1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183# === Standard library imports === import os import sys import gc import logging import contextlib from io import BytesIO # === Third-party imports === import pandas as pd import requests from tqdm import tqdm from PyPDF2 import PdfReader from pdfminer.high_level import extract_text logging.basicConfig(level=logging.INFO, format='%(message)s') # Silence pdfminer logging to CRITICAL for noisy_logger in ["pdfminer", "pdfminer.layout", "pdfminer.pdfinterp"]: logging.getLogger(noisy_logger).setLevel(logging.CRITICAL) # Initialize output CSV with headers output_headers = [ 'Link Type', 'Location Type', 'Title', 'Link', 'URL', 'PDF Size (bytes)', 'Page Count', 'Text-based', 'Tagged', 'Notes' ] pd.DataFrame(columns=output_headers).to_csv('output.csv', index=False) # Load input CSV df = pd.read_csv('input.csv') logging.info("Starting PDF accessibility analysis...") results_batch = [] BATCH_SIZE = 100 for i, url in enumerate(tqdm(df['Link'], desc="Processing PDFs", unit="file")): logging.info(f"\nProcessing: {url}") link_type = str(df.iloc[i]['Link Type']).strip().lower() if link_type == 'box': row = df.iloc[i].to_dict() row.update({ 'PDF Size (bytes)': None, 'Page Count': None, 'Text-based': None, 'Tagged': None, 'Notes': 'Skipped: Box link' }) filtered_row = {key: row.get(key, None) for key in output_headers} results_batch.append(filtered_row) if len(results_batch) >= BATCH_SIZE: pd.DataFrame(results_batch).to_csv('output.csv', mode='a', header=False, index=False) results_batch = [] gc.collect() logging.info("→ Skipped: Box link") continue if not url.lower().endswith('.pdf'): row = df.iloc[i].to_dict() row.update({ 'PDF Size (bytes)': None, 'Page Count': None, 'Text-based': None, 'Tagged': None, 'Notes': 'Skipped: Not a PDF link' }) filtered_row = {key: row.get(key, None) for key in output_headers} results_batch.append(filtered_row) if len(results_batch) >= BATCH_SIZE: pd.DataFrame(results_batch).to_csv('output.csv', mode='a', header=False, index=False) results_batch = [] gc.collect() continue try: response = requests.get(url, timeout=15) response.raise_for_status() if 'application/pdf' not in response.headers.get('Content-Type', ''): raise ValueError("Not a PDF based on Content-Type") except Exception as e: logging.warning(f"→ Failed to download PDF: {e}") row = df.iloc[i].to_dict() row.update({ 'PDF Size (bytes)': None, 'Page Count': None, 'Text-based': None, 'Tagged': None, 'Notes': f"Download failed: {e}" }) filtered_row = {key: row.get(key, None) for key in output_headers} results_batch.append(filtered_row) if len(results_batch) >= BATCH_SIZE: pd.DataFrame(results_batch).to_csv('output.csv', mode='a', header=False, index=False) results_batch = [] gc.collect() continue # Default values size = None pages = None is_text_based = None is_tagged = None notes = [] # Size size = len(response.content) # Page Count try: reader = PdfReader(BytesIO(response.content)) pages = len(reader.pages ) except Exception as e: if "invalid float value" in str(e).lower(): logging.warning("→ PDF parsing issue: invalid float in color setting (non-fatal).") else: logging.warning(f"→ Failed to read page count: {e}") notes.append("Failed to read page count") pages = None # Text-based check try: text = extract_text(BytesIO(response.content)) is_text_based = bool(text.strip()) except Exception as e: logging.warning(f"→ Failed to extract text: {e}") notes.append("Failed to extract text") # Tag detection using pdfminer3 only try: from pdfminer3.pdfinterp import PDFResourceManager, PDFPageInterpreter from pdfminer3.pdfdevice import TagExtractor from pdfminer3.pdfpage import PDFPage rsrcmgr = PDFResourceManager() retstr = BytesIO() try: device = TagExtractor(rsrcmgr, retstr, codec='utf-8') except: device = TagExtractor(rsrcmgr, retstr, codec='ascii') interpreter = PDFPageInterpreter(rsrcmgr, device) maxpages = 1 pagenos = set() import contextlib import os import sys for page in PDFPage.get_pages(BytesIO(response.content), pagenos, maxpages=maxpages, caching=True, check_extractable=True): with contextlib.redirect_stdout(open(os.devnull, 'w')), contextlib.redirect_stderr(open(os.devnull, 'w')): interpreter.process_page(page) contents = retstr.getvalue().decode() # Acrobat tag indicators tag_indicators = ["<b'Part'", "</b'Sect'", "</b'Art'", "<b'Content'", "<b'Artifact'"] if any(tag in contents for tag in tag_indicators): is_tagged = True notes.append("Tags detected via pdfminer3") else: is_tagged = False notes.append("No tags detected via pdfminer3") except Exception as e: is_tagged = None notes.append(f"pdfminer3 tag check failed: {e}") row = df.iloc[i].to_dict() row.update({ 'PDF Size (bytes)': size, 'Page Count': pages, 'Text-based': is_text_based, 'Tagged': is_tagged, 'Notes': "; ".join(notes) }) # Filter row to only include output_headers keys in correct order filtered_row = {key: row.get(key, None) for key in output_headers} results_batch.append(filtered_row) if len(results_batch) >= BATCH_SIZE: pd.DataFrame(results_batch).to_csv('output.csv', mode='a', header=False, index=False) results_batch = [] gc.collect() if results_batch: pd.DataFrame(results_batch).to_csv('output.csv', mode='a', header=False, index=False) logging.info("\nAnalysis complete. Results saved to 'output.csv'.")