temporary_ds
    reorganize_data(output_path)
    

if __name__ == "__main__":
    log_path = os.path.join(logs,"extract.log")
    logging.basicConfig(
        level=logging.INFO,
        format="%(asctime)s - %(levelname)s - %(message)s",
        handlers=[
            logging.StreamHandler(),
            logging.FileHandler(log_path),
        ],
    )
    main()