Update app.py

app.py

@@ -4,26 +4,26 @@ import pandas as pd
 from math import isnan
 
 ROWS = [
-    {"Team_name":"Nguyen Quang Thao","vi-law-nli":0.5816,"vi-law-qa":0.8217,"vilaw-syllo":0.38,"Final Result":0.5944333333},
-    {"Team_name":"NHK","vi-law-nli":0.9333,"vi-law-qa":0.8683,"vilaw-syllo":0.3275,"Final Result":0.7097},
-    {"Team_name":"Innovation-LLM","vi-law-nli":0.9567,"vi-law-qa":0.8367,"vilaw-syllo":0.541666667,"Final Result":0.7783555556},
-    {"Team_name":"Bosch@AI Team","vi-law-nli":0.97,"vi-law-qa":0.9267,"vilaw-syllo":0.535833333,"Final Result":0.8108444444},
-    {"Team_name":"URAx","vi-law-nli":0.945,"vi-law-qa":0.8333,"vilaw-syllo":0.576666667,"Final Result":0.7849888889},
-    {"Team_name":"Abe","vi-law-nli":0.82,"vi-law-qa":0.84,"vilaw-syllo":0.2875,"Final Result":0.6491666667},
-    {"Team_name":"PSLV-Warrior","vi-law-nli":0.565,"vi-law-qa":0.0333,"vilaw-syllo":0.525,"Final Result":0.3744333333},
-    {"Team_name":"MinLegal","vi-law-nli":0.98,"vi-law-qa":0.8733,"vilaw-syllo":0.530833333,"Final Result":0.7947111111},
-    {"Team_name":"NLPhi","vi-law-nli":0.6517,"vi-law-qa":0.815,"vilaw-syllo":0.479166667,"Final Result":0.6486222222},
-    {"Team_name":"LICTU","vi-law-nli":0.8467,"vi-law-qa":0.8067,"vilaw-syllo":0.5375,"Final Result":0.7303},
+    {"Team_name":"Nguyen Quang Thao","vi-law-nli":0.5816,"vi-law-qa":0.8217,"vilaw-syllo":0.38,"Avg":0.5944333333},
+    {"Team_name":"NHK","vi-law-nli":0.9333,"vi-law-qa":0.8683,"vilaw-syllo":0.3275,"Avg":0.7097},
+    {"Team_name":"Innovation-LLM","vi-law-nli":0.9567,"vi-law-qa":0.8367,"vilaw-syllo":0.541666667,"Avg":0.7783555556},
+    {"Team_name":"Bosch@AI Team","vi-law-nli":0.97,"vi-law-qa":0.9267,"vilaw-syllo":0.535833333,"Avg":0.8108444444},
+    {"Team_name":"URAx","vi-law-nli":0.945,"vi-law-qa":0.8333,"vilaw-syllo":0.576666667,"Avg":0.7849888889},
+    {"Team_name":"Abe","vi-law-nli":0.82,"vi-law-qa":0.84,"vilaw-syllo":0.2875,"Avg":0.6491666667},
+    {"Team_name":"PSLV-Warrior","vi-law-nli":0.565,"vi-law-qa":0.0333,"vilaw-syllo":0.525,"Avg":0.3744333333},
+    {"Team_name":"MinLegal","vi-law-nli":0.98,"vi-law-qa":0.8733,"vilaw-syllo":0.530833333,"Avg":0.7947111111},
+    {"Team_name":"NLPhi","vi-law-nli":0.6517,"vi-law-qa":0.815,"vilaw-syllo":0.479166667,"Avg":0.6486222222},
+    {"Team_name":"LICTU","vi-law-nli":0.8467,"vi-law-qa":0.8067,"vilaw-syllo":0.5375,"Avg":0.7303},
 ]
 BASE_DF = pd.DataFrame(ROWS)
 
-NUM_COLS = ["vi-law-nli", "vi-law-qa", "vilaw-syllo", "Final Result"]
+NUM_COLS = ["vi-law-nli", "vi-law-qa", "vilaw-syllo", "Avg"]
 
 def _prep_df(df: pd.DataFrame) -> pd.DataFrame:
     out = df.copy()
     for c in NUM_COLS:
         out[c] = pd.to_numeric(out[c], errors="coerce").astype(float).round(6)
-    out = out.sort_values("Final Result", ascending=False, kind="mergesort").reset_index(drop=True)
+    out = out.sort_values("Avg", ascending=False, kind="mergesort").reset_index(drop=True)
     out.insert(0, "Rank", range(1, len(out)+1))
     return out
 
@@ -44,7 +44,7 @@ def _render_table(df: pd.DataFrame) -> str:
             f"<td>{_bar_html(row['vi-law-nli'])}</td>",
             f"<td>{_bar_html(row['vi-law-qa'])}</td>",
             f"<td>{_bar_html(row['vilaw-syllo'])}</td>",
-            f"<td>{_bar_html(row['Final Result'])}</td>",
+            f"<td>{_bar_html(row['Avg'])}</td>",
         ]
         rows_html.append(f"<tr>{''.join(tds)}</tr>")
     return f"<table class='lb-table'><thead><tr>{header}</tr></thead><tbody>{''.join(rows_html)}</tbody></table>"
@@ -56,11 +56,11 @@ def _filter_and_sort(search: str, quick: str):
         for t in terms:
             df = df[df["Team_name"].str.contains(t, case=False, na=False)]
     if quick == "Top 3":
-        df = df.sort_values("Final Result", ascending=False).head(3)
+        df = df.sort_values("Avg", ascending=False).head(3)
     elif quick == "Top 5":
-        df = df.sort_values("Final Result", ascending=False).head(5)
+        df = df.sort_values("Avg", ascending=False).head(5)
     else:
-        df = df.sort_values("Final Result", ascending=False)
+        df = df.sort_values("Avg", ascending=False)
     return _prep_df(df)
 
 def _controller(search, quick):