import argparse import base64 import csv import datetime import json import os import random import re import sqlite3 import string import tempfile from concurrent.futures import ThreadPoolExecutor from pathlib import Path from typing import Optional import boto3 import tinyhost from tqdm import tqdm from olmocr.data.renderpdf import render_pdf_to_base64webp from olmocr.s3_utils import get_s3_bytes, parse_s3_path def parse_args(): parser = argparse.ArgumentParser(description="Scan OLMO OCR workspace results and create visual samples") parser.add_argument("workspace", help="OLMO OCR workspace path (s3://bucket/workspace)") parser.add_argument("--pages_per_output", type=int, default=30, help="Number of pages per output file") parser.add_argument("--repeats", type=int, default=1, help="Number of output files to generate") parser.add_argument("--pdf_profile", help="AWS profile for accessing PDFs") parser.add_argument("--output_dir", default="dolma_samples", help="Directory to save output HTML files") parser.add_argument("--max_workers", type=int, default=4, help="Maximum number of worker threads") parser.add_argument( "--db_path", default="~/s2pdf_url_data/d65142df-6588-4b68-a12c-d468b3761189.csv.db", help="Path to the SQLite database containing PDF hash to URL mapping", ) parser.add_argument( "--prolific_csv", default="prolific_codes.csv", help="Path to save the CSV file with Prolific codes", ) return parser.parse_args() def generate_prolific_code(length=8): """Generate a random code for Prolific.""" characters = string.ascii_uppercase + string.digits return "".join(random.choice(characters) for _ in range(length)) def obfuscate_code(code): """Gently obfuscate the Prolific code so it's not immediately visible in source.""" # Convert to base64 and reverse encoded = base64.b64encode(code.encode()).decode() return encoded[::-1] def deobfuscate_code(obfuscated_code): """Deobfuscate the code - this will be done in JavaScript.""" # Reverse and decode from base64 reversed_encoded = obfuscated_code[::-1] try: return base64.b64decode(reversed_encoded).decode() except: return "ERROR_DECODING" def parse_pdf_hash(pretty_pdf_path: str) -> Optional[str]: pattern = r"s3://ai2-s2-pdfs/([a-f0-9]{4})/([a-f0-9]+)\.pdf" match = re.match(pattern, pretty_pdf_path) if match: return match.group(1) + match.group(2) return None def get_original_url(pdf_hash: str, db_path: str) -> Optional[str]: """Look up the original URL for a PDF hash in the SQLite database.""" if not pdf_hash: return None try: sqlite_db_path = os.path.expanduser(db_path) if not os.path.exists(sqlite_db_path): print(f"SQLite database not found at {sqlite_db_path}") return None conn = sqlite3.connect(sqlite_db_path) cursor = conn.cursor() cursor.execute("SELECT uri FROM pdf_mapping WHERE pdf_hash = ?", (pdf_hash,)) result = cursor.fetchone() conn.close() if result: return result[0] return None except Exception as e: print(f"Error looking up URL for PDF hash {pdf_hash}: {e}") return None def list_result_files(s3_client, workspace_path): """List all JSON result files in the workspace results directory.""" bucket, prefix = parse_s3_path(workspace_path) results_prefix = os.path.join(prefix, "results").rstrip("/") + "/" all_files = [] paginator = s3_client.get_paginator("list_objects_v2") for page in paginator.paginate(Bucket=bucket, Prefix=results_prefix): if "Contents" in page: all_files.extend([f"s3://{bucket}/{obj['Key']}" for obj in page["Contents"] if obj["Key"].endswith(".jsonl") or obj["Key"].endswith(".json")]) if len(all_files) > 1000: break return all_files def get_random_pages(s3_client, result_files, count=30): """Get random pages from the result files.""" random_pages = [] # Try to collect the requested number of pages attempts = 0 max_attempts = count * 3 # Allow extra attempts to handle potential failures while len(random_pages) < count and attempts < max_attempts: attempts += 1 # Pick a random result file if not result_files: print("No result files found!") break result_file = random.choice(result_files) try: # Get the content of the file content = get_s3_bytes(s3_client, result_file) lines = content.decode("utf-8").strip().split("\n") if not lines: continue # Pick a random line (which contains a complete document) line = random.choice(lines) doc = json.loads(line) # A Dolma document has "text", "metadata", and "attributes" fields if "text" not in doc or "metadata" not in doc or "attributes" not in doc: print(f"Document in {result_file} is not a valid Dolma document") continue # Get the original PDF path from metadata pdf_path = doc["metadata"].get("Source-File") if not pdf_path: continue # Get page spans from attributes page_spans = doc["attributes"].get("pdf_page_numbers", []) if not page_spans: continue # Pick a random page span page_span = random.choice(page_spans) if len(page_span) >= 3: # Page spans are [start_pos, end_pos, page_num] page_num = page_span[2] # Extract text for this page start_pos, end_pos = page_span[0], page_span[1] page_text = doc["text"][start_pos:end_pos].strip() # Include the text snippet with the page info random_pages.append((pdf_path, page_num, page_text, result_file)) if len(random_pages) >= count: break except Exception as e: print(f"Error processing {result_file}: {e}") continue print(f"Found {len(random_pages)} random pages from Dolma documents") return random_pages def create_presigned_url(s3_client, pdf_path, expiration=3600 * 24 * 7): """Create a presigned URL for the given S3 path.""" try: bucket, key = parse_s3_path(pdf_path) url = s3_client.generate_presigned_url("get_object", Params={"Bucket": bucket, "Key": key}, ExpiresIn=expiration) return url except Exception as e: print(f"Error creating presigned URL for {pdf_path}: {e}") return None def create_html_output(random_pages, pdf_s3_client, output_path, workspace_path, db_path, prolific_code, resolution=2048): """Create an HTML file with rendered PDF pages.""" # Obfuscate the provided Prolific code obfuscated_code = obfuscate_code(prolific_code) # Get current date and time for the report current_time = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S") html_content = f""" OLMO OCR Samples

