Update src/streamlit_app.py

src/streamlit_app.py

@@ -50,7 +50,7 @@ st.markdown(
 )
 # ─── Load data ────────────────────────────────────────────────────────────────
 @st.cache_data
-def load_data(path="src/models.json"):
+def load_data(path):
     df = pd.read_json(path, lines=True)
     score_cols = [f"T{i}" for i in range(1, 12)]
     df["Avg"] = df[score_cols].mean(axis=1).round(1)
@@ -59,11 +59,6 @@ def load_data(path="src/models.json"):
         df[f"{col}_rank"] = df[col].rank(ascending=False, method="min").astype(int)
     return df
 
-df = load_data()
-
-# Precompute max ranks for color scaling
-score_cols = [f"T{i}" for i in range(1, 12)] + ["Avg"]
-max_ranks = {col: df[f"{col}_rank"].max() for col in score_cols}
 
 # one page description
 st.markdown("## Leaderboard")
@@ -73,6 +68,12 @@ tiers = ['F1', 'Accuracy']
 selected_tier = st.selectbox('Select metric:', tiers)
 
 if selected_tier == 'F1':
+    df = load_data("src/models.json")
+
+    # Precompute max ranks for color scaling
+    score_cols = [f"T{i}" for i in range(1, 12)] + ["Avg"]
+    max_ranks = {col: df[f"{col}_rank"].max() for col in score_cols}
+    
     # Build raw HTML table
     cols = ["Model"] + [f"T{i}" for i in range(1,12)] + ["Avg"]
     html = "<table style='border-collapse:collapse; width:100%; font-size:14px;'>"