56 lines
2.8 KiB
Python
56 lines
2.8 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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CSV_DIR = os.path.join(os.getcwd(), 'csv')
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def get_hashes_in_dir(dir_path: str) -> list:
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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 file in files:
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filepath = os.path.join(subdir, file)
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with open(filepath, 'rb') as f:
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filehash = hashlib.sha256(f.read()).hexdigest()
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hash_list.append({ 'file': filepath, 'sha256 hash': filehash})
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return hash_list
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def hash_submissions(submissions_dir_path: str):
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os.makedirs(CSV_DIR, exist_ok=True)
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submissions_dir_name = os.path.abspath(submissions_dir_path).split(os.path.sep)[-1]
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csv_file_name = f'{submissions_dir_name}_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', 'file', 'sha256 hash']
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writer = csv.DictWriter(csvfile, fieldnames=fieldnames)
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writer.writeheader()
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for student_dir_name in os.listdir(submissions_dir_path):
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student_dir_path = os.path.join(submissions_dir_path, student_dir_name)
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hashes_dict = get_hashes_in_dir(student_dir_path)
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for d in hashes_dict:
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d.update({'Student ID': student_dir_name}) # update hash records with student id
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writer.writerows(hashes_dict)
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return csv_file_path
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def get_suspicious_hashes(df: pd.DataFrame) -> list:
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drop_columns = ['file']
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df = df.drop(columns=drop_columns).sort_values('sha256 hash') # clear not needed colums & sort by hash
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duplicate_hash = df.loc[df.duplicated(subset=['sha256 hash'], keep=False), :] # all files with duplicate hash - incl. files from the same student id
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hash_with_multiple_student_ids = duplicate_hash.groupby('sha256 hash').agg(lambda x: len(x.unique())>1) # true if more than 1 unique student ids (= multiple student ids with same hash), false if unique (= same student id re-submitting with the same hash)
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suspicious_hashes_list = hash_with_multiple_student_ids[hash_with_multiple_student_ids['Student ID']==True].index.to_list() # list with duplicate hashes - only if different student id (doesn't include attempts from same student id)
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return suspicious_hashes_list
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def suspicious_by_hash(df: pd.DataFrame) -> pd.DataFrame:
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suspicious_hashes_list = get_suspicious_hashes(df)
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files_with_suspicious_hash = df[df['sha256 hash'].isin(suspicious_hashes_list)] # excluding duplicate from same student id
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return files_with_suspicious_hash.sort_values(['sha256 hash', 'Student ID'])
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