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| import streamlit as st | |
| import PyPDF2 | |
| import re | |
| # Function to extract text from a PDF | |
| def extract_text_from_pdf(uploaded_file): | |
| pdf_reader = PyPDF2.PdfReader(uploaded_file) | |
| text = " ".join([page.extract_text() for page in pdf_reader.pages if page.extract_text()]) | |
| return text | |
| # Function to extract skills from text (reference resumes and user resumes) | |
| def extract_skills_from_text(text): | |
| # A simple way to extract skills based on words in the resume. | |
| # This can be more advanced by integrating NER (Named Entity Recognition) or using predefined skill lists. | |
| skills = re.findall(r'\b[A-Za-z]+\b', text.lower()) # Extract words | |
| skills = set(skills) # Remove duplicates | |
| return skills | |
| # Function to evaluate user's resume based on job description | |
| def evaluate_resume(candidate_text, job_description_text): | |
| # Extract skills dynamically from job description | |
| job_description_skills = extract_skills_from_text(job_description_text) | |
| # Extract skills from candidate's resume | |
| candidate_skills = extract_skills_from_text(candidate_text) | |
| # Calculate matching skills | |
| matching_skills = job_description_skills.intersection(candidate_skills) | |
| missing_skills = job_description_skills.difference(candidate_skills) | |
| # Calculate improvement skills (skills present in the JD but not in candidate's resume) | |
| improvement_skills = job_description_skills.difference(matching_skills) | |
| # Calculate matching percentage (based on skills) | |
| matching_percentage = (len(matching_skills) / len(job_description_skills)) * 100 if len(job_description_skills) > 0 else 0 | |
| result = { | |
| "matching_skills": list(matching_skills), | |
| "missing_skills": list(missing_skills), | |
| "improvement_skills": list(improvement_skills), | |
| "matching_percentage": round(matching_percentage, 2) | |
| } | |
| return result | |
| # Streamlit UI | |
| st.title("📄 Resume Screening with Job Description Matching") | |
| st.write("Upload a candidate's resume and provide a job description to check the matching percentage based on skills.") | |
| # Upload job description | |
| st.subheader("Upload Job Description") | |
| job_description_file = st.file_uploader("Upload Job Description (PDF)", type=["pdf"]) | |
| # Upload candidate resume | |
| st.subheader("Upload Candidate Resume") | |
| uploaded_file = st.file_uploader("Upload Candidate Resume (PDF)", type=["pdf"]) | |
| if uploaded_file and job_description_file: | |
| st.write("🔍 **Processing resume and job description...**") | |
| # Extract text from candidate resume | |
| candidate_text = extract_text_from_pdf(uploaded_file) | |
| # Extract text from job description | |
| job_description_text = extract_text_from_pdf(job_description_file) | |
| # Evaluate resume based on job description | |
| evaluation = evaluate_resume(candidate_text, job_description_text) | |
| # Display results | |
| st.subheader("📌 Evaluation Result:") | |
| st.write(f"**🔢 Matching Percentage:** {evaluation['matching_percentage']}%") | |
| # st.write(f"**✅ Matching Skills:** {', '.join(evaluation['matching_skills']) or 'None'}") | |
| # st.write(f"**❌ Missing Skills:** {', '.join(evaluation['missing_skills']) or 'None'}") | |
| st.write(f"**💡 Improvement Skills (Required but Missing):** {', '.join(evaluation['improvement_skills']) or 'None'}") | |
| if evaluation["matching_percentage"] > 70: | |
| st.success("✅ Resume is a strong match for the job description!") | |
| elif evaluation["matching_percentage"] > 40: | |
| st.warning("⚠️ Resume is a partial match for the job description.") | |
| else: | |
| st.error("❌ Resume does not match well with the job description.") | |
| else: | |
| st.write("Please upload both the job description and candidate resume.") | |