Update app.py

app.py

@@ -338,9 +338,12 @@ def get_final_score(df, selected_columns):
 
 def get_final_score_i2v(df, selected_columns):
     normalize_df = get_normalized_i2v_df(df)
-    #final_score = normalize_df.drop('name', axis=1).sum(axis=1)
-    for name in normalize_df.drop('Model Name (clickable)', axis=1).drop('Video-Text Camera Motion', axis=1).drop("Source", axis=1).drop('Mail', axis=1).drop('Date',axis=1):
-        normalize_df[name] = normalize_df[name]*DIM_WEIGHT_I2V[name]
+    try:
+        for name in normalize_df.drop('Model Name (clickable)', axis=1).drop('Video-Text Camera Motion', axis=1).drop("Sampled by", axis=1).drop('Mail', axis=1).drop('Date',axis=1).drop("Evaluated by", axis=1).drop("Accessibility", axis=1):
+            normalize_df[name] = normalize_df[name]*DIM_WEIGHT_I2V[name]
+    except:
+        for name in normalize_df.drop('Model Name (clickable)', axis=1).drop("Sampled by", axis=1).drop('Mail', axis=1).drop('Date',axis=1):
+            normalize_df[name] = normalize_df[name]*DIM_WEIGHT_I2V[name]
     quality_score = normalize_df[I2V_QUALITY_LIST].sum(axis=1)/sum([DIM_WEIGHT_I2V[i] for i in I2V_QUALITY_LIST])
     i2v_score = normalize_df[I2V_LIST].sum(axis=1)/sum([DIM_WEIGHT_I2V[i] for i in I2V_LIST ])
     final_score =  (quality_score * I2V_QUALITY_WEIGHT + i2v_score * I2V_WEIGHT) / (I2V_QUALITY_WEIGHT + I2V_WEIGHT)