Datasets:
File size: 6,252 Bytes
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"""Pdf-Data 1.ipynb
Automatically generated by Colab.
Original file is located at
https://colab.research.google.com/drive/1IB0DbFJbA27C0womZkoMQZ7oIkgJkHYU
# Install & import libs
"""
!pip install pypdf pandas tqdm
!apt-get install -y tesseract-ocr
!pip install pytesseract pdf2image pypdf pandas tqdm pillow
!apt-get install -y tesseract-ocr-ara
!apt-get install -y poppler-utils
import os
import pandas as pd
from pypdf import PdfReader
from tqdm import tqdm
"""# Mount Google Drive"""
from google.colab import drive
drive.mount('/content/drive')
"""# Config paths"""
BASE_FOLDER = "/content/drive/MyDrive/OitLab/Text"
OUTPUT_CSV = "/content/drive/MyDrive/OitLab/Text/pdf_dataset1.csv"
"""# Core extraction logic"""
# import os
# from pypdf import PdfReader
# from pdf2image import convert_from_path
# import pytesseract
# from tqdm.notebook import tqdm
# import re
# rows = []
# def clean_text(text):
# text = re.sub(r'\s+', ' ', text)
# return text.strip()
# for category in os.listdir(BASE_FOLDER):
# category_path = os.path.join(BASE_FOLDER, category)
# if not os.path.isdir(category_path):
# continue
# print(f"\nProcessing category: {category}")
# files = [f for f in os.listdir(category_path) if f.lower().endswith(".pdf")]
# for file in tqdm(files, desc="PDF files"):
# pdf_path = os.path.join(category_path, file)
# try:
# reader = PdfReader(pdf_path)
# total_pages = len(reader.pages)
# for page_num, page in enumerate(
# tqdm(reader.pages, desc=f"{file}", total=total_pages, leave=False)
# ):
# text = page.extract_text()
# text = "" if text is None else clean_text(text)
# # --------- OCR FALLBACK ----------
# if len(text) < 30:
# images = convert_from_path(
# pdf_path,
# first_page=page_num + 1,
# last_page=page_num + 1
# )
# ocr_text = pytesseract.image_to_string(
# images[0],
# lang="ara+eng"
# )
# text = clean_text(ocr_text)
# rows.append({
# "name": file,
# "page": page_num + 1,
# "content": text,
# "category": category,
# "char": len(text)
# })
# except Exception as e:
# print(f"Error with {pdf_path}: {e}")
import os
import pandas as pd
from pypdf import PdfReader
from pdf2image import convert_from_path
import pytesseract
from tqdm import tqdm
import re
from multiprocessing import Pool, cpu_count
from functools import partial
# ---------------- HELPERS ----------------
def clean_text(text):
text = re.sub(r'\s+', ' ', text)
return text.strip()
# ---------------- CONFIG ----------------
BASE_FOLDER = "/content/drive/MyDrive/OitLab/Text"
OUTPUT_CSV = "/content/drive/MyDrive/OitLab/Text/pdf_dataset1.csv"
PREFERRED_CATEGORIES = ["Historique","Religion","Muslim"]
N_WORKERS = max(1, cpu_count() - 1)
print(f"N_WORKERS: {N_WORKERS}")
# ---------------- LOAD EXISTING CSV ----------------
if os.path.exists(OUTPUT_CSV):
df_existing = pd.read_csv(OUTPUT_CSV)
else:
df_existing = pd.DataFrame(columns=["name","page","content","category","char"])
processed_set = set(zip(df_existing['category'], df_existing['name']))
# ---------------- PDF PROCESSOR ----------------
def process_pdf(task):
category, pdf_path, file_name = task
pdf_rows = []
try:
reader = PdfReader(pdf_path)
total_pages = len(reader.pages)
for page_num, page in enumerate(reader.pages):
text = page.extract_text()
text = "" if text is None else clean_text(text)
# OCR fallback
if len(text) < 30:
images = convert_from_path(
pdf_path,
first_page=page_num + 1,
last_page=page_num + 1
)
ocr_text = pytesseract.image_to_string(
images[0],
lang="ara+eng"
)
text = clean_text(ocr_text)
pdf_rows.append({
"name": file_name,
"page": page_num + 1,
"content": text,
"category": category,
"char": len(text)
})
except Exception as e:
print(f"Error processing {pdf_path}: {e}")
return pdf_rows
# ---------------- BUILD TASK LIST ----------------
all_categories = [f for f in os.listdir(BASE_FOLDER)
if os.path.isdir(os.path.join(BASE_FOLDER, f))]
sorted_categories = []
for p_cat in PREFERRED_CATEGORIES:
if p_cat in all_categories:
sorted_categories.append(p_cat)
all_categories.remove(p_cat)
sorted_categories.extend(all_categories)
tasks = []
for category in sorted_categories:
category_path = os.path.join(BASE_FOLDER, category)
files_in_category = [f for f in os.listdir(category_path)
if f.lower().endswith(".pdf")]
for file_name in files_in_category:
if (category, file_name) in processed_set:
continue
pdf_path = os.path.join(category_path, file_name)
tasks.append((category, pdf_path, file_name))
print(f"Total PDFs to process: {len(tasks)}")
print(f"Using {N_WORKERS} workers")
# ---------------- MULTIPROCESSING ----------------
all_rows = []
with Pool(N_WORKERS) as pool:
for pdf_rows in tqdm(pool.imap_unordered(process_pdf, tasks),
total=len(tasks)):
if pdf_rows:
all_rows.extend(pdf_rows)
# incremental save (safe: only main process writes)
df_temp = pd.DataFrame(pdf_rows)
write_header = not os.path.exists(OUTPUT_CSV) or df_existing.empty
df_temp.to_csv(
OUTPUT_CSV,
mode='a',
header=write_header,
index=False
)
print("Processing complete!") |