plagiarism_analysis.py

def process_document(text):
    """
    Create a vector for given text and adjust it for cosine similarity search
    """
    text_vect = create_vector_from_text(tokenizer, model, text)
    text_vect = np.array(text_vect)
    text_vect = text_vect.reshape(1, -1)

    return text_vect

    
def is_plagiarism(similarity_score, plagiarism_threshold):

  return similarity_score < plagiarism_threshold


def check_incoming_document(incoming_document):

  text_lang = detect(incoming_document)
  language_list = ['de', 'fr', 'el', 'ja', 'ru']

  final_result = ""

  if(text_lang == 'en'):
    final_result = incoming_document 

  elif(text_lang not in language_list):
    final_result = None

  else:
    # Translate in English
    final_result = translate_text(incoming_document, text_lang)

  return final_result


def run_plagiarism_analysis(query_text, data, plagiarism_threshold=0.8):

    top_N=3

    # Check the language of the query/incoming text and translate if required. 
    document_translation = check_incoming_document(query_text)

    if(document_translation is None):
      print("Only the following languages are supported: English, French, Russian, German, Greek and Japanese")
      exit(-1)

    else:
      # Preprocess the document to get the required vector for similarity analysis
      query_vect = process_document(document_translation)
      
      # Run similarity Search
      data["similarity"] = data["vectors"].apply(lambda x: cosine_similarity(query_vect, x))
      data["similarity"] = data["similarity"].apply(lambda x: x[0][0])

      similar_articles = data.sort_values(by='similarity', ascending=False)[1:top_N+1]
      formated_result = similar_articles[["abstract", "paper_id", "similarity"]].reset_index(drop = True)

      similarity_score = formated_result.iloc[0]["similarity"] 
      most_similar_article = formated_result.iloc[0]["abstract"] 
      is_plagiarism_bool = is_plagiarism(similarity_score, plagiarism_threshold)

      plagiarism_decision = {'similarity_score': similarity_score, 
                             'is_plagiarism': is_plagiarism_bool,
                             'most_similar_article': most_similar_article, 
                             'article_submitted': query_text
                            }

      return plagiarism_decision

It looks interesting! I also want to create a plagiarism checker app, as this problem is very spread today. Currently, I use this one https://fixgerald.com/plagiarism-checker-for-website to check the content I post in my blog. It's quite good, and I'd like to make something similar but a kind of automated check of the page. It must be useful for those who care about author's rights and uniquness of content.