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| import streamlit as st | |
| import pandas as pd | |
| import pickle | |
| import os | |
| import io | |
| from reportlab.platypus import SimpleDocTemplate, Paragraph, Spacer, Table, TableStyle | |
| from reportlab.lib.pagesizes import A4 | |
| from reportlab.lib.styles import getSampleStyleSheet | |
| from reportlab.lib import colors | |
| from reportlab.lib.units import inch | |
| from datetime import datetime | |
| from sentence_transformers import SentenceTransformer, util | |
| import numpy as np | |
| # FILE BASED PERSISTENT STORAGE | |
| USERS_FILE = "skillsense_users.pkl" | |
| HISTORY_FILE = "skillsense_history.pkl" | |
| # Load/Save functions | |
| def load_users(): | |
| if os.path.exists(USERS_FILE): | |
| with open(USERS_FILE, 'rb') as f: | |
| return pickle.load(f) | |
| return { | |
| "user@gmail.com": {"password": "user123", "role": "user"}, | |
| "hr@company.com": {"password": "hr456", "role": "user"} | |
| } | |
| def save_users(users): | |
| with open(USERS_FILE, 'wb') as f: | |
| pickle.dump(users, f) | |
| def load_history(): | |
| if os.path.exists(HISTORY_FILE): | |
| with open(HISTORY_FILE, 'rb') as f: | |
| return pickle.load(f) | |
| return [] | |
| def save_history(history): | |
| with open(HISTORY_FILE, 'wb') as f: | |
| pickle.dump(history, f) | |
| # Load data | |
| users_db = load_users() | |
| history_db = load_history() | |
| ADMIN_AUTH_CODE = "SSAI-ADMIN-2026-X7K9" | |
| def generate_interview_questions(detected_skills, level): | |
| """Generate targeted interview questions based on detected skills""" | |
| questions = [] | |
| for skill in detected_skills[:5]: | |
| questions.extend([ | |
| f"Can you describe your hands-on experience with {skill}?", | |
| f"What was the most challenging project you worked on using {skill}?", | |
| f"How do you approach debugging issues in {skill} projects?" | |
| ]) | |
| return questions[:8] | |
| # AI FUNCTION (HUGGINGFACE) | |
| def load_ai_model(): | |
| return SentenceTransformer('all-MiniLM-L6-v2') | |
| def ai_resume_analysis(resume_text, job_role="Software Developer"): | |
| try: | |
| model = load_ai_model() | |
| # Extract skills from resume | |
| resume_lower = resume_text.lower() | |
| detected_skills = [] | |
| skill_list = ["python", "react", "java", "sql", "aws", "docker", "angular", "node", "javascript", "mysql"] | |
| for skill in skill_list: | |
| if skill in resume_lower: | |
| detected_skills.append(skill.title()) | |
| # Job required skills | |
| job_skills = ["python", "react", "sql", "aws", "docker", "node"] | |
| # AI Similarity calculation | |
| if detected_skills: | |
| resume_text = ", ".join(detected_skills) | |
| job_text = ", ".join(job_skills) | |
| resume_emb = model.encode([resume_text]) | |
| job_emb = model.encode([job_text]) | |
| similarity = util.cos_sim(resume_emb, job_emb).item() * 100 | |
| else: | |
| similarity = 20 | |
| # AI Recommendation | |
| score = min(95, max(25, int(similarity))) | |
| recommendation = "HIRE" if score > 75 else "INTERVIEW" if score > 50 else "REVIEW" | |
| # Generate interview questions | |
| interview_questions = generate_interview_questions(detected_skills, "Mid") | |
| return { | |
| "ai_score": score, | |
| "recommendation": recommendation, | |
| "detected_skills": detected_skills[:8], | |
| "job_fit": f"{score}% match for {job_role}", | |
| "strengths": detected_skills[:3], | |
| "confidence": "High" if score > 70 else "Medium", | |
| "interview_questions": interview_questions | |
| } | |
| except: | |
| return { | |
| "ai_score": 50, | |
| "recommendation": "REVIEW", | |
| "detected_skills": [], | |
| "job_fit": "AI temporarily unavailable", | |
| "strengths": [], | |
| "confidence": "Low", | |
| "interview_questions": [] | |
| } | |
| def generate_hiring_pdf(results): | |
| buffer = io.BytesIO() | |
