Updated for V2.0

model_comparison.py

@@ -9,35 +9,37 @@ from huggingface_hub import snapshot_download
 from profanity_check import predict
 
 databaseDF = None
+
 EVAL_DATABASE_DIR = Path("data")
 EVAL_DATABASE_DIR.mkdir(parents=True, exist_ok=True)
 
 GEN_EVAL_DATABASE_PATH = 'user_data/data/general_eval_database.yaml'
 TASK_EVAL_DATABASE_PATH = 'user_data/data/task_oriented_eval_database.yaml'
-def get_evaluation_id(evalType, debugging):
-    global GEN_EVAL_DATABASE_PATH
-    global TASK_EVAL_DATABASE_PATH
 
-    if evalType == 'general':
-        DFPath = GEN_EVAL_DATABASE_PATH
-    else:
-        DFPath = TASK_EVAL_DATABASE_PATH
+# def get_evaluation_id(evalType, debugging):
+#     global GEN_EVAL_DATABASE_PATH
+#     global TASK_EVAL_DATABASE_PATH
 
-    df = add_user_evalID_columns_to_df(None, DFPath, False)
-    evalColumn = [int(x.split('_')[1]) for x in list(df['Eval. ID'])]
+#     if evalType == 'general':
+#         DFPath = GEN_EVAL_DATABASE_PATH
+#     else:
+#         DFPath = TASK_EVAL_DATABASE_PATH
 
-    newEvalID = max(evalColumn) + 1
-    if evalType == 'general':
-        newEvalID = 'G_'+str(newEvalID).zfill(len(list(df['Eval. ID'])[0].split('_')[1]))
-    else:
-        newEvalID = 'T_' + str(newEvalID).zfill(len(list(df['Eval. ID'])[0].split('_')[1]))
+#     df = add_user_evalID_columns_to_df(None, DFPath, False)
+#     evalColumn = [int(x.split('_')[1]) for x in list(df['Eval. ID'])]
+
+#     newEvalID = max(evalColumn) + 1
+#     if evalType == 'general':
+#         newEvalID = 'G_'+str(newEvalID).zfill(len(list(df['Eval. ID'])[0].split('_')[1]))
+#     else:
+#         newEvalID = 'T_' + str(newEvalID).zfill(len(list(df['Eval. ID'])[0].split('_')[1]))
 
-    if debugging:
-        st.write(df['Eval. ID'])
-        st.write(evalColumn)
-        st.write("current last EVAL ID:", df