import streamlit as st import fitz import os import requests import json import re st.set_page_config(page_title="AI Resume Screener", page_icon="🔍", layout="wide") st.markdown(""" """, unsafe_allow_html=True) # ─── Helpers ────────────────────────────────────────────────────────────────── def extract_pdf_text(pdf_bytes: bytes) -> str: doc = fitz.open(stream=pdf_bytes, filetype="pdf") text = "" for page in doc: text += page.get_text("text") + "\n" doc.close() return text.strip() def score_resume(jd_text: str, resume_text: str, candidate_name: str, api_key: str) -> dict: prompt = f"""You are an expert HR recruiter and talent evaluator. Analyze the candidate's resume against the job description and provide a detailed evaluation. Job Description: {jd_text[:2000]} Candidate Resume ({candidate_name}): {resume_text[:2500]} Respond ONLY with a valid JSON object in exactly this format: {{ "score": , "verdict": "", "summary": "<2-3 sentence overall assessment>", "strengths": ["", "", ""], "gaps": ["", ""], "recommendation": "", "experience_match": , "skills_match": , "education_match": }} Be objective and specific. Base scores purely on how well the resume matches the JD requirements.""" headers = {"Authorization": f"Bearer {api_key}", "Content-Type": "application/json"} payload = { "model": "llama-3.3-70b-versatile", "messages": [{"role": "user", "content": prompt}], "max_tokens": 800, "temperature": 0.1, } r = requests.post("https://api.groq.com/openai/v1/chat/completions", headers=headers, json=payload, timeout=30) r.raise_for_status() raw = r.json()["choices"][0]["message"]["content"] raw = re.sub(r"```json|```", "", raw).strip() return json.loads(raw) # ─── Sidebar ────────────────────────────────────────────────────────────────── with st.sidebar: st.markdown("## 🔍 Resume Screener") st.markdown("

", unsafe_allow_html=True) st.markdown("---") env_key = os.environ.get("GROQ_API_KEY", "") api_key = env_key if env_key else st.text_input("🔑 Groq API Key", type="password", placeholder="gsk_...") if not env_key and not api_key: st.caption("Free key → [console.groq.com](https://console.groq.com)") st.markdown("---") st.markdown("""

How it works
1. Paste the Job Description
2. Upload candidate resumes (PDF)
3. AI scores each resume 0–100
4. Candidates ranked automatically

Scoring Dimensions
• Overall fit score
• Skills match %
• Experience match %
• Education match %

""", unsafe_allow_html=True) # ─── Main UI ────────────────────────────────────────────────────────────────── st.markdown("""

🔍 AI Resume Screener

Upload a Job Description and multiple resumes — AI scores, ranks, and explains each candidate automatically

""", unsafe_allow_html=True) col_jd, col_resumes = st.columns([1, 1], gap="large") with col_jd: st.markdown("

Step 1 — Job Description

", unsafe_allow_html=True) jd_input = st.text_area( "Job Description", placeholder="Paste the full job description here including role, responsibilities, required skills, and qualifications...", height=320, label_visibility="collapsed" ) with col_resumes: st.markdown("

Step 2 — Upload Resumes (PDF)

", unsafe_allow_html=True) uploaded_resumes = st.file_uploader( "Upload Resumes", type=["pdf"], accept_multiple_files=True, label_visibility="collapsed" ) if uploaded_resumes: for r in uploaded_resumes: st.markdown(f"

📄 {r.name} · {round(r.size/1024,1)}KB

", unsafe_allow_html=True) st.markdown("") run_btn = st.button("🚀 Screen All Candidates", type="primary", use_container_width=True, disabled=not (jd_input and uploaded_resumes and api_key)) if not api_key: st.warning("👈 Add your Groq API key to get started.") elif not jd_input: st.info("📋 Paste the job description on the left to begin.") elif not uploaded_resumes: st.info("📂 Upload at least one resume PDF to begin.") if run_btn and jd_input and uploaded_resumes and api_key: results = [] progress = st.progress(0, text="Screening candidates...") for i, resume_file in enumerate(uploaded_resumes): candidate_name = resume_file.name.replace(".pdf", "").replace("_", " ").replace("-", " ").title() progress.progress(i / len(uploaded_resumes), text=f"Analyzing {candidate_name}...") with st.spinner(f"Evaluating {candidate_name}..."): try: resume_text = extract_pdf_text(resume_file.read()) result = score_resume(jd_input, resume_text, candidate_name, api_key) result["name"] = candidate_name result["filename"] = resume_file.name results.append(result) except Exception as e: st.error(f"❌ Error processing {candidate_name}: {str(e)}") progress.progress(1.0, text="Screening complete!") if results: # Sort by score results.sort(key=lambda x: x.get("score", 0), reverse=True) st.markdown("---") st.markdown("## 📊 Screening Results") # Summary stats avg_score = round(sum(r.get("score", 0) for r in results) / len(results)) top_score = results[0].get("score", 0) strong = sum(1 for r in results if r.get("score", 0) >= 70) st.markdown(f"""

{len(results)}

Candidates Screened

{top_score}

Top Score

{avg_score}

Average Score

{strong}

Strong Matches

""", unsafe_allow_html=True) # Ranked results for rank, result in enumerate(results, start=1): score = result.get("score", 0) rank_class = f"rank-{rank}" if rank <= 3 else "rank-other" score_class = "score-high" if score >= 70 else "score-mid" if score >= 50 else "score-low" rank_emoji = "🥇" if rank == 1 else "🥈" if rank == 2 else "🥉" if rank == 3 else f"#{rank}" skills_w = result.get("skills_match", 0) exp_w = result.get("experience_match", 0) edu_w = result.get("education_match", 0) strengths_html = "".join([f"✓ {s}" for s in result.get("strengths", [])]) gaps_html = "".join([f"✗ {g}" for g in result.get("gaps", [])]) with st.expander(f"{rank_emoji} {result['name']} — {score}/100 · {result.get('verdict', '')}", expanded=(rank <= 3)): st.markdown(f"""

Rank #{rank}

{result['name']}

📄 {result['filename']}

{score} / 100

{result.get("summary","")}

Skills Match

{skills_w}%

Experience Match

{exp_w}%

Education Match

{edu_w}%

✅ Strengths

{strengths_html}

⚠️ Gaps

{gaps_html}

💡 Recommendation: {result.get("recommendation","")}

""", unsafe_allow_html=True)