fix

app.py

@@ -76,7 +76,6 @@ llm_perf_dataset_repo = load_dataset_repo(LLM_PERF_DATASET_REPO, OPTIMUM_TOKEN)
 def get_benchmark_df(benchmark="Succeeded-1xA100-80GB"):
     if llm_perf_dataset_repo:
         llm_perf_dataset_repo.git_pull()
-
     # load data
     benchmark_df = pd.read_csv(f"./llm-perf-dataset/reports/{benchmark}.csv")
     clusters_df = pd.read_csv("./llm-perf-dataset/Clustered-Open-LLM-Leaderboard.csv")
@@ -99,22 +98,11 @@ def get_benchmark_df(benchmark="Succeeded-1xA100-80GB"):
     merged_df["quantization"] = merged_df["backend.quantization_strategy"].apply(
         lambda x: "BnB.4bit" if x == "bnb" else ("GPTQ.4bit" if x == "gptq" else "None")
     )
-
-    # # distance to 100% score
-    # score_distance = 100 - merged_df["best_score"]
-    # # distance to 0s latency
-    # latency_distance = merged_df["generate.latency(s)"]
-    # # distance to 0MB memory
-    # memory_distance = merged_df["forward.peak_memory(MB)"]
-    # # add perf distance
-    # merged_df["perf_distance"] = (
-    #     score_distance**2 + latency_distance**2 + memory_distance**2
-    # ) ** 0.5
-
     # sort
     merged_df.sort_values(by=SORTING_COLUMN, ascending=SORTING_ASCENDING, inplace=True)
     # drop duplicates
     merged_df.drop_duplicates(subset=NO_DUPLICATES_COLUMNS, inplace=True)
+    merged_df = merged_df[[*ALL_COLUMNS_DATATYPES, "generate.latency(s)"]]
     return merged_df