Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,68 @@
-import altair as alt
-import numpy as np
-import pandas as pd
+"""Streamlit app to display the NPU Arabic leaderboard."""
+
+from __future__ import annotations
+
+import json
+from datetime import datetime
+from pathlib import Path
+from typing import List, Sequence
+
 import streamlit as st
 
-"""
-# Welcome to Streamlit!
-
-Edit `/streamlit_app.py` to customize this app to your heart's desire :heart:.
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
-
-In the meantime, below is an example of what you can do with just a few lines of code:
-"""
-
-num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
-num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
-
-indices = np.linspace(0, 1, num_points)
-theta = 2 * np.pi * num_turns * indices
-radius = indices
-
-x = radius * np.cos(theta)
-y = radius * np.sin(theta)
-
-df = pd.DataFrame({
-    "x": x,
-    "y": y,
-    "idx": indices,
-    "rand": np.random.randn(num_points),
-})
-
-st.altair_chart(alt.Chart(df, height=700, width=700)
-    .mark_point(filled=True)
-    .encode(
-        x=alt.X("x", axis=None),
-        y=alt.Y("y", axis=None),
-        color=alt.Color("idx", legend=None, scale=alt.Scale()),
-        size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
-    ))
+_DATA_PATH = Path("leaderboard.json")
+_COLUMNS: Sequence[str] = (
+    "model_name",
+    "avg_tps",
+    "iwslt2017-en-ar_sacrebleu",
+    "mlqa_ar_ar_f1",
+    "xquad_ar_f1",
+    "timestamp",
+)
+
+
+def _load_rows() -> List[dict]:
+    if not _DATA_PATH.exists():
+        return []
+    try:
+        raw = json.loads(_DATA_PATH.read_text(encoding="utf-8"))
+    except json.JSONDecodeError:
+        return []
+
+    if isinstance(raw, dict):
+        data = [raw]
+    elif isinstance(raw, list):
+        data = [item for item in raw if isinstance(item, dict)]
+    else:
+        data = []
+
+    # Filter to desired columns and sort newest-first.
+    filtered: List[dict] = []
+    for row in data:
+        compact = {key: row.get(key) for key in _COLUMNS}
+        filtered.append(compact)
+
+    def _sort_key(item: dict) -> tuple:
+        stamp = item.get("timestamp")
+        try:
+            return (datetime.fromisoformat(str(stamp)),)
+        except Exception:
+            return (datetime.min,)
+
+    filtered.sort(key=_sort_key, reverse=True)
+    return filtered
+
+
+st.set_page_config(page_title="Intel NPU Arabic Leaderboard", layout="wide")
+st.title("Intel® NPU Arabic Leaderboard")
+
+rows = _load_rows()
+if not rows:
+    st.info("No evaluations uploaded yet. Trigger a run to populate the leaderboard.")
+else:
+    st.write(
+        "Latest evaluation per model. Add new results by emailing the evaluation endpoint "
+        "or running the CLI with the Hugging Face publishing flags."
+    )
+    st.dataframe(rows, column_config={col: st.column_config.Column(col) for col in _COLUMNS})
+
+st.caption("Data auto-synced from leaderboard.json produced by the evaluation pipeline.")