Instructions: Please review each document below and mark if it contains PII (Personally identifiable information). If you cannot read it (ex. the document is not in English, or is otherwise unreadable), mark it as such. If the document contains disturbing or graphic content, please mark that. Finally, if there is PII, type in a brief description and press Enter. Once you mark all 30 documents, the completetion code will be presented.

PII Direct Identifiers

Information that identifies a data subject without further context

  • Names: Full names, first names, last names, nicknames, maiden names, birth names, aliases
  • Addresses: Street addresses, postal codes, city, state, country
  • Contact Information: Phone numbers, email addresses
  • Government IDs: Social Security Numbers (SSNs), passport numbers, driver's license numbers, tax identification numbers
  • Financial Information: Credit card numbers, bank account numbers, routing numbers
  • Biometric Data: Fingerprints, retina scans, voice signatures, facial recognition data
  • Date of Birth of data subject
  • Place of Birth of data subject
  • Gender of data subject
  • Race of data subject
  • Religion of data subject

PII Indirect Identifiers

Information that can be used to identify a data subject in context or in combination with other information

  • IP Addresses
  • Login IDs
  • Geolocations
  • Employment Information
  • Education Information
  • Medical Information
  • Usernames
  • Passwords
  • Keys
  • URLs
  • Company Names

Generated On

{current_time}

Workspace

{workspace_path}

Sample Size

{len(random_pages)} pages

""" for i, (pdf_path, page_num, page_text, result_file) in enumerate(tqdm(random_pages, desc="Rendering pages")): # Get original URL from PDF hash pdf_hash = parse_pdf_hash(pdf_path) original_url = get_original_url(pdf_hash, db_path) if pdf_hash else None # Create a truncated path for display display_path = pdf_path if len(display_path) > 60: display_path = "..." + display_path[-57:] # Generate presigned URL presigned_url = create_presigned_url(pdf_s3_client, pdf_path) try: # Download PDF to temp file bucket, key = parse_s3_path(pdf_path) with tempfile.NamedTemporaryFile(suffix=".pdf", delete=False) as temp_file: pdf_data = pdf_s3_client.get_object(Bucket=bucket, Key=key)["Body"].read() temp_file.write(pdf_data) temp_file_path = temp_file.name # Render PDF to base64 webp base64_image = render_pdf_to_base64webp(temp_file_path, page_num, resolution) # Add CSS class for the first annotation interface to be active by default active_class = " active" if i == 0 else "" # Add to HTML with the annotation interface html_content += f"""