| doc = SimpleDocTemplate(buffer, pagesize=A4, rightMargin=36, leftMargin=36, topMargin=36, bottomMargin=36) | |
| styles = getSampleStyleSheet() | |
| story = [] | |
| # HEADER | |
| story.append(Paragraph("SkillSense AI - Resume Analysis Report", styles['Title'])) | |
| story.append(Paragraph(f"Generated: {datetime.now().strftime('%B %d, %Y %I:%M %p')}", styles['Normal'])) | |
| story.append(Spacer(1, 20)) | |
| # CANDIDATE INFO TABLE | |
| candidate_data = [["Candidate Name:", results['candidate']], | |
| ["Position Level:", results.get('level', 'N/A')], | |
| ["Analysis Date:", datetime.now().strftime('%Y-%m-%d')]] | |
| candidate_table = Table(candidate_data, colWidths=[2*inch, 3.5*inch]) | |
| candidate_table.setStyle(TableStyle([ | |
| ('BACKGROUND', (0, 0), (-1, -1), colors.lightblue), | |
| ('FONTNAME', (0, 0), (0, 0), 'Helvetica-Bold'), | |
| ('ALIGN', (0, 0), (-1, -1), 'LEFT'), | |
| ('GRID', (0, 0), (-1, -1), 1, colors.black), | |
| ('FONTSIZE', (0, 0), (-1, -1), 12) | |
| ])) | |
| story.extend([candidate_table, Spacer(1, 20)]) | |
| # MAIN DECISION TABLE | |
| ai_score = results.get('ai_score', 0) | |
| decision_data = [ | |
| ["Metric", "Status", "Score"], | |
| ["Hiring Decision", results.get('recommendation', 'N/A'), f"{ai_score}%"], | |
| ["Skill Match", f"{len(results.get('detected_skills', []))} Skills"], | |
| ["AI Recommendation", results.get('job_fit', 'N/A')] | |
| ] | |
| decision_table = Table(decision_data, colWidths=[1.8*inch, 2.2*inch, 1.8*inch]) | |
| decision_table.setStyle(TableStyle([ | |
| ('BACKGROUND', (0, 0), (-1, 0), colors.darkgreen), | |
| ('TEXTCOLOR', (0, 0), (-1, 0), colors.whitesmoke), | |
| ('FONTNAME', (0, 0), (-1, 0), 'Helvetica-Bold'), | |
| ('FONTSIZE', (0, 0), (-1, 0), 14), | |
| ('ALIGN', (0, 0), (-1, -1), 'CENTER'), | |
| ('BACKGROUND', (0, 1), (-1, -1), colors.lightgreen if ai_score > 70 else colors.lightcoral), | |
| ('GRID', (0, 0), (-1, -1), 2, colors.black), | |
| ('FONTSIZE', (0, 1), (-1, -1), 12) | |
| ])) | |
| story.extend([Paragraph("AI-ENHANCED HIRING DECISION", styles['Heading1']), | |
| Spacer(1, 12), decision_table, Spacer(1, 20)]) | |
| # SKILLS GRID | |
| all_skills = results.get('detected_skills', []) | |
| story.append(Paragraph("DETECTED TECHNICAL SKILLS", styles['Heading2'])) | |
| if all_skills: | |
| skills_data = [] | |
| for i in range(0, len(all_skills), 2): | |
| row = [all_skills[i]] | |
| if i+1 < len(all_skills): | |
| row.append(all_skills[i+1]) | |
| skills_data.append(row) | |
| skills_table = Table(skills_data, colWidths=[3*inch, 3*inch]) | |
| skills_table.setStyle(TableStyle([ | |
| ('BACKGROUND', (0, 0), (-1, -1), colors.ivory), | |
| ('FONTNAME', (0, 0), (-1, -1), 'Helvetica-Bold'), | |
| ('FONTSIZE', (0, 0), (-1, -1), 11), | |
| ('ALIGN', (0, 0), (-1, -1), 'CENTER'), | |
| ('GRID', (0, 0), (-1, -1), 1, colors.darkgreen), | |
| ('VALIGN', (0, 0), (-1, -1), 'MIDDLE') | |
| ])) | |
| story.append(skills_table) | |
| story.append(Spacer(1, 20)) | |
| # INTERVIEW QUESTIONS | |
| questions = results.get('interview_questions', []) | |
| if questions: | |
| story.append(Paragraph("INTERVIEW QUESTIONS", styles['Heading2'])) | |
| for i, question in enumerate(questions[:6], 1): | |
| story.append(Paragraph(f"{i}. {question}", styles['Normal'])) | |
| story.append(Spacer(1, 8)) | |
| # ANALYSIS | |
| story.append(Paragraph("DETAILED AI ANALYSIS", styles['Heading2'])) | |
| analysis_para = Paragraph(f""" | |
| <b>AI Score:</b> {ai_score}% semantic match using HuggingFace Transformers<br/> | |
| <b>Skills Found:</b> {len(all_skills)} technical skills detected<br/> | |
| <b>Position Fit:</b> {results.get('level', 'N/A')} level position<br/> | |
| <b>Recommendation:</b> {results.get('recommendation', 'N/A')}<br/><br/> | |
| <b>Powered by:</b> SkillSense AI + HuggingFace AI | |
| """, styles['Normal']) | |
