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| import streamlit as st | |
| from transformers import BertForQuestionAnswering, BertTokenizer | |
| import torch | |
| from io import BytesIO | |
| import PyPDF2 | |
| import pandas as pd | |
| # Initialize session state to store the log of QA pairs and satisfaction responses | |
| if 'qa_log' not in st.session_state: | |
| st.session_state.qa_log = [] | |
| def extract_text_from_pdf(pdf_file): | |
| pdf_reader = PyPDF2.PdfReader(BytesIO(pdf_file.read())) | |
| text = "" | |
| for page in pdf_reader.pages: | |
| text += page.extract_text() | |
| return text | |
| def answer_question(question, context, model, tokenizer): | |
| inputs = tokenizer.encode_plus( | |
| question, | |
| context, | |
| add_special_tokens=True, | |
| return_tensors="pt", | |
| truncation="only_second", | |
| max_length=512, | |
| ) | |
| outputs = model(**inputs, return_dict=True) | |
| answer_start_scores = outputs.start_logits | |
| answer_end_scores = outputs.end_logits | |
| answer_start = torch.argmax(answer_start_scores) | |
| answer_end = torch.argmax(answer_end_scores) + 1 | |
| input_ids = inputs["input_ids"].tolist()[0] | |
| answer = tokenizer.convert_tokens_to_string( | |
| tokenizer.convert_ids_to_tokens(input_ids[answer_start:answer_end]) | |
| ) | |
| return answer | |
| st.title("Resume Question Answering") | |
| uploaded_file = st.file_uploader("Upload your resume (PDF format only)", type=["pdf"]) | |
| if uploaded_file is not None: | |
| resume_text = extract_text_from_pdf(uploaded_file) | |
| st.write("Resume Text:") | |
| st.write(resume_text) | |
| user_question = st.text_input("Ask a question based on your resume:") | |
| if user_question: | |
| model = BertForQuestionAnswering.from_pretrained("bert-large-uncased-whole-word-masking-finetuned-squad") | |
| tokenizer = BertTokenizer.from_pretrained("bert-large-uncased-whole-word-masking-finetuned-squad") | |
| answer = answer_question(user_question, resume_text, model, tokenizer) | |
| st.write("Answer:") | |
| st.write(answer) | |
| # Ask for user feedback on satisfaction | |
| satisfaction = st.radio('Are you satisfied with the answer?', ('Yes', 'No'), key='satisfaction') | |
| # Log the interaction | |
| st.session_state.qa_log.append({ | |
| 'Question': user_question, | |
| 'Answer': answer, | |
| 'Satisfaction': satisfaction | |
| }) | |
| # Display the log in a table format | |
| st.write("Interaction Log:") | |
| log_df = pd.DataFrame(st.session_state.qa_log) | |
| st.dataframe(log_df) | |