remove clickable scores

app.py

@@ -21,7 +21,7 @@ COLUMNS_MAPPING = {
     "forward.peak_memory(MB)": "Peak Memory (MB) ⬇️",
     "generate.throughput(tokens/s)": "Throughput (tokens/s) ⬆️",
 }
-COLUMNS_DATATYPES = ["markdown", "str", "str", "markdown", "number", "number"]
+COLUMNS_DATATYPES = ["markdown", "str", "str", "number", "number", "number"]
 SORTING_COLUMN = ["Throughput (tokens/s) ⬆️"]
 
 
@@ -39,8 +39,8 @@ def get_benchmark_df(benchmark):
     scores_df = pd.read_csv(
         f"./llm-perf-dataset/reports/average_scores.csv")
     bench_df = bench_df.merge(scores_df, on="model", how="left")
-    bench_df["average"] = bench_df["average"].apply(
-        make_clickable_score)
+    # bench_df["average"] = bench_df["average"].apply(
+    #     make_clickable_score)
 
     # preprocess
     bench_df["model"] = bench_df["model"].apply(make_clickable_model)
@@ -72,16 +72,16 @@ def submit_query(text, backends, datatypes, threshold, raw_dfs):
     filtered_dfs = []
     for raw_df in raw_dfs:
         # extract the average score (float) from the clickable score (clickable markdown)
-        raw_df["Average H4 Score ⬆️"] = raw_df["Average H4 Score ⬆️"].apply(
-            extract_score_from_clickable)
+        # raw_df["Average H4 Score ⬆️"] = raw_df["Average H4 Score ⬆️"].apply(
+        #     extract_score_from_clickable)
         filtered_df = raw_df[
             raw_df["Model 🤗"].str.contains(text) &
             raw_df["Backend 🏭"].isin(backends) &
             raw_df["Datatype 📥"].isin(datatypes) &
             (raw_df["Average H4 Score ⬆️"] >= threshold)
         ]
-        filtered_df["Average H4 Score ⬆️"] = filtered_df["Average H4 Score ⬆️"].apply(
-            make_clickable_score)
+        # filtered_df["Average H4 Score ⬆️"] = filtered_df["Average H4 Score ⬆️"].apply(
+        #     make_clickable_score)
 
         filtered_dfs.append(filtered_df)