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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 | |
| import spacy | |
| from spacy.matcher import Matcher | |
| import os | |
| # Download the Spacy model if it's not already present | |
| if not spacy.util.is_package("en_core_web_sm"): | |
| os.system("python -m spacy download en_core_web_sm") | |
| # Load Spacy Model | |
| nlp = spacy.load("en_core_web_sm") | |
| # Extract Text from PDF | |
| def extract_text_from_pdf(uploaded_file): | |
| pdf_reader = PyPDF2.PdfReader(BytesIO(uploaded_file.read())) | |
| resume_text = '' | |
| for page in pdf_reader.pages: | |
| resume_text += page.extract_text() | |
| return resume_text | |
| # Load BERT Model for QA | |
| model = BertForQuestionAnswering.from_pretrained("bert-large-uncased-whole-word-masking-finetuned-squad") | |
| tokenizer = BertTokenizer.from_pretrained("bert-large-uncased-whole-word-masking-finetuned-squad") | |
| # Generate Answer from QA Model | |
| 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 | |
| # Extract Keywords for Resume Improvement | |
| def extract_keywords_for_sections(text): | |
| doc = nlp(text) | |
| skills_keywords = set() | |
| project_keywords = set() | |
| # Define patterns for skills and project ideas | |
| skill_patterns = [[{"POS": "NOUN", "OP": "+"}], [{"POS": "PROPN", "OP": "+"}]] | |
| project_patterns = [[{"POS": "VERB"}, {"POS": "NOUN", "OP": "+"}], [{"POS": "VERB"}, {"POS": "PROPN", "OP": "+"}]] | |
| matcher = Matcher(nlp.vocab) | |
| matcher.add("SKILLS", skill_patterns) | |
| matcher.add("PROJECTS", project_patterns) | |
| for match_id, start, end in matcher(doc): | |
| span = doc[start:end] | |
| if nlp.vocab.strings[match_id] == "SKILLS": | |
| skills_keywords.add(span.text) | |
| elif nlp.vocab.strings[match_id] == "PROJECTS": | |
| project_keywords.add(span.text) | |
| return skills_keywords, project_keywords | |
| # Suggest Resume Improvements | |
| def suggest_resume_improvements(resume_text, job_description): | |
| skills_keywords, project_keywords = extract_keywords_for_sections(job_description) | |
| missing_skills = [kw for kw in skills_keywords if kw.lower() not in resume_text.lower()] | |
| potential_projects = [f"Consider a project involving '{keyword}'." for keyword in project_keywords] | |
| skill_suggestions = [f"Consider highlighting your experience or skills related to '{keyword}'." for keyword in missing_skills[:5]] | |
| project_suggestions = potential_projects[:5] | |
| return skill_suggestions, project_suggestions | |
| # Analyze Matches between Resume and Job Description | |
| def analyze_matches(resume_text, job_description): | |
| resume_keywords = set(extract_keywords_for_sections(resume_text)[0]) | |
| job_desc_keywords = set(extract_keywords_for_sections(job_description)[0]) | |
| matches = resume_keywords & job_desc_keywords | |
| if matches: | |
| commentary = f"Your resume matches the following keywords from the job description: {', '.join(matches)}" | |
| else: | |
| commentary = "There are no direct keyword matches between your resume and the job description." | |
| return commentary | |
| # 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 = [] | |
| # Streamlit App Interface | |
| st.title('Resume Enhancement and Analysis App') | |
| # Resume PDF upload | |
| uploaded_file = st.file_uploader("Upload your resume (PDF format):", type='pdf') | |
| resume_text = '' | |
| if uploaded_file is not None: | |
| resume_text = extract_text_from_pdf(uploaded_file) | |
| st.write("Resume Text:") | |
| st.write(resume_text) | |
| # Question-Answer Functionality | |
| user_question = st.text_input("Ask a question based on your resume:") | |
| if user_question: | |
| answer = answer_question(user_question, resume_text, model, tokenizer) | |
| st.write("Answer:") | |
| st.write(answer) | |
| # Log the interaction | |
| st.session_state.qa_log.append({ | |
| 'Question': user_question, | |
| 'Answer': answer, | |
| 'Satisfaction': 'Pending' | |
| }) | |
| # Job Description Input for Resume Improvement | |
| job_description = st.text_area("Input the job description here for resume improvement suggestions:") | |
| if job_description: | |
| skill_suggestions, project_suggestions = suggest_resume_improvements(resume_text, job_description) | |
| st.write('Technical Skill Improvement Suggestions:') | |
| for suggestion in skill_suggestions: | |
| st.write(suggestion) | |
| st.write('Notable Project Ideas:') | |
| for suggestion in project_suggestions: | |
| st.write(suggestion) | |
| # Analyze Matches and Provide Commentary | |
| match_commentary = analyze_matches(resume_text, job_description) | |
| st.write("Match Commentary:") | |
| st.write(match_commentary) | |
| # User Feedback and Interaction Log | |
| if st.session_state.qa_log: | |
| st.write("Interaction Log:") | |
| for i, interaction in enumerate(st.session_state.qa_log): | |
| if interaction['Satisfaction'] == 'Pending': | |
| satisfaction = st.radio(f'Are you satisfied with the answer to: "{interaction["Question"]}"?', ('Yes', 'No'), key=f'satisfaction_{i}') | |
| st.session_state.qa_log[i]['Satisfaction'] = satisfaction | |
| log_df = pd.DataFrame(st.session_state.qa_log) | |
| st.dataframe(log_df) | |