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| """Streamlit demo for the competition ranking pipeline. | |
| Run with: | |
| streamlit run sandbox/streamlit_app.py | |
| This app loads a sample of candidates from the JSONL, runs the | |
| deterministic ranking pipeline, and displays results interactively. | |
| """ | |
| import streamlit as st | |
| import pandas as pd | |
| import sys | |
| from pathlib import Path | |
| # Add project root to path | |
| project_root = Path(__file__).resolve().parent.parent | |
| sys.path.insert(0, str(project_root)) | |
| from src.ranker.io import load_candidates_jsonl | |
| from src.ranker.score import score_all, select_top_n | |
| from src.ranker.honeypot import detect_honeypot | |
| st.set_page_config( | |
| page_title="RankAI — Competition Demo", | |
| page_icon="🏆", | |
| layout="wide", | |
| ) | |
| st.title("🏆 RankAI — Candidate Ranking Demo") | |
| st.markdown( | |
| "Deterministic, zero-LLM candidate ranking for the **Senior AI Engineer** role at Redrob AI. " | |
| "Processes 100K candidates in ~37 seconds on CPU." | |
| ) | |
| # --- Sidebar --- | |
| st.sidebar.header("Configuration") | |
| max_candidates = st.sidebar.slider( | |
| "Candidates to load", min_value=100, max_value=10000, value=1000, step=100 | |
| ) | |
| top_n = st.sidebar.slider( | |
| "Top N to display", min_value=10, max_value=200, value=100, step=10 | |
| ) | |
| # --- Data loading --- | |
| DATA_DIR = ( | |
| project_root | |
| / "indiaruns" | |
| / "[PUB] India_runs_data_and_ai_challenge" | |
| / "India_runs_data_and_ai_challenge" | |
| ) | |
| JSONL_PATH = DATA_DIR / "candidates.jsonl" | |
| if not JSONL_PATH.exists(): | |
| st.error(f"Candidates file not found: {JSONL_PATH}") | |
| st.info("Place candidates.jsonl in the expected location.") | |
| st.stop() | |
| # --- Run pipeline --- | |
| with st.spinner("Loading candidates..."): | |
| candidates = list(load_candidates_jsonl(JSONL_PATH))[:max_candidates] | |
| st.success(f"Loaded {len(candidates):,} candidates") | |
| with st.spinner("Scoring candidates..."): | |
| scored = score_all(candidates) | |
| top = select_top_n(scored, n=top_n) | |
| st.success(f"Scored {len(scored):,} candidates, selected top {len(top)}") | |
| # --- Tabs --- | |
| tab_rank, tab_dist, tab_honeypot = st.tabs( | |
| ["📋 Rankings", "📊 Score Distribution", "🍯 Honeypot Analysis"] | |
| ) | |
| with tab_rank: | |
| st.subheader(f"Top {len(top)} Candidates") | |
| rows = [] | |
| for i, s in enumerate(top, start=1): | |
| rows.append({ | |
| "Rank": i, | |
| "Candidate ID": s.candidate_id, | |
| "Score": round(s.score, 4), | |
| "Name": s.name or "—", | |
| "Current Title": s.current_title or "—", | |
| "Honeypot": "⚠️" if s.is_honeypot else "✅", | |
| }) | |
| df = pd.DataFrame(rows) | |
| st.dataframe(df, use_container_width=True, hide_index=True) | |
| # Download button | |
| csv = df.to_csv(index=False) | |
| st.download_button( | |
| label="📥 Download CSV", | |
| data=csv, | |
| file_name="submission_preview.csv", | |
| mime="text/csv", | |
| ) | |
| with tab_dist: | |
| st.subheader("Score Distribution") | |
| all_scores = [s.score for s in scored] | |
| top_scores = [s.score for s in top] | |
| col1, col2 = st.columns(2) | |
| with col1: | |
| st.metric("All candidates", len(all_scores)) | |
| score_series = pd.Series(all_scores) | |
| bins = pd.cut(score_series, bins=30).value_counts().sort_index() | |
| st.bar_chart(bins) | |
| with col2: | |
| st.metric("Top candidates", len(top_scores)) | |
| st.metric("Min score (top)", f"{min(top_scores):.4f}") | |
| st.metric("Max score (top)", f"{max(top_scores):.4f}") | |
| st.metric("Mean score (all)", f"{score_series.mean():.4f}") | |
| st.metric("Std dev (all)", f"{score_series.std():.4f}") | |
| with tab_honeypot: | |
| st.subheader("Honeypot Detection Summary") | |
| honeypot_count = sum(1 for s in scored if s.is_honeypot) | |
| safe_count = len(scored) - honeypot_count | |
| col1, col2, col3 = st.columns(3) | |
| col1.metric("Total candidates", f"{len(scored):,}") | |
| col2.metric("Safe candidates", f"{safe_count:,}", delta=f"{100*safe_count/len(scored):.1f}%") | |
| col3.metric("Honeypots detected", f"{honeypot_count:,}", delta=f"{100*honeypot_count/len(scored):.1f}%", delta_color="inverse") | |
| st.markdown("**Honeypot Detectors:**") | |
| st.markdown(""" | |
| - 🕐 **Timeline Impossibility**: Overlapping full-time roles | |
| - 📊 **YOE Span Mismatch**: Career history doesn't cover declared experience | |
| - 🏅 **Expert Without Endorsements**: AI-heavy skills but no endorsements | |
| - 📈 **Title Seniority Inflation**: Senior title with <3 years experience | |
| - 🧩 **Skill Stuffing**: Non-technical role with 6+ AI keywords | |
| """) | |
| # --- Footer --- | |
| st.divider() | |
| st.caption( | |
| "RankAI — Deterministic candidate ranking. Zero LLM calls. ~37s for 100K candidates." | |
| ) | |