| """Database dashboard — visual browse of the Awesome-Embodied&MM corpus (Streamlit). |
| |
| Read-only views over data/wam.db: headline metrics, research directions (chart), top scored |
| papers with their two-layer dimensions, the benchmark leaderboard, and influential authors. |
| """ |
|
|
| from __future__ import annotations |
|
|
| import json |
| import sqlite3 |
|
|
| import pandas as pd |
| import streamlit as st |
|
|
| from wam.config import Config |
|
|
| _WAM_TOP4 = [("inference_speed", "spd"), ("generalist", "gen"), |
| ("specialist", "spec"), ("inference_cost", "cost")] |
|
|
|
|
| def _conn(cfg: Config) -> sqlite3.Connection: |
| return sqlite3.connect(f"file:{cfg.path('db')}?mode=ro", uri=True) |
|
|
|
|
| def render(cfg: Config) -> None: |
| con = _conn(cfg) |
|
|
| counts = dict(con.execute("SELECT track, count(*) FROM papers GROUP BY track").fetchall()) |
| nbench = con.execute("SELECT count(*) FROM benchmarks").fetchone()[0] |
| nvar = con.execute("SELECT count(*) FROM model_variants").fetchone()[0] |
| nauth = con.execute("SELECT count(*) FROM authors").fetchone()[0] |
| c = st.columns(6) |
| c[0].metric("Core", counts.get("core", 0)) |
| c[1].metric("Adjacent", counts.get("adjacent", 0)) |
| c[2].metric("News", counts.get("news", 0)) |
| c[3].metric("Benchmark rows", nbench) |
| c[4].metric("Model variants", nvar) |
| c[5].metric("Authors", nauth) |
|
|
| |
| st.subheader("📈 Research directions") |
| fronts = pd.read_sql_query( |
| "SELECT name, size, momentum, summary FROM fronts WHERE snapshot_date=" |
| "(SELECT max(snapshot_date) FROM fronts) ORDER BY size DESC", con) |
| if not fronts.empty: |
| st.bar_chart(fronts.set_index("name")["size"], height=320) |
| st.dataframe(fronts, use_container_width=True, hide_index=True) |
| else: |
| st.info("No trend snapshot yet.") |
|
|
| |
| st.subheader("🏆 Top scored papers") |
| track = st.selectbox("Track", ["core", "adjacent", "all"], index=0) |
| where = "" if track == "all" else f"AND track='{track}'" |
| rows = con.execute( |
| f"SELECT id, title, track, published, relevance, scores_json, links_json FROM papers " |
| f"WHERE scores_json IS NOT NULL {where} " |
| f"ORDER BY json_extract(scores_json,'$.weighted_total') DESC LIMIT 200").fetchall() |
| recs = [] |
| for pid, title, trk, pub, rel, sj, lj in rows: |
| s = json.loads(sj or "{}") |
| wam = s.get("wam", {}) |
| links = json.loads(lj or "{}") |
| rec = {"score": s.get("weighted_total"), "title": title, "track": trk, |
| "published": pub} |
| for key, lbl in _WAM_TOP4: |
| v = wam.get(key) |
| rec[lbl] = v if isinstance(v, int) else None |
| rec["code"] = links.get("code") or "" |
| rec["abs"] = links.get("abs") or "" |
| recs.append(rec) |
| if recs: |
| df = pd.DataFrame(recs) |
| st.dataframe(df, use_container_width=True, hide_index=True, column_config={ |
| "abs": st.column_config.LinkColumn("abs"), |
| "code": st.column_config.LinkColumn("code")}) |
| st.caption("Score = weighted total. spd/gen/spec/cost = top-4 WAM dims (blank = N/A).") |
| else: |
| st.info("No scored papers for this track yet.") |
|
|
| |
| st.subheader("📊 Benchmark leaderboard") |
| bench = pd.read_sql_query( |
| "SELECT benchmark, task, model_name AS model, training_dataset AS trained_on, " |
| "metric_name AS metric, metric_value AS value, " |
| "CASE claimed_by_authors WHEN 1 THEN 'authors' ELSE '3rd-party' END AS source " |
| "FROM benchmarks WHERE metric_value IS NOT NULL", con) |
| if bench.empty: |
| st.info("No benchmark rows yet.") |
| else: |
| from wam.store.benchmarks import BENCH_FAMILIES, normalize_benchmark |
| bench["family"] = bench["benchmark"].map(lambda b: normalize_benchmark(b) or "(other)") |
| present = set(bench["family"]) |
| fams = ([f for f in BENCH_FAMILIES if f in present] |
| + sorted(present - set(BENCH_FAMILIES))) |
| c1, c2, c3 = st.columns([2, 1, 1]) |
| sel = c1.multiselect("Benchmark family", fams, |
| default=[f for f in BENCH_FAMILIES if f in fams]) |
| src = c2.selectbox("Source", ["all", "authors", "3rd-party"]) |
| query = c3.text_input("Model contains") |
| view = bench |
| if sel: |
| view = view[view["family"].isin(sel)] |
| if src != "all": |
| view = view[view["source"] == src] |
| if query: |
| view = view[view["model"].str.contains(query, case=False, na=False)] |
| view = view.sort_values(["family", "value"], ascending=[True, False]) |
| st.caption(f"{len(view)} of {len(bench)} rows · model identity = (model, training data) " |
| "· `authors` = self-reported. Click a column header to sort.") |
| st.dataframe(view[["family", "benchmark", "task", "model", "trained_on", "metric", |
| "value", "source"]], use_container_width=True, hide_index=True) |
|
|
| |
| st.subheader("👥 Influential authors") |
| arows = con.execute( |
| "SELECT name, region, affiliation, citations, h_index, paper_ids_json, directions, " |
| "s2_url FROM authors ORDER BY json_array_length(paper_ids_json) DESC").fetchall() |
| if arows: |
| adf = pd.DataFrame([{ |
| "author": n, "region": reg or "", "institution": aff or "", |
| "papers": len(json.loads(pj or "[]")), "citations": cit, "h_index": h, |
| "directions": (d or "")[:200], "s2": url or ""} |
| for n, reg, aff, cit, h, pj, d, url in arows]) |
| st.dataframe(adf, use_container_width=True, hide_index=True, |
| column_config={"s2": st.column_config.LinkColumn("s2")}) |
| else: |
| st.info("No authors yet.") |
|
|
| |
| st.subheader("📰 Embodied / physical-AI news") |
| nrows = con.execute("SELECT title, authors_json, published, links_json FROM papers " |
| "WHERE track='news' ORDER BY published DESC LIMIT 50").fetchall() |
| if nrows: |
| ndf = pd.DataFrame([{ |
| "date": pub or "", "outlet": (json.loads(aj or "[]") or [""])[0], "title": t, |
| "link": json.loads(lj or "{}").get("abs", "")} |
| for t, aj, pub, lj in nrows]) |
| st.dataframe(ndf, use_container_width=True, hide_index=True, |
| column_config={"link": st.column_config.LinkColumn("link")}) |
| else: |
| st.info("No news items yet.") |
|
|
