add_images.py

import pandas as pd
import gzip
import csv
import requests
from requests.adapters import HTTPAdapter, Retry
import urllib3

import urllib.parse
from io import StringIO


# NOTE: this is not a good idea; this is solely a fix for Met networks
do_verify = False

if not do_verify:
    urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning)

# Setup HTTPAdaptor & requests session to add retry pattern
s = requests.Session()

retries = Retry(total=3,
                backoff_factor=0.1,
                status_forcelist=[ 500, 502, 503, 504 ])

s.mount('https://', HTTPAdapter(max_retries=retries))

# Function to load and clean the CSV data & add images
def load_and_clean_csv(file_path):
    valid_rows = []
    invalid_rows = []

    index = 0

    # Read the gzip file line by line
    with gzip.open(file_path, 'rt', newline='\r\n', encoding='utf-8') as f:
        reader = csv.reader(f)
        header = next(reader)  # Read the header separately
        header.append("primaryImageSmall")  # Add the new column to the header
        valid_rows.append(header)
        expected_columns = len(header) - 1  # Exclude the new column
        
        for line in f:
            try:
                # Try to parse the line
                row = next(csv.reader([line]))
                index = index + 1
                # print(len(row)+":"+expected_columns)
                if len(row) == expected_columns:
                    # Fetch primaryImageSmall from the API
                    object_id = row[4]
                    image_url = fetch_primary_image_small(object_id)
                    image_url = image_url.replace(" ","%20")
                    image_url = image_url.replace(u'\u2013',"–")
                    row.append(image_url)
                    valid_rows.append(row)

                    if index % 100 == 0:
                        print("Fetched " + str(index) +" image URLs")
                else:
                    print("Invalid: "+object_id)
                    print(row)
                    invalid_rows.append(line)
            except Exception as e:
                print(e)
                print("Invalid + error: "+object_id)
                invalid_rows.append(line)

    print(f"Found {len(invalid_rows)} invalid rows")
    return valid_rows, invalid_rows

# Function to load and clean the CSV data & add images
def test_csv(file_path):
    valid_rows = []
    invalid_rows = []

    index = 0

    # Read the gzip file line by line
    with gzip.open(file_path, 'rt', newline='\r\n', encoding='utf-8') as f:
        reader = csv.reader(f)
        header = next(reader)  # Read the header separately
        valid_rows.append(header)
        expected_columns = len(header)

        for line in f:
            try:
                # Try to parse the line
                row = next(csv.reader([line]))
                index = index + 1
                if len(row) == expected_columns:
                    object_id = row[4]
                    print(object_id)
                    valid_rows.append(row)
                else:
                    print("Invalid: "+object_id)
                    print(len(row), expected_columns)
                    print(row)
                    invalid_rows.append(line)
            except Exception as e:
                print(e)
                print("Invalid + error: "+object_id)
                invalid_rows.append(line)

    print(f"Found {len(invalid_rows)} invalid rows")
    return valid_rows, invalid_rows

# Function to fetch the primaryImageSmall URL from the MET Museum API
def fetch_primary_image_small(object_id):
    url = f"https://collectionapi.metmuseum.org/public/collection/v1/objects/{object_id}"
    try:
        response = s.get(url, verify=do_verify)
        response.raise_for_status()  # Raise an error for bad status codes
        data = response.json()
        # print (data.get("primaryImageSmall", ""))
        return data.get("primaryImageSmall", "")
    except Exception as e:
        print(f"Error fetching image for object ID {object_id}: {e}")
        return ""

# Function to save the cleaned data to a new gzip CSV file
def save_cleaned_csv(valid_rows, output_path):
    with gzip.open(output_path, 'wt', newline='') as f:
        writer = csv.writer(f)
        writer.writerows(valid_rows)
    print(f"Cleaned data saved to {output_path}")

def main():
    input_file = 'metadata.csv.gz'
    output_file = 'metadata_images.csv.gz'

    # Test
    # test_csv(input_file)

    # Load and clean the data
    valid_rows, invalid_rows = load_and_clean_csv(input_file)

    # Save the cleaned data
    save_cleaned_csv(valid_rows, output_file)

if __name__ == "__main__":
    main()