| story.append(analysis_para) | |
| # FOOTER | |
| story.append(Spacer(1, 20)) | |
| footer_data = [["Generated by:", "SkillSense AI Enterprise + HuggingFace AI"], | |
| ["Report ID:", f"SSAI-{datetime.now().strftime('%Y%m%d%H%M')}"]] | |
| footer_table = Table(footer_data, colWidths=[2*inch, 3.5*inch]) | |
| footer_table.setStyle(TableStyle([ | |
| ('BACKGROUND', (0, 0), (-1, -1), colors.lightgrey), | |
| ('ALIGN', (0, 0), (-1, -1), 'CENTER'), | |
| ('GRID', (0, 0), (-1, -1), 1, colors.black) | |
| ])) | |
| story.append(footer_table) | |
| doc.build(story) | |
| buffer.seek(0) | |
| return buffer | |
| # SESSION STATE | |
| if 'logged_in' not in st.session_state: | |
| st.session_state.logged_in = False | |
| if 'current_user' not in st.session_state: | |
| st.session_state.current_user = None | |
| if 'user_role' not in st.session_state: | |
| st.session_state.user_role = None | |
| if 'results' not in st.session_state: | |
| st.session_state.results = None | |
| st.set_page_config(page_title="SkillSense AI", layout="wide") | |
| # MAIN APP | |
| if not st.session_state.logged_in: | |
| st.title("SkillSense AI") | |
| st.markdown("Professional Resume Analysis Platform") | |
| tab1, tab2 = st.tabs(["Login", "Create Account"]) | |
| # TAB 1: LOGIN | |
| with tab1: | |
| st.markdown("Login with your saved account") | |
| with st.form("login_form"): | |
| email = st.text_input("Email") | |
| password = st.text_input("Password", type="password") | |
| if st.form_submit_button("LOGIN", type="primary"): | |
| if email in users_db and users_db[email]["password"] == password: | |
| st.session_state.logged_in = True | |
| st.session_state.current_user = email | |
| st.session_state.user_role = users_db[email]["role"] | |
| st.success(f"Welcome back {email}!") | |
| st.rerun() | |
| else: | |
| st.error("Invalid credentials!") | |
| st.info("Default: user@gmail.com / user123") | |
| # TAB 2: CREATE ACCOUNT | |
| with tab2: | |
| st.markdown("Create New Account (Saved Forever)") | |
| with st.form("create_account"): | |
| email = st.text_input("New Email") | |
| password = st.text_input("New Password", type="password") | |
| col1, col2 = st.columns(2) | |
| with col1: | |
| if st.form_submit_button("Create USER Account", type="secondary"): | |
| if email and password: | |
| if email not in users_db: | |
| users_db[email] = {"password": password, "role": "user"} | |
| save_users(users_db) | |
| st.success(f"User account SAVED: {email}") | |
| st.info("Login anytime with same details!") | |
| else: | |
| st.error("Email already exists!") | |
| else: | |
| st.error("Fill all fields!") | |
| with col2: | |
| admin_code = st.text_input("Admin Auth Code", type="password", placeholder="") | |
| if st.form_submit_button("Create ADMIN Account", type="primary"): | |
| if email and password: | |
| if email not in users_db: | |
| if admin_code == ADMIN_AUTH_CODE: | |
| users_db[email] = {"password": password, "role": "admin"} | |
| save_users(users_db) | |
| st.success(f"Admin account SAVED: {email}") | |
| else: | |
| st.error("Wrong admin code!") | |
| else: | |
| st.error("Email already exists!") | |
| else: | |
| st.error("Fill all fields!") | |
| else: | |
| # DASHBOARD | |
| st.sidebar.markdown("SkillSense AI") | |
| st.sidebar.markdown(f"User: {st.session_state.current_user}") | |
| st.sidebar.markdown(f"Role: {st.session_state.user_role.upper()}") | |
| if st.sidebar.button("Logout"): | |
| st.session_state.logged_in = False | |
| st.session_state.current_user = None | |
| st.session_state.user_role = None | |
| st.session_state.results = None | |
| st.rerun() | |
| # Navigation | |
| if st.session_state.user_role == "admin": | |
| page = st.sidebar.radio("Dashboard", ["Resume Analyzer", "History", "Admin"]) | |
| else: | |
| page = st.sidebar.radio("Dashboard", ["Resume Analyzer"]) | |
