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| import streamlit as st | |
| import re | |
| from transformers import pipeline | |
| from transformers import AutoModelForQuestionAnswering, AutoTokenizer | |
| import tempfile | |
| import pytesseract | |
| import PyPDF2 | |
| from pdf2image import convert_from_path | |
| from PIL import Image | |
| st.set_page_config(page_title="Automated Question Answering System") # set page title | |
| # heading | |
| st.markdown("<h2 style='text-align: center;'>Question Answering on Academic Essays</h2>", unsafe_allow_html=True) | |
| # description | |
| st.markdown("<h3 style='text-align: left; color:#F63366; font-size:18px;'><b>What is this project about?<b></h3>", unsafe_allow_html=True) | |
| st.write("This project is to develop a web-based automated question-and-answer system for academic essays using natural language processing (NLP). Users can enter the essay and ask questions about it, and the system will automatically create answers.") | |
| st.write("👐 Click 'Input Text' or 'Upload File' to start experience the system. ") | |
| # store the model in cache resources to enhance efficiency (ref: https://docs.streamlit.io/library/advanced-features/caching) | |
| def question_model(): | |
| # call my model for question answering | |
| with st.spinner(text="Loading question model..."): | |
| model_name = "kxx-kkk/FYP_qa_final" | |
| tokenizer = AutoTokenizer.from_pretrained(model_name) | |
| model = AutoModelForQuestionAnswering.from_pretrained(model_name) | |
| question_answerer = pipeline("question-answering", model=model, tokenizer=tokenizer, handle_impossible_answer=True) | |
| print("QA model is dowloaded and ready to use") | |
| return question_answerer | |
| qamodel = question_model() | |
| def extract_text(file_path): | |
| text = "" | |
| image_text = "" | |
| with st.spinner(text="Extracting text from file..."): | |
| with open(file_path, "rb") as pdf_file: | |
| pdf_reader = PyPDF2.PdfReader(pdf_file) | |
| num_pages = len(pdf_reader.pages) | |
| for page_number in range(num_pages): | |
| # st.write(f"Page {page_number + 1}") | |
| page = pdf_reader.pages[page_number] | |
| text += page.extract_text() | |
| images = convert_from_path(file_path) # Convert PDF pages to images | |
| for i, image in enumerate(images): | |
| image_text += pytesseract.image_to_string(image) | |
| # text = text + image_text | |
| text = image_text | |
| # remove more than one new line | |
| text = re.sub(r"(?<!\n)\n(?!\n)", " ", text) | |
| return text | |
| # get the answer by passing the context & question to the model | |
| def question_answering(context, question): | |
| with st.spinner(text="Loading question model..."): | |
| question_answerer = qamodel | |
| with st.spinner(text="Getting answer..."): | |
| answer = question_answerer(context=context, question=question) | |
| print(answer) | |
| answer_score = str(answer["score"]) | |
| answer = answer["answer"] | |
| if (answer==""): | |
| answer = "CANNOT ANSWER" | |
| # display the result in container | |
| container = st.container(border=True) | |
| container.write("<h5><b>Answer:</b></h5>"+answer+"<p><small>(F1 score: "+answer_score+")</small></p><br>", unsafe_allow_html=True) | |
| # def question_answering(context, question): | |
| # with st.spinner(text="Loading question model..."): | |
| # question_answerer = qamodel | |
| # print("loading QA model...") | |
| # with st.spinner(text="Getting answer..."): | |
| # segment_size = 512 | |
| # overlap_size = 32 | |
| # text_length = len(context) | |
| # segments = [] | |
| # # Split context into segments | |
| # for i in range(0, text_length, segment_size - overlap_size): | |
| # segment_start = i | |
| # segment_end = i + segment_size | |
| # segment = context[segment_start:segment_end] | |
| # segments.append(segment) | |
| # answers = {} # Dictionary to store answers for each segment | |
| # # Get answers for each segment | |
| # for i, segment in enumerate(segments): | |
| # answer = question_answerer(context=segment, question=question) | |
| # answers[i] = answer | |
| # # Find the answer with the highest score | |
