import streamlit as st import nltk from gensim.models.doc2vec import Doc2Vec, TaggedDocument from nltk.tokenize import word_tokenize import PyPDF2 import pandas as pd import re import matplotlib.pyplot as plt import seaborn as sns import spacy import re import pandas as pd import matplotlib.pyplot as plt import seaborn as sns nltk.download('punkt') nlp_model_path = "Priyanka-Balivada/en_Resume_Matching_Keywords" nlp = spacy.load(nlp_model_path) float_regex = re.compile(r'^\d{1,2}(\.\d{1,2})?$') email_pattern = r'\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Z|a-z]{2,}\b' float_digit_regex = re.compile(r'^\d{10}$') email_with_phone_regex = email_with_phone_regex = re.compile( r'(\d{10}).|.(\d{10})') def extract_text_from_pdf(pdf_file): pdf_reader = PyPDF2.PdfReader(pdf_file) text = "" for page_num in range(len(pdf_reader.pages)): text += pdf_reader.pages[page_num].extract_text() return text def tokenize_text(text, nlp_model): doc = nlp_model(text, disable=["tagger", "parser"]) tokens = [(token.text.lower(), token.label_) for token in doc.ents] return tokens def extract_cgpa(resume_text): # Define a regular expression pattern for CGPA extraction cgpa_pattern = r'\b(?:CGPA|GPA|C\.G\.PA|Cumulative GPA)\s*:?[\s-]([0-9]+(?:\.[0-9]+)?)\b|\b([0-9]+(?:\.[0-9]+)?)\s(?:CGPA|GPA)\b' # Search for CGPA pattern in the text match = re.search(cgpa_pattern, resume_text, re.IGNORECASE) # Check if a match is found if match: # Extract CGPA value cgpa = match.group(1) if match.group(1) else match.group(2) return float(cgpa) else: return None def extract_skills(text, skills_keywords): skills = [skill.lower() for skill in skills_keywords if re.search(r'\b' + re.escape(skill.lower()) + r'\b', text.lower())] return skills def preprocess_text(text): return word_tokenize(text.lower()) def train_doc2vec_model(documents): model = Doc2Vec(vector_size=20, min_count=2, epochs=50) model.build_vocab(documents) model.train(documents, total_examples=model.corpus_count, epochs=model.epochs) return model def calculate_similarity(model, text1, text2): vector1 = model.infer_vector(preprocess_text(text1)) vector2 = model.infer_vector(preprocess_text(text2)) return model.dv.cosine_similarities(vector1, [vector2])[0] def accuracy_calculation(true_positives, false_positives, false_negatives): total = true_positives + false_positives + false_negatives accuracy = true_positives / total if total != 0 else 0 return accuracy # Streamlit Frontend st.markdown("# Resume Matching Tool 📃📃") st.markdown("An application to match resumes with a job description.") # Sidebar - File Upload for Resumes st.sidebar.markdown("## Upload Resumes PDF") resumes_files = st.sidebar.file_uploader( "Upload Resumes PDF", type=["pdf"], accept_multiple_files=True) if resumes_files: # Sidebar - File Upload for Job Descriptions st.sidebar.markdown("## Upload Job Description PDF") job_descriptions_file = st.sidebar.file_uploader( "Upload Job Description PDF", type=["pdf"]) if job_descriptions_file: # Backend Processing job_description_text = extract_text_from_pdf(job_descriptions_file) resumes_texts = [extract_text_from_pdf( resume_file) for resume_file in resumes_files] job_description_text = extract_text_from_pdf(job_descriptions_file) job_description_tokens = tokenize_text(job_description_text, nlp) # st.subheader("Matching Keywords") # Initialize counters overall_skill_matches = 0 overall_qualification_matches = 0 # Create a list to store individual results results_list = [] job_skills = set() job_qualifications = set() for job_token, job_label in job_description_tokens: if job_label == 'QUALIFICATION': job_qualifications.add(job_token.replace('\n', ' ')) elif job_label == 'SKILLS': job_skills.add(job_token.replace('\n', ' ')) job_skills_number = len(job_skills) job_qualifications_number = len(job_qualifications) # Lists to store counts of matched skills for all resumes skills_counts_all_resumes = [] # Iterate over all uploaded resumes for uploaded_resume in resumes_files: resume_text = extract_text_from_pdf(uploaded_resume) resume_tokens = tokenize_text(resume_text, nlp) # Initialize counters for individual