129 lines
8.6 KiB
Python
129 lines
8.6 KiB
Python
import os
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from datetime import datetime
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import csv
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import hashlib
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import pandas as pd
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from functools import partial
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from utils.settings import CSV_DIR
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def load_excluded_filenames(submissions_dir_name: str) -> list[str]: # helper function for hashing all files
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csv_file_path = os.path.join(CSV_DIR, f'{submissions_dir_name}_excluded.csv')
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if not os.path.exists(csv_file_path): # if csv file with excluded file names for submission does not exist
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print(f'[WARNING] Cannot find CSV file with list of excluded file names: {csv_file_path}\n[INFO] All files will be hashed & inspected')
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return [] # return empty list to continue without any excluded file names
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else: # if csv file with excluded file names for submission exists
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try:
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df = pd.read_csv(csv_file_path)
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filename_list = df['exclude_filename'].tolist() # get the values of the 'filename' column as a list
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filename_list = [ f.lower() for f in filename_list ] # convert to lowercase for comparison with submission files
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print(f'[INFO] Using CSV file with list of excluded file names: {csv_file_path}')
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return filename_list
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except Exception as e: # any exception, print error and return empty list to continue without any excluded file names
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print(f'[WARNING] Unable to load / read CSV file with list of excluded file names: {csv_file_path}\n[INFO] All files will be hashed & inspected')
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print(f'[INFO] Error message: {e}')
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return []
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def get_hashes_in_dir(dir_path: str, excluded_filenames: list = []) -> list: # helper function for hashing all files
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hash_list = []
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for subdir, dirs, files in os.walk(dir_path): # loop through all files in the directory and generate hashes
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for filename in files:
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if filename.lower() not in excluded_filenames: # convert to lowercase for comparison with excluded files & do not hash if in the excluded list
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filepath = os.path.join(subdir, filename)
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with open(filepath, 'rb') as f:
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filehash = hashlib.sha256(f.read()).hexdigest()
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if filehash != 'e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855': # do not include hashes of empty files
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hash_list.append({ 'filepath': filepath, 'filename': filename, 'sha256 hash': filehash})
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return hash_list
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def generate_hashes_gradebook(gradebook_dir_path: str) -> str: # main function for hashing all files in gradebook
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gradebook_dir_name = os.path.abspath(gradebook_dir_path).split(os.path.sep)[-1] # get name of gradebook by separating path and use rightmost part
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if not os.path.isdir(gradebook_dir_path):
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exit(f'Directory {gradebook_dir_path} does not exist.\nMake sure "{gradebook_dir_name}" exists in "BB_gradebooks".\n')
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dicts_with_hashes_list = get_hashes_in_dir(gradebook_dir_path)
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for hash_dict in dicts_with_hashes_list:
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student_id = hash_dict['filename'].split('_attempt_')[0].split('_')[-1]
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full_path = os.path.join(os.getcwd(), hash_dict["filepath"])
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hash_dict['filename'] = f'=HYPERLINK("{full_path}", "{hash_dict["filename"]}")'
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del hash_dict['filepath']
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hash_dict.update({'Student ID': student_id})
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os.makedirs(CSV_DIR, exist_ok=True)
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csv_file_name = f'{gradebook_dir_name}_gradebook_file_hashes_{datetime.now().strftime("%Y%m%d-%H%M%S")}.csv'
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csv_file_path = os.path.join(CSV_DIR, csv_file_name)
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with open(csv_file_path, 'w', newline='') as csvfile: # open the output CSV file for writing
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fieldnames = ['Student ID', 'filename', 'sha256 hash']
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writer = csv.DictWriter(csvfile, fieldnames=fieldnames)
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writer.writeheader()
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writer.writerows(dicts_with_hashes_list)
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print(f'[INFO] Created CSV file with all files & hashes in gradebook: {gradebook_dir_name}\nCSV file: {csv_file_path}')
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return csv_file_path
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def generate_hashes_submissions(submissions_dir_path: str) -> str: # main function for hashing all files in submissions
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submissions_dir_name = os.path.abspath(submissions_dir_path).split(os.path.sep)[-1] # get name of submission/assignment by separating path and use rightmost part