{original_url}

Page {page_num}

{f'View Cached PDF' if presigned_url else pdf_path}

Status: Pending

PDF Page {page_num}
""" # Clean up temp file os.unlink(temp_file_path) except Exception as e: # Add CSS class for the first annotation interface to be active by default active_class = " active" if i == 0 else "" html_content += f"""

{original_url}

Page {page_num}

{f'View Cached PDF' if presigned_url else pdf_path}

Status: Pending

Error: {str(e)}
""" html_content += ( """
Thank you! All annotations are complete.
Your Prolific completion code is: Loading...
Annotation Progress: 1/
""" ) with open(output_path, "w") as f: f.write(html_content) print(f"Created HTML output at {output_path}") def generate_sample_set(args, i, s3_client, pdf_s3_client, result_files): """Generate a single sample set.""" output_filename = Path(args.output_dir) / f"dolma_samples_{i+1}.html" print(f"\nGenerating sample set {i+1} of {args.repeats}") # Get random pages random_pages = get_random_pages(s3_client, result_files, args.pages_per_output) # Generate a unique Prolific code for this sample set prolific_code = generate_prolific_code() # Create HTML output with the Prolific code create_html_output(random_pages, pdf_s3_client, output_filename, args.workspace, args.db_path, prolific_code) return output_filename, prolific_code def main(): args = parse_args() # Set up S3 clients s3_client = boto3.client("s3") # Set up PDF S3 client with profile if specified if args.pdf_profile: pdf_session = boto3.Session(profile_name=args.pdf_profile) pdf_s3_client = pdf_session.client("s3") else: pdf_s3_client = s3_client # Create output directory output_dir = Path(args.output_dir) output_dir.mkdir(exist_ok=True, parents=True) # List all result files print(f"Listing result files in {args.workspace}/results...") result_files = list_result_files(s3_client, args.workspace) print(f"Found {len(result_files)} result files") # Use ThreadPoolExecutor to parallelize the generation of sample sets output_files = [] prolific_codes = [] if args.repeats > 1: print(f"Using ThreadPoolExecutor with {min(args.max_workers, args.repeats)} workers") with ThreadPoolExecutor(max_workers=min(args.max_workers, args.repeats)) as executor: futures = [] for i in range(args.repeats): future = executor.submit(generate_sample_set, args, i, s3_client, pdf_s3_client, result_files) futures.append(future) # Wait for all futures to complete and collect results for future in futures: try: output_filename, prolific_code = future.result() output_files.append(output_filename) prolific_codes.append(prolific_code) print(f"Completed generation of {output_filename} with code {prolific_code}") except Exception as e: print(f"Error generating sample set: {e}") else: # If only one repeat, just run it directly output_filename, prolific_code = generate_sample_set(args, 0, s3_client, pdf_s3_client, result_files) output_files.append(output_filename) prolific_codes.append(prolific_code) # Now upload each resulting file into tinyhost print("Generated all files, uploading tinyhost links now") links = [] for output_filename in output_files: link = tinyhost.tinyhost([str(output_filename)])[0] links.append(link) print(link) # Create CSV file with tinyhost links and Prolific codes csv_path = args.prolific_csv print(f"Writing Prolific codes to {csv_path}") with open(csv_path, "w", newline="") as csvfile: writer = csv.writer(csvfile) writer.writerow(["tinyhost_link", "code"]) for link, code in zip(links, prolific_codes): writer.writerow([link, code]) print(f"Prolific codes written to {csv_path}") if __name__ == "__main__": main()