# -*- coding: utf-8 -*- """ Created on Mon Dec 25 18:18:27 2023 @author: alish """ import gradio as gr import fitz # PyMuPDF import questiongenerator as qs import random from sentence_transformers import SentenceTransformer, util from questiongenerator import QuestionGenerator qg = QuestionGenerator() def highlight_similar_sentence(text1, text2, color='yellow'): # Load the pre-trained sentence-transformers model model = SentenceTransformer("paraphrase-MiniLM-L6-v2") # Split text into sentences sentences_text1 = [sentence.strip() for sentence in text1.split('.') if sentence.strip()] sentences_text2 = [sentence.strip() for sentence in text2.split('.') if sentence.strip()] # Compute embeddings for text1 #embeddings_text1 = model.encode(sentences_text1, convert_to_tensor=True) highlighted_text2 = text2 max_similarity = 0.0 # Find the most similar sentence in text2 for each sentence in text1 for sentence_text1 in sentences_text1: # Compute embeddings for the current sentence in text1 embedding_text1 = model.encode(sentence_text1, convert_to_tensor=True) for sentence_text2 in sentences_text2: # Compute cosine similarity between sentence in text1 and text2 embedding_text2 = model.encode(sentence_text2, convert_to_tensor=True) similarity = util.pytorch_cos_sim(embedding_text1, embedding_text2).item() # Highlight the most similar sentence in text2 if similarity > max_similarity: max_similarity = similarity highlighted_text2= highlight_text(text2, sentence_text2, color=color) #highlighted_text2 = text2.replace(sentence_text2, f"{sentence_text2}") return highlighted_text2 def Extract_QA(qlist,selected_extracted_text): Q_All='' A_All='' xs=['A','B','C','D'] h_colors=['yellow', 'red', 'DodgerBlue', 'Orange', 'Violet'] for i in range(len(qlist)): question_i= qlist[i]['question'] Choices_ans= [] Choice_is_correct=[] for j in range(4): Choices_ans= Choices_ans+ [qlist[i]['answer'][j]['answer']] Choice_is_correct= Choice_is_correct+ [qlist[i]['answer'][j]['correct']] Q=f""" Q_{i+1}: {question_i} A. {Choices_ans[0]} B. {Choices_ans[1]} C. {Choices_ans[2]} D. {Choices_ans[3]} """ result = [x for x, y in zip(xs, Choice_is_correct) if y ] correct_answer= [x for x, y in zip(Choices_ans, Choice_is_correct) if y ] A= f"""

Answer_{i+1}: {result[0]} - {correct_answer[0]}

""" color= h_colors[i] A_sen= f""" The correct answer is {correct_answer[0]}.""" A= highlight_text(input_text=A, selcted_text=correct_answer[0], color=color) selected_extracted_text= highlight_similar_sentence(A_sen, selected_extracted_text, color=color) Q_All= Q_All+Q A_All=A_All+ A return (Q_All,A_All,selected_extracted_text) def extract_text_from_pdf(pdf_file_path): # Read the PDF file global extracted_text text = [] with fitz.open(pdf_file_path) as doc: for page in doc: text.append(page.get_text()) extracted_text= '\n'.join(text) extracted_text= get_sub_text(extracted_text) #return ("The pdf is uploaded Successfully from:"+ str(pdf_file_path)) return ("File is uploaded Successfuly!") qg = qs.QuestionGenerator() def get_sub_text(TXT): sub_texts= qg._split_into_segments(TXT) if isinstance(sub_texts, list): return sub_texts else: return [sub_texts] def highlight_text(input_text, selcted_text, color='yellow'): # Replace 'highlight' with tags for highlighting highlighted_text = input_text.replace(selcted_text, f'{selcted_text}') return highlighted_text def pick_One_txt(sub_texts): global selected_extracted_text N= len(sub_texts) if N==1: selected_extracted_text= sub_texts[0] return(selected_extracted_text) # Generate a random number between low and high random_number = random.uniform(0, N) # Pick the integer part of the random number random_number = int(random_number) selected_extracted_text= sub_texts[random_number] return(selected_extracted_text) def pipeline(NoQs): global Q,A text= selected_extracted_text qlist= qg.generate(text, num_questions=NoQs, answer_style="multiple_choice") Q,A,highligthed_text= Extract_QA(qlist,text) A= A + '\n'+highligthed_text return (Q,A) def ReurnAnswer(): return A def GetQuestion(NoQs): NoQs=int(NoQs) pick_One_txt(extracted_text) Q,A=pipeline(NoQs) return Q with gr.Blocks() as demo: global input_file with gr.Row(): with gr.Column(scale=1): with gr.Row(): gr.Image("PupQuizAI.png") gr.Markdown(""" 🐶 **PupQuizAI** is an Artificial-Intelligence tool that streamlines the studying process. Simply input a text pdf that you need to study from. Then, PupQuiz will create 1-5 custom questions for you to study from each time you push 'Show Questions'. """ ) with gr.Row(): input_file=gr.UploadButton(label='Select a file!', file_types=[".pdf"]) #status = gr.Textbox(label="Status") status = gr.HTML( ) input_file.upload(fn=extract_text_from_pdf, inputs=input_file,outputs=status) #upload_btn = gr.Button(value="Upload the pdf File.") Gen_Question = gr.Button(value="Show Questions") Gen_Answer = gr.Button(value="Show Answers") No_Qs= gr.Slider(minimum=1, maximum=5,value=3, step=1, label='Max # of Questions') gr.Markdown(""" 🐶 **Instructions:** * Start by selecting a 'pdf' text file you want to upload by clicking the "Select file" button. (PupQuiz currently only supports files that can have highlightable text) * Select the number of questions you want generated from the "# of Questions" selector. * Click "Show Questions" * Then, if you want answers to the questions, select "Show Answers" """ ) #gr.Image("PupQuizAI.png") with gr.Column(scale=2.0): #file_stat= gr.Textbox(label="File Status") question = gr.Textbox(label="Question(s)") #Answer = gr.Textbox(label="Answer(s)") Answer = gr.HTML(label="Answer(s)") Gen_Question.click(GetQuestion, inputs=No_Qs, outputs=question, api_name="QuestioGenerator") Gen_Answer.click(ReurnAnswer, inputs=None, outputs=Answer, api_name="QuestioGenerator") demo.launch()