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exam_data_scrapper.py
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#!pip install python-docx
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#!pip install PyPDF2 --upgrade
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import os
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import json
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from PyPDF2 import PdfReader
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from docx import Document
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def extract_from_pdf(pdf_path):
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"""Extract text from a PDF file."""
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pdf_data = ""
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with open(pdf_path, "rb") as pdf_file:
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reader = PdfReader(pdf_file)
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for page_num in range(len(reader.pages)):
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page = reader.pages[page_num]
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pdf_data += page.extract_text()
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return pdf_data
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def extract_from_json(json_path):
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"""Extract data from a JSON file."""
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with open(json_path, "r") as json_file:
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json_data = json.load(json_file)
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return json_data
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def extract_from_word(word_path):
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"""Extract text from a Word (.docx) file."""
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doc = Document(word_path)
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word_data = ""
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for para in doc.paragraphs:
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word_data += para.text + "\n"
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return word_data
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def extract_data(file_path):
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"""Extract data from a file based on its extension."""
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_, file_extension = os.path.splitext(file_path)
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if file_extension == ".pdf":
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return extract_from_pdf(file_path)
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elif file_extension == ".json":
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return extract_from_json(file_path)
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elif file_extension == ".docx":
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return extract_from_word(file_path)
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else:
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raise ValueError("Unsupported file extension: " + file_extension)
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def create_data_dictionary(files):
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"""Create a dictionary containing data from files based on their extension."""
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data_dict = {}
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for file_path in files:
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try:
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file_data = extract_data(file_path)
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data_dict[file_path] = file_data
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except ValueError as e:
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print(e)
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return data_dict
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# Usage example
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path = r'C:\Users\00110138\OneDrive - The University of Western Australia\Project\KaggleX FellowshipProgram\code\Exam_Data'
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# Usage example
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files = [str(path)+"/Geology_Geophysics_Exam.pdf", str(path)+"/Geology_Geophysics_Exam.json", str(path)+"/Geology_Geophysics_Exam.docx"]
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exam_data = [files[1]]
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data_dict = create_data_dictionary(exam_data)
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##
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school_data = ['university','department','course_code','course_title','date','duration','instructor']
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qcm_data = ['question','options', 'answer']
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short_data = ['question','answer']
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#print(data_dict[str(exam_data[0])]['multiple_choice_questions'])
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multiple_choice_questions = data_dict[str(exam_data[0])]['multiple_choice_questions']
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short_answer_questions = data_dict[str(exam_data[0])]['short_answer_questions']
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long_answer_questions = data_dict[str(exam_data[0])]['long_answer_questions']
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for s_data in school_data:
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print(f" {s_data}: {data_dict[str(exam_data[0])]['header'][str(s_data)]}")
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print(f"***************'school data'************************")
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for idx,qcm in enumerate(multiple_choice_questions):
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print(f" Index is: {idx} and 'Question': {qcm['question']}")
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print(f" Index is: {idx} and 'Options': {qcm['options']}")
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print(f" Index is: {idx} and 'Answer': {qcm['answer']}")
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print(f"***************'multiple_choice_questions'************************")
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for idx,qcm in enumerate(short_answer_questions):
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print(f" Index is: {idx} and 'Question': {qcm['question']}")
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print(f" Index is: {idx} and 'Answer': {qcm['answer']}")
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print(f"***************' END short_answer_questions'************************")
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print(f"***************' START long_answer_questions'************************")
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for idx,qcm in enumerate(long_answer_questions):
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print(f" Index is: {idx} and 'Question': {qcm['question']}")
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print(f" Index is: {idx} and 'Answer': {qcm['answer']}")
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print(f"***************' END long_answer_questions'************************")
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