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if not os.path.isdir(submissions_dir_path):
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exit(f'Directory {submissions_dir_path} does not exist.\nMake sure "{submissions_dir_name}" exists in "BB_submissions".\n')
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excluded_filenames = load_excluded_filenames(submissions_dir_name)
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dicts_with_hashes_list = []
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for student_dir_name in os.listdir(submissions_dir_path): # loop through each student dir to get hashes for all files per student
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student_dir_path = os.path.join(submissions_dir_path, student_dir_name)
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student_dicts_with_hashes_list = get_hashes_in_dir(student_dir_path, excluded_filenames) # dict with hashes for all student files - except for 'excluded' file names
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student_dicts_list = []
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for hash_dict in student_dicts_with_hashes_list:
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hash_dict.update({'Student ID': student_dir_name}) # update hash records with student id
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full_path = os.path.join(os.getcwd(), hash_dict["filepath"])
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hash_dict['filepath'] = f'=HYPERLINK("{full_path}", "{hash_dict["filepath"]}")'
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hash_dict['filename'] = f'=HYPERLINK("{full_path}", "{hash_dict["filename"]}")'
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student_dicts_list.append(hash_dict) # append file dict to student list of dict for csv export
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dicts_with_hashes_list.append(student_dicts_list) # append student hashes to main list with all submissions
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os.makedirs(CSV_DIR, exist_ok=True)
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csv_file_name = f'{submissions_dir_name}_submissions_file_hashes_{datetime.now().strftime("%Y%m%d-%H%M%S")}.csv'
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csv_file_path = os.path.join(CSV_DIR, csv_file_name)
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with open(csv_file_path, 'w', newline='') as csvfile: # open the output CSV file for writing
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fieldnames = ['Student ID', 'filepath', 'filename', 'sha256 hash']
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writer = csv.DictWriter(csvfile, fieldnames=fieldnames)
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writer.writeheader()
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for student_dict in dicts_with_hashes_list:
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writer.writerows(student_dict)
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print(f'[INFO] Created CSV file with all files & hashes for submissions in: {submissions_dir_name}\nCSV file: {csv_file_path}')
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return csv_file_path
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def generate_duplicate_hashes_generic(hashes_csv_file_path: str, drop_columns: list[str]):
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csv = pd.read_csv(hashes_csv_file_path)
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df = pd.DataFrame(csv) # df with all files and their hashes
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df_clean = df.drop(columns=drop_columns) # clear not needed columns
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duplicate_hash = df_clean.loc[df_clean.duplicated(subset=['sha256 hash'], keep=False), :] # all files with duplicate hash - incl. files from the same student id
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# agg() for 'Student ID' True if more than 1 in groupby (= files with the same hash by multiple student ids)
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# False if unique (= files from the same student id with the same hash)
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hash_with_multiple_student_ids = duplicate_hash.groupby('sha256 hash').agg(lambda x: len(x.unique())>1)
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# list with duplicate hashes - only if different student id (doesn't include files from same student id)
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duplicate_hashes_list = hash_with_multiple_student_ids[hash_with_multiple_student_ids['Student ID']==True].index.to_list()
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files_with_duplicate_hash = df[df['sha256 hash'].isin(duplicate_hashes_list)] # df with all files with duplicate hash, excludes files from the same student id
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df_duplicate = files_with_duplicate_hash.sort_values(['sha256 hash', 'Student ID']) # sort before output to csv
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gradebook_or_submissions_str = os.path.basename(hashes_csv_file_path).split('_file_hashes_')[0].split('_')[-1] # 'gradebook' or 'submissions' depending on which files hashes csv is read
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assignment_name = os.path.basename(hashes_csv_file_path).split(f'_{gradebook_or_submissions_str}_')[0]
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csv_out = hashes_csv_file_path.rsplit('_', 1)[0].replace('file_hashes', 'duplicate_') + datetime.now().strftime("%Y%m%d-%H%M%S") + '.csv'
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try:
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df_duplicate.to_csv(csv_out, index=False)
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print(f'[INFO] Created CSV file with duplicate hashes in {gradebook_or_submissions_str}: {assignment_name}\nCSV file: {csv_out}')
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except Exception as e:
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exit(f'[ERROR] Something went wrong while trying to save csv file with duplicate hashes\nError message: {e}')
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# partials for generate_duplicate_hashes_generic(), setting the appropriate drop_columns for gradebook / submissions
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generate_duplicate_hashes_gradebook = partial(generate_duplicate_hashes_generic, drop_columns=['filename'])
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generate_duplicate_hashes_submissions = partial(generate_duplicate_hashes_generic, drop_columns=['filepath', 'filename'])
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