Create script.py

script.py

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+# -*- coding: utf-8 -*-
+"""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!")