| # highest_score = -1 | |
| # highest_answer = None | |
| # for segment_index, answer in answers.items(): | |
| # print(answer) | |
| # score = answer["score"] | |
| # if score > highest_score: | |
| # highest_score = score | |
| # highest_answer = answer | |
| # if highest_answer is not None: | |
| # answer = highest_answer["answer"] | |
| # if answer == "": | |
| # answer = "CANNOT ANSWER" | |
| # answer_score = str(highest_answer["score"]) | |
| # # Display the result in container | |
| # container = st.container(border=True) | |
| # container.write("<h5><b>Answer:</b></h5>" + answer + "<p><small>(F1 score: " + answer_score + ")</small></p><br>", | |
| # unsafe_allow_html=True) | |
| #-------------------- Main Webpage -------------------- | |
| # choose the source with different tabs | |
| tab1, tab2 = st.tabs(["Input Text", "Upload File"]) | |
| #---------- input text ---------- | |
| # if type the text as input | |
| with tab1: | |
| # set the example | |
| sample_question = "What is NLP?" | |
| with open("sample.txt", "r") as text_file: | |
| sample_text = text_file.read() | |
| # Get the initial values of context and question | |
| context = st.session_state.get("contextInput", "") | |
| question = st.session_state.get("questionInput", "") | |
| # Button to try the example | |
| example = st.button("Try with example") | |
| # Update the values if the "Try with example" button is clicked | |
| if example: | |
| context = sample_text | |
| question = sample_question | |
| # Display the text area and text input with the updated or default values | |
| context = st.text_area("Enter the essay below:", value=context, key="contextInput", height=330) | |
| question = st.text_input(label="Enter the question: ", value=question, key="questionInput") | |
| # perform question answering when "get answer" button clicked | |
| button = st.button("Get answer", key="textInput", type="primary") | |
| if button: | |
| if context=="" or question=="": | |
| st.error ("Please enter BOTH the context and the question", icon="🚨") | |
| else: | |
| question_answering(context, question) | |
| # ---------- upload file ---------- | |
| # if upload file as input | |
| with tab2: | |
| # provide upload place | |
| uploaded_file = st.file_uploader("Upload essay in PDF format:", type=["pdf"]) | |
| # Create a session-level variable to track the uploaded file | |
| if 'file' not in st.session_state: | |
| st.session_state.file = None | |
| # Create a session-level variable to track if text extraction has been done | |
| if 'text_extracted' not in st.session_state: | |
| st.session_state.text_extracted = False | |
| # Get the initial values of context and question | |
| context2 = st.session_state.get("contextInput2", "") | |
| question2 = st.session_state.get("questionInput2", "") | |
| # transfer file to context and allow ask question, then perform question answering | |
| if uploaded_file is not None: | |
| if st.session_state.file != uploaded_file: | |
| # Update the session state with the new file | |
| st.session_state.file = uploaded_file | |
| st.session_state.text_extracted = False | |
| if not st.session_state.text_extracted: | |
| with tempfile.NamedTemporaryFile(delete=False) as temp_file: | |
| temp_file.write(uploaded_file.read()) # Save uploaded file to a temporary path | |
| raw_text = extract_text(temp_file.name) | |
| context2 = raw_text | |
| st.session_state.text_extracted = True | |
| question2 = st.text_input(label="Enter your question",value=question2, key="questionInput2") | |
| context2 = st.text_area("Your essay context: ", value=context2, height=330, key="contextInput2") | |
| # perform question answering when "get answer" button clicked | |
| button2 = st.button("Get answer", key="fileInput", type="primary") | |
| if button2: | |
| if context2=="" or question2=="": | |
| st.error ("Please enter BOTH the context and the question", icon="🚨") | |
| else: | |
| question_answering(context2, question2) | |
| st.markdown("<p style='text-align:center;'>© 20069913D HUI Man Ki - Final Year Project</p>", unsafe_allow_html=True) | |
| st.markdown("<br><br><br><br><br>", unsafe_allow_html=True) | |