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343# === Standard library imports ===
import os
import gc
import logging
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
from boxsdk.exception import BoxAPIException
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', 'Equalify Scan Results'
]
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
equalify_batch = []
equalify_url_to_index = {}
for i, url in enumerate(tqdm(df['Link'], desc="Processing PDFs", unit="file")):
logging.info(f"\nProcessing: {url}")
row = df.iloc[i].to_dict()
# === Determine test requirements flags ===
pdf_size_raw = row.get('PDF Size (bytes)', '')
page_count_raw = row.get('Page Count', '')
text_based_raw = row.get('Text-based', '')
tagged_raw = row.get('Tagged', '')
equalify_scan_raw = row.get('Equalify Scan Results', '')
pdf_size_val = str(pdf_size_raw).strip().upper()
page_count_val = str(page_count_raw).strip().upper()
text_based_val = str(text_based_raw).strip().upper()
tagged_val = "TRUE" if str(tagged_raw).strip().upper() == "TRUE" else "FALSE"
equalify_scan_val = str(equalify_scan_raw).strip().upper() if pd.notna(equalify_scan_raw) else ""
needs_size = pdf_size_val in ["", "FAILED"]
needs_pages = page_count_val in ["", "FAILED"]
needs_text = text_based_val in ["", "FAILED"]
needs_tagged = tagged_val in ["", "FAILED"]
# Move link_type detection before needs_equalify
link_type = str(row.get('Link Type', '')).strip().lower()
# Immediately after defining link_type, adjust needs_equalify
needs_equalify = (
tagged_val == "TRUE"
and equalify_scan_val in ["", "FAILED"]
)
if link_type == 'box':
needs_equalify = False
needs_anything = any([needs_size, needs_pages, needs_text, needs_tagged, needs_equalify])
# Unified skip for both Box and PDF links if all tests previously failed and not tagged and not needing Equalify
if all(val == "FAILED" for val in [pdf_size_val, page_count_val, text_based_val, tagged_val]) and not needs_equalify:
row.update({
'Notes': 'Skipped: All tests previously failed, skipping reprocessing'
})
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
# Skip PDF download if only Equalify scan is required (no local tests needed)
if needs_equalify and not any([needs_size, needs_pages, needs_text, needs_tagged]):
equalify_batch.append({ "url": url })
equalify_url_to_index[url] = len(results_batch)
row.update({
'PDF Size (bytes)': row.get('PDF Size (bytes)'),
'Page Count': row.get('Page Count'),
'Text-based': row.get('Text-based'),
'Tagged': row.get('Tagged'),
'Notes': "Skipped: Only Equalify scan required",
'Equalify Scan Results': None
})
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 not needs_anything:
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
# Removed redundant Box skip condition that relied on needs_anything and needs_equalify
# === Retrieve PDF content ===
if link_type == 'box':
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()
response_content = pdf_data
except Exception as e:
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
else:
if not url.lower().endswith('.pdf'):
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.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
# === Initialize default values ===
size = None
pages = None
is_text_based = None
is_tagged = None
notes = []
equalify_scan_results = None
# === PDF Size (bytes) ===
if needs_size:
size = len(response_content)
else:
size = row.get('PDF Size (bytes)')
# === Page Count ===
if needs_pages:
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 = "FAILED"
else:
pages = row.get('Page Count')
# === Text-based check ===
if needs_text:
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")
is_text_based = "FAILED"
else:
is_text_based = row.get('Text-based')
# === Tag detection heuristic ===
if needs_tagged:
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 = "FAILED"
notes.append(f"Tag check failed: {e}")
else:
is_tagged = row.get('Tagged')
# === Prepare Equalify batch if needed and not Box link ===
if needs_equalify and link_type != 'box':
equalify_batch.append({ "url": url })
equalify_url_to_index[url] = len(results_batch) # store index in results_batch
equalify_scan_results = f"Equalify result saved to results/job_{url}.json"
else:
equalify_scan_results = row.get('Equalify Scan Results')
if needs_equalify and link_type == 'box':
equalify_scan_results = "Skipped Equalify scan: Box-hosted PDF"
row.update({
'PDF Size (bytes)': size,
'Page Count': pages,
'Text-based': is_text_based,
'Tagged': is_tagged,
'Notes': "; ".join(notes),
'Equalify Scan Results': equalify_scan_results
})
# 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()
# After processing all rows, handle Equalify batch scans
if equalify_batch:
try:
resp = requests.post(
"https://scan-dev.equalify.app/generate/urls",
json={"urls": equalify_batch, "mode": "verapdf"},
timeout=30
)
resp.raise_for_status()
resp_json = resp.json()
jobs = resp_json.get("jobs", [])
url_to_jobId = {}
for job in jobs:
if job is None:
continue
url = job.get("url") if isinstance(job, dict) else None
jobId = job.get("jobId") if isinstance(job, dict) else None
if url and jobId:
url_to_jobId[url] = jobId
import time
max_wait = 60
poll_interval = 15
for url, jobId in url_to_jobId.items():
waited = 0
while waited < max_wait:
logging.info(f"→ Polling Equalify job for {jobId} ({waited}/{max_wait} seconds elapsed)")
poll_url = f"https://scan-dev.equalify.app/results/axe/{jobId}"
try:
poll_resp = requests.get(poll_url, timeout=15)
poll_resp.raise_for_status()
poll_json = poll_resp.json()
if poll_json.get("status") == "completed":
result_obj = poll_json.get("result")
job_file_path = f"results/job_{jobId}.json"
os.makedirs("results", exist_ok=True)
with open(job_file_path, 'w') as f:
import json
json.dump(result_obj, f)
idx = equalify_url_to_index.get(url)
if idx is not None and idx < len(results_batch):
results_batch[idx]['Equalify Scan Results'] = f"Equalify result saved to {job_file_path}"
break
elif poll_json.get("status") == "error":
idx = equalify_url_to_index.get(url)
if idx is not None and idx < len(results_batch):
results_batch[idx]['Equalify Scan Results'] = f"Equalify scan error"
break
except Exception as e:
logging.warning(f"→ Polling error for jobId {jobId}: {e}")
time.sleep(poll_interval)
waited += poll_interval
else:
idx = equalify_url_to_index.get(url)
if idx is not None and idx < len(results_batch):
results_batch[idx]['Equalify Scan Results'] = "Equalify scan timed out after 180 seconds"
except Exception as e:
logging.warning(f"→ Equalify batch scan error: {e}")
for url in equalify_batch:
idx = equalify_url_to_index.get(url)
if idx is not None and idx < len(results_batch):
results_batch[idx]['Equalify Scan Results'] = f"Equalify scan error: {e}"
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'.")