import streamlit as st import nltk from nltk.corpus import stopwords from nltk.tokenize import word_tokenize from nltk.stem import PorterStemmer from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.metrics.pairwise import cosine_similarity from PyPDF2 import PdfReader import os from io import BytesIO import pickle import pdfminer from pdfminer.high_level import extract_text import re import PyPDF2 import textract import tempfile from docx import Document nltk.download('punkt') nltk.download('stopwords') def preprocess_text(text): words = word_tokenize(text.lower()) stop_words = set(stopwords.words('english')) words = [word for word in words if word not in stop_words] stemmer = PorterStemmer() words = [stemmer.stem(word) for word in words] return ' '.join(words) def extract_text_from_pdf(pdf_content): pdf_reader = PdfReader(BytesIO(pdf_content)) text = '' for page in pdf_reader.pages: text += page.extract_text() return text def extract_text_from_docx(docx_content): doc = Document(BytesIO(docx_content)) text = " ".join(paragraph.text for paragraph in doc.paragraphs) return text def extract_text_from_txt(txt_content): text = textract.process(input_filename=None, input_bytes=txt_content) return text def extract_text_from_resume(file_path): file_extension = file_path.split('.')[-1].lower() if file_extension == 'pdf': return extract_text_from_pdf(file_path) elif file_extension == 'docx': return extract_text_from_docx(file_path) elif file_extension == 'txt': return extract_text_from_txt(file_path) else: raise ValueError(f"Unsupported file format: {file_extension}") def clean_pdf_text(text): text = re.sub('http\S+\s*', ' ', text) text = re.sub('RT|cc', ' ', text) text = re.sub('#\S+', '', text) text = re.sub('@\S+', ' ', text) text = re.sub('[%s]' % re.escape("""!"#$%&'()*+,-./:;<=>?@[\]^_`{|}~"""), ' ', text) text = re.sub(r'[^\x00-\x7f]',r' ', text) text = re.sub('\s+', ' ', text) return text def extract_candidate_name(text): pattern = r'(?:Mr\.|Ms\.|Mrs\.)?\s?([A-Z][a-z]+)\s([A-Z][a-z]+)' match = re.search(pattern, text) if match: return match.group(0) return "Candidate Name Not Found" def calculate_similarity(job_description, cvs, cv_file_names): processed_job_desc = preprocess_text(job_description) processed_cvs = [preprocess_text(cv) for cv in cvs] all_text = [processed_job_desc] + processed_cvs vectorizer = TfidfVectorizer() tfidf_matrix = vectorizer.fit_transform(all_text) similarity_scores = cosine_similarity(tfidf_matrix)[0][1:] ranked_cvs = list(zip(cv_file_names, similarity_scores)) ranked_cvs.sort(key=lambda x: x[1], reverse=True) return ranked_cvs def extract_email_phone(text): email_pattern = r'\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Z|a-z]{2,}\b' phone_pattern = r'\b(?:\d{3}[-.\s]??\d{3}[-.\s]??\d{4}|\d{3}[-.\s]??\d{4})\b' emails = re.findall(email_pattern, text) phones = re.findall(phone_pattern, text) return emails, phones def rank_and_shortlist(job_description, cv_files, threshold=0.10): cv_texts = [] cv_file_names = [] cv_emails = [] cv_phones = [] for cv_file in cv_files: file_extension = os.path.splitext(cv_file.name)[1].lower() try: if file_extension == '.pdf': cv_text = extract_text_from_pdf(cv_file.read()) elif file_extension == '.docx': cv_text = extract_text_from_docx(cv_file.read()) elif file_extension == '.txt': cv_text = cv_file.read().decode('utf-8', errors='ignore') else: st.warning(f"Unsupported file format: {file_extension}. Skipping file: {cv_file.name}") continue cv_texts.append(clean_pdf_text(cv_text)) cv_file_names.append(cv_file.name) # Extract email and phone number from the CV text emails, phones = extract_email_phone(cv_text) cv_emails.append(emails) cv_phones.append(phones) except Exception as e: st.warning(f"Error processing file '{cv_file.name}': {str(e)}") continue if not cv_texts: st.error("No valid resumes found. Please upload resumes in supported formats (PDF, DOCX, or TXT).") return [], {} similarity_scores = calculate_similarity(job_description, cv_texts, cv_file_names) ranked_cvs = [(cv_name, score) for (cv_name, score) in similarity_scores] shortlisted_cvs = [(cv_name, score) for (cv_name, score) in ranked_cvs if score >= threshold] contact_info_dict = {} for cv_name, emails, phones in zip(cv_file_names, cv_emails, cv_phones): contact_info_dict[cv_name] = { 'emails': emails, 'phones': phones, } return ranked_cvs, shortlisted_cvs, contact_info_dict def main(): st.title("Resume Ranking App") st.write("Enter Job Title:") job_title = st.text_input("Job Title") st.write("Enter Job Description:") job_description = st.text_area("Job Description", height=200, key='job_description') st.write("Upload the Resumes:") cv_files = st.file_uploader("Choose files", accept_multiple_files=True, key='cv_files') if st.button("Submit"): if job_title and job_description and cv_files: job_description_text = f"{job_title} {job_description}" ranked_cvs, shortlisted_cvs, contact_info_dict = rank_and_shortlist(job_description_text, cv_files) st.markdown("### Ranking of Resumes:") for rank, score in ranked_cvs: st.markdown(f"**File Name:** {rank}, **Similarity Score:** {score:.2f}") st.markdown("### Shortlisted Candidates:") if not shortlisted_cvs: st.markdown("None") else: for rank, score in shortlisted_cvs: st.markdown(f"**File Name:** {rank}, **Similarity Score:** {score:.2f}") contact_info = contact_info_dict[rank] candidate_emails = contact_info.get('emails', []) candidate_phones = contact_info.get('phones', []) if candidate_emails: st.markdown(f"**Emails:** {', '.join(candidate_emails)}") if candidate_phones: st.markdown(f"**Phone Numbers:** {', '.join(candidate_phones)}") else: st.error("Please enter the job title, job description, and upload resumes to proceed.") else: st.write("Please enter the job title, job description, and upload resumes to proceed.") if __name__ == "__main__": main()