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185# === 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
from boxsdk import OAuth2, Client
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)
oauth = OAuth2(
client_id='97mcp2od8tlluiu7skbo6coxzkir178z',
client_secret='YOUR_CLIENT_SECRET',
access_token='9R5TneoJhBnDIuYcoJ7CXlkpJoiNx47P'
)
box_client = Client(oauth)
# 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':
from boxsdk.exception import BoxAPIException
try:
shared_link_url = url
box_file = box_client.get_shared_item(shared_link_url)
if box_file.type != 'file':
raise ValueError("Box item is not a file")
if not box_file.name.lower().endswith('.pdf'):
raise ValueError("Box file is not a PDF")
pdf_stream = BytesIO()
box_file.download_to(pdf_stream)
pdf_stream.seek(0)
pdf_data = pdf_stream.read()
except Exception as e:
row = df.iloc[i].to_dict()
row.update({
'PDF Size (bytes)': None,
'Page Count': None,
'Text-based': None,
'Tagged': None,
'Notes': f'Skipped: Box access 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
if link_type == 'box':
response_content = pdf_data
else:
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")
response_content = response.content
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 heuristic
try:
reader = PdfReader(BytesIO(response_content))
if "/StructTreeRoot" in reader.trailer["/Root"]:
is_tagged = True
notes.append("StructTreeRoot tag found")
else:
is_tagged = False
notes.append("No StructTreeRoot tag")
except Exception as e:
is_tagged = None
notes.append(f"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'.")