| if page == "Resume Analyzer": | |
| st.title("SkillSense AI - Resume Analyzer") | |
| col1, col2 = st.columns([2,1]) | |
| with col1: | |
| resume_text = st.text_area("Paste Resume", height=300) | |
| with col2: | |
| name = st.text_input("Candidate Name") | |
| level = st.select_slider("Level", ["Junior", "Mid", "Senior"]) | |
| # AI ANALYSIS BUTTON | |
| if st.button("AI ANALYSIS", type="primary", use_container_width=True): | |
| with st.spinner("AI analyzing with HuggingFace Transformers..."): | |
| ai_result = ai_resume_analysis(resume_text) | |
| ai_result["candidate"] = name or "Candidate" | |
| ai_result["level"] = level | |
| st.session_state.results = ai_result | |
| history_db.append(ai_result) | |
| save_history(history_db) | |
| st.success(f"AI Score: {ai_result['ai_score']}%") | |
| # DISPLAY RESULTS | |
| if st.session_state.get('results'): | |
| r = st.session_state.results | |
| ai_score = r.get('ai_score', 0) | |
| if ai_score > 75: | |
| st.markdown(f""" | |
| <div style='background:linear-gradient(45deg,#4CAF50,#45a049); | |
| padding:30px;border-radius:15px;color:white;text-align:center'> | |
| <h1>HIRE RECOMMENDED</h1><h2>{ai_score}%</h2></div>""", unsafe_allow_html=True) | |
| elif ai_score > 50: | |
| st.markdown(f""" | |
| <div style='background:linear-gradient(45deg,#FF9800,#F57C00); | |
| padding:30px;border-radius:15px;color:white;text-align:center'> | |
| <h1>INTERVIEW</h1><h2>{ai_score}%</h2></div>""", unsafe_allow_html=True) | |
| else: | |
| st.markdown(f""" | |
| <div style='background:linear-gradient(45deg,#f44336,#d32f2f); | |
| padding:30px;border-radius:15px;color:white;text-align:center'> | |
| <h1>REVIEW NEEDED</h1><h2>{ai_score}%</h2></div>""", unsafe_allow_html=True) | |
| col1, col2, col3 = st.columns(3) | |
| with col1: st.metric("Skills Found", len(r.get("detected_skills", []))) | |
| with col2: st.metric("Target Level", r.get("level", "N/A")) | |
| with col3: st.metric("AI Score", f"{ai_score}%") | |
| st.subheader("AI Detected Skills") | |
| for skill in r.get("detected_skills", []): | |
| st.success(skill) | |
| # INTERVIEW QUESTIONS | |
| questions = r.get("interview_questions", []) | |
| if questions: | |
| st.subheader("Interview Questions") | |
| for i, q in enumerate(questions[:6], 1): | |
| st.info(f"Q{i}: {q}") | |
| st.caption("Powered by HuggingFace Sentence Transformers") | |
| # PDF DOWNLOAD (Admin only) | |
| if st.session_state.results and st.session_state.user_role == "admin": | |
| pdf_data = generate_hiring_pdf(st.session_state.results) | |
| st.download_button( | |
| "Download AI-Enhanced PDF Report", | |
| pdf_data.getvalue(), | |
| f"SkillSense_AI_Report_{st.session_state.results['candidate'].replace(' ', '_')}.pdf", | |
| "application/pdf" | |
| ) | |
| elif page == "History" and st.session_state.user_role == "admin": | |
| st.title("Analysis History") | |
| if history_db: | |
| df = pd.DataFrame(history_db) | |
| st.dataframe(df[['candidate', 'recommendation', 'ai_score', 'timestamp']], use_container_width=True) | |
| else: | |
| st.info("No analysis history yet") | |
| elif page == "Admin" and st.session_state.user_role == "admin": | |
| st.title("Admin Panel") | |
| col1, col2 = st.columns(2) | |
| with col1: | |
| st.subheader("All User Accounts") | |
| user_df = pd.DataFrame([ | |
| {"Email": email, "Role": info["role"]} | |
| for email, info in users_db.items() | |
| ]) | |
| st.dataframe(user_df) | |
| st.info(f"Files: skillsense_users.pkl, skillsense_history.pkl") | |
| with col2: | |
| if st.button("Delete All Data"): | |
| if os.path.exists(USERS_FILE): | |
| os.remove(USERS_FILE) | |
| if os.path.exists(HISTORY_FILE): | |
| os.remove(HISTORY_FILE) | |
| st.success("All data deleted!") | |
| st.rerun() | |
| st.markdown("---") | |
| st.markdown("SkillSense AI 2026 | Powered by HuggingFace Transformers") | |