Update app.py

app.py

@@ -118,6 +118,35 @@ def get_final_score(df, selected_columns):
         df.insert(1, 'Selected Score', selected_score)
     return df
 
+
+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)
+    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)
+    if 'Selected Score' in df:
+        df['Selected Score'] = selected_score
+    else:
+        df.insert(1, 'Selected Score', selected_score)
+    return df
+
 def get_baseline_df():
     submission_repo = Repository(local_dir=SUBMISSION_NAME, clone_from=SUBMISSION_URL, use_auth_token=HF_TOKEN, repo_type="dataset")
     submission_repo.git_pull()