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| # -*- 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 questiongenerator import QuestionGenerator | |
| qg = QuestionGenerator() | |
| def Extract_QA(qlist): | |
| i=0 | |
| 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: {question_i} | |
| A. {Choices_ans[0]} | |
| B. {Choices_ans[1]} | |
| C. {Choices_ans[2]} | |
| D. {Choices_ans[3]} | |
| """ | |
| xs=['A','B','C','D'] | |
| result = [x for x, y in zip(xs, Choice_is_correct) if y ] | |
| A= f""" | |
| The rigth answer is: {result[0]} | |
| """ | |
| return (Q,A) | |
| 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)) | |
| 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 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(): | |
| global Q,A | |
| text= selected_extracted_text | |
| qlist= qg.generate(text, num_questions=1, answer_style="multiple_choice") | |
| Q,A= Extract_QA(qlist) | |
| A= A + '\n'+text | |
| return (Q,A) | |
| def ReurnAnswer(): | |
| return A | |
| def GetQuestion(): | |
| pick_One_txt(extracted_text) | |
| Q,A=pipeline() | |
| return Q | |
| with gr.Blocks() as demo: | |
| with gr.Row(): | |
| #input_file=gr.File(type="filepath", label="Upload PDF Document") | |
| input_file=gr.UploadButton(label='Select a file!', file_types=[".pdf"]) | |
| #upload_btn = gr.Button(value="Upload File") | |
| #txt= extract_text_from_pdf(input_file) | |
| with gr.Row(): | |
| with gr.Column(): | |
| upload_btn = gr.Button(value="Upload the pdf File.") | |
| Gen_Question = gr.Button(value="Show the Question") | |
| Gen_Answer = gr.Button(value="Show the Answer") | |
| with gr.Column(): | |
| file_stat= gr.Textbox(label="File Status") | |
| question = gr.Textbox(label="Question(s)") | |
| Answer = gr.Textbox(label="Answer(s)") | |
| upload_btn.click(extract_text_from_pdf, inputs=input_file, outputs=file_stat, api_name="QuestioGenerator") | |
| Gen_Question.click(GetQuestion, inputs=None, outputs=question, api_name="QuestioGenerator") | |
| Gen_Answer.click(ReurnAnswer, inputs=None, outputs=Answer, api_name="QuestioGenerator") | |
| #examples = gr.Examples(examples=["I went to the supermarket yesterday.", "Helen is a good swimmer."], | |
| # inputs=[english]) | |
| demo.launch() |