added composite score sorting

app.py

@@ -36,6 +36,7 @@ COLUMNS_MAPPING = {
     "forward.peak_memory(MB)": "Peak Memory (MB) ⬇️",
     "score": "Average Open LLM Score ⬆️",
     #
+    "composite_score": "Composite Score ⬆️",
     "num_params": "#️⃣ Parameters 📏",
 }
 COLUMNS_DATATYPES = [
@@ -92,8 +93,13 @@ def get_benchmark_table(bench_df):
     bench_df = bench_df[list(COLUMNS_MAPPING.keys())]
     # rename
     bench_df.rename(columns=COLUMNS_MAPPING, inplace=True)
-    # sort
-    bench_df.sort_values(by=SORTING_COLUMN, ascending=False, inplace=True)
+    # sort based on composite score made by adding score, -latency, -memory each normalized to values between 0 and 1
+    normalized_score =(bench_df["score"]-bench_df["score"].min())/(bench_df["score"].max()-bench_df["score"].min())
+    normalized_latency = (bench_df["generate.latency(s)"].max()-bench_df["generate.latency(s)"])/(bench_df["generate.latency(s)"].max()-bench_df["generate.latency(s)"].min())
+    normalized_memory = (bench_df["forward.peak_memory(MB)"].max()-bench_df["forward.peak_memory(MB)"])/(bench_df["forward.peak_memory(MB)"].max()-bench_df["forward.peak_memory(MB)"].min())
+    bench_df["composite_score"] = normalized_score + normalized_latency + normalized_memory
+    bench_df.sort_values(by=["composite_score"], ascending=False, inplace=True)
+    
     # transform
     bench_df["Model 🤗"] = bench_df["Model 🤗"].apply(make_clickable_model)
     bench_df["#️⃣ Parameters 📏"] = bench_df["#️⃣ Parameters 📏"].apply(num_to_str)