resume skillMatch = 0 qualificationMatch = 0 cgpa = "" # Lists to store matched skills and qualifications for each resume matched_skills = set() matched_qualifications = set() email = set() phone = set() name = set() # Compare the tokens in the resume with the job description for resume_token, resume_label in resume_tokens: for job_token, job_label in job_description_tokens: if resume_token.lower().replace('\n', ' ') == job_token.lower().replace('\n', ' '): if resume_label == 'SKILLS': matched_skills.add(resume_token.replace('\n', ' ')) elif resume_label == 'QUALIFICATION': matched_qualifications.add(resume_token.replace('\n', ' ')) elif resume_label == 'PHONE' and bool(float_digit_regex.match(resume_token)): phone.add(resume_token) elif resume_label == 'QUALIFICATION': matched_qualifications.add(resume_token.replace('\n', ' ')) skillMatch = len(matched_skills) qualificationMatch = len(matched_qualifications) # Convert the list of emails to a set email_set = set(re.findall(email_pattern, resume_text.replace('\n', ' '))) email.update(email_set) numberphone="" for email_str in email: numberphone = email_with_phone_regex.search(email_str) if numberphone: email.remove(email_str) val=numberphone.group(1) or numberphone.group(2) phone.add(val) email.add(email_str.strip(val)) # Increment overall counters based on matches overall_skill_matches += skillMatch overall_qualification_matches += qualificationMatch # Add count of matched skills for this resume to the list skills_counts_all_resumes.append( [resume_text.count(skill.lower()) for skill in job_skills]) # Create a dictionary for the current resume and append to the results list result_dict = { "Resume": uploaded_resume.name, "Similarity Score": (skillMatch/job_skills_number)*100, "Skill Matches": skillMatch, "Matched Skills": matched_skills, "CGPA": extract_cgpa(resume_text), "Email": email, "Phone": phone, "Qualification Matches": qualificationMatch, "Matched Qualifications": matched_qualifications } results_list.append(result_dict) # Display overall matches st.subheader("Overall Matches") st.write(f"Total Skill Matches: {overall_skill_matches}") st.write( f"Total Qualification Matches: {overall_qualification_matches}") st.write(f"Job Qualifications: {job_qualifications}") st.write(f"Job Skills: {job_skills}") # Display individual results in a table results_df = pd.DataFrame(results_list) st.subheader("Individual Results") st.dataframe(results_df) tagged_resumes = [TaggedDocument(words=preprocess_text( text), tags=[str(i)]) for i, text in enumerate(resumes_texts)] model_resumes = train_doc2vec_model(tagged_resumes) st.subheader("\nHeatmap:") # Get skills keywords from user input skills_keywords_input = st.text_input( "Enter skills keywords separated by commas (e.g., python, java, machine learning):") skills_keywords = [skill.strip() for skill in skills_keywords_input.split(',') if skill.strip()] if skills_keywords: # Calculate the similarity score between each skill keyword and the resume text skills_similarity_scores = [] for resume_text in resumes_texts: resume_text_similarity_scores = [] for skill in skills_keywords: similarity_score = calculate_similarity( model_resumes, resume_text, skill) resume_text_similarity_scores.append(similarity_score) skills_similarity_scores.append(resume_text_similarity_scores) # Create a DataFrame with the similarity scores and set the index to the names of the PDFs skills_similarity_df = pd.DataFrame( skills_similarity_scores, columns=skills_keywords, index=[resume_file.name for resume_file in resumes_files]) # Plot the heatmap fig, ax = plt.subplots(figsize=(12, 8)) sns.heatmap(skills_similarity_df, cmap='YlGnBu', annot=True, fmt=".2f", ax=ax) ax.set_title('Heatmap for Skills Similarity') ax.set_xlabel('Skills') ax.set_ylabel('Resumes') # Rotate the y-axis labels for better readability plt.yticks(rotation=0) # Display the Matplotlib figure using st.pyplot() st.pyplot(fig) else: st.write("Please enter at least one skill keyword.") else: st.warning("Please upload the Job Description PDF to proceed.") else: st.warning("Please upload Resumes PDF to proceed.")