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| import os | |
| import glob | |
| import PyPDF2 | |
| from sklearn.feature_extraction.text import CountVectorizer | |
| from sklearn.metrics.pairwise import cosine_similarity | |
| import zipfile | |
| import shutil | |
| def read_pdf(file_path): | |
| """ | |
| Read text content from a PDF file. | |
| Args: | |
| file_path (str): Path to the PDF file. | |
| Returns: | |
| str: Text content of the PDF. | |
| """ | |
| text = "" | |
| with open(file_path, "rb") as file: | |
| reader = PyPDF2.PdfReader(file) | |
| for page_num in range(len(reader.pages)): | |
| text += reader.pages[page_num].extract_text() | |
| return text | |
| def text_similarity(text1, text2): | |
| """ | |
| Compute the cosine similarity between two texts. | |
| Args: | |
| text1 (str): The first text. | |
| text2 (str): The second text. | |
| Returns: | |
| float: The cosine similarity between the two texts. | |
| """ | |
| # Create a CountVectorizer instance | |
| vectorizer = CountVectorizer().fit_transform([text1, text2]) | |
| # Calculate cosine similarity | |
| similarity = cosine_similarity(vectorizer) | |
| # Since there are only 2 texts, similarity[0, 1] or similarity[1, 0] gives the similarity | |
| return similarity[0, 1] | |
| def compare_pdfs(pdf_file1, pdf_file2): | |
| """ | |
| Compare two PDF files for similarity. | |
| Args: | |
| pdf_file1 (str): Path to the first PDF file. | |
| pdf_file2 (str): Path to the second PDF file. | |
| """ | |
| text1 = read_pdf(pdf_file1) | |
| text2 = read_pdf(pdf_file2) | |
| file1 = pdf_file1.split('/')[-1] | |
| file2 = pdf_file2.split('/')[-1] | |
| similarity_score = text_similarity(text1, text2) | |
| if similarity_score > 0.75: | |
| print(f"Similarity between '{file1}' and '{file2}': {similarity_score}") | |
| if similarity_score > 0.9: | |
| print(f"Complete plagiarism detected between '{file1}' and '{file2}'!") | |
| else: | |
| print(f"Potential plagiarism detected between '{file1}' and '{file2}'!") | |
| def main(folder_or_zip_path): | |
| """ | |
| Main function to compare PDF files either in a folder or within a zip file. | |
| Args: | |
| folder_or_zip_path (str): Path to the folder containing PDF files or to the zip file. | |
| """ | |
| if folder_or_zip_path.endswith('.zip'): | |
| # Unzip the file | |
| output_folder = '/Users/rishit/Documents/innovate_you/plagiarism_detection/plagiarism_rishit/zip_outputs' | |
| unzipped_folder = unzip_file(folder_or_zip_path, output_folder) | |
| folder_path = os.path.join(unzipped_folder, 'pdfs') | |
| else: | |
| folder_path = folder_or_zip_path | |
| # Get all PDF files in the folder | |
| pdf_files = glob.glob(os.path.join(folder_path, "*.pdf")) | |
| num_files = len(pdf_files) | |
| print(f"Found {num_files} PDF files in the folder.") | |
| if num_files == 0: | |
| print("No PDF files found in the specified folder.") | |
| return | |
| # Compare similarity for all pairs of PDF files | |
| for i in range(num_files): | |
| for j in range(i+1, num_files): | |
| compare_pdfs(pdf_files[i], pdf_files[j]) | |
| def unzip_file(zip_file, output_folder): | |
| """ | |
| Unzip a zip file to the specified output folder. | |
| Args: | |
| zip_file (str): Path to the zip file. | |
| output_folder (str): Path to the output folder where the contents will be extracted. | |
| Returns: | |
| str: Path to the folder containing the extracted files. | |
| """ | |
| # Create the output folder if it doesn't exist | |
| os.makedirs(output_folder, exist_ok=True) | |
| # Empty the output folder if it already exists | |
| if os.path.exists(output_folder): | |
| shutil.rmtree(output_folder) | |
| # Extract the zip file | |
| with zipfile.ZipFile(zip_file, 'r') as zip_ref: | |
| zip_ref.extractall(output_folder) | |
| return output_folder | |
| # Example usage: | |
| input_path = input("Enter the path to the folder containing PDF files or to the zip file: ").strip() | |
| main(input_path) | |