Update app.py

app.py

@@ -122,26 +122,13 @@ def get_final_score(df, selected_columns):
 
 def get_final_score_quality(df, selected_columns):
     normalize_df = get_normalized_df(df)
-    #final_score = normalize_df.drop('name', axis=1).sum(axis=1)
-    # for name in normalize_df.drop('Model Name (clickable)', axis=1):
-    #     normalize_df[name] = normalize_df[name]*DIM_WEIGHT[name]
-    quality_score = normalize_df[QUALITY_LIST].sum(axis=1) / len(QUALITY_LIST)
-    # quality_score = normalize_df[QUALITY_LIST].sum(axis=1)/sum([DIM_WEIGHT[i] for i in QUALITY_LIST])
-    # semantic_score = normalize_df[SEMANTIC_LIST].sum(axis=1)/sum([DIM_WEIGHT[i] for i in SEMANTIC_LIST ])
-    final_score =  (quality_score * QUALITY_WEIGHT + semantic_score * SEMANTIC_WEIGHT) / (QUALITY_WEIGHT + SEMANTIC_WEIGHT)
-    if 'Total Score' in df:
-        df['Total Score'] = final_score
-    else:
-        df.insert(1, 'Total Score', final_score)
-    if 'Semantic Score' in df:
-        df['Semantic Score'] = semantic_score
-    else:
-        df.insert(2, 'Semantic Score', semantic_score)
+    quality_score = normalize_df[QUALITY_TAB].sum(axis=1) / len(QUALITY_TAB)
+
     if 'Quality Score' in df:
         df['Quality Score'] = quality_score
     else:
-        df.insert(3, 'Quality Score', quality_score)
-    selected_score = calculate_selected_score(normalize_df, selected_columns)
+        df.insert(1, 'Quality Score', quality_score)
+    selected_score = normalize_df[selected_columns].sum(axis=1) / len(selected_columns)
     if 'Selected Score' in df:
         df['Selected Score'] = selected_score
     else:
@@ -162,8 +149,8 @@ def get_baseline_df():
 def get_baseline_df_quality():
     submission_repo = Repository(local_dir=SUBMISSION_NAME, clone_from=SUBMISSION_URL, use_auth_token=HF_TOKEN, repo_type="dataset")
     submission_repo.git_pull()
-    df = pd.read_csv(CSV_DIR)
-    df = get_final_score(df, checkbox_group.value)
+    df = pd.read_csv(QUALITY_DIR)
+    df = get_final_score_quality(df, checkbox_group_quality.value)
     df = df.sort_values(by="Selected Score", ascending=False)
     present_columns = MODEL_INFO_TAB_QUALITY + checkbox_group_quality.value
     df = df[present_columns]
@@ -220,7 +207,7 @@ def on_filter_model_size_method_change(selected_columns):
     return filter_component#.value
 
 def on_filter_model_size_method_change_quality(selected_columns):
-    updated_data = get_all_df(selected_columns)
+    updated_data = get_baseline_df_quality()
     #print(updated_data)
     # columns:
     selected_columns = [item for item in QUALITY_TAB if item in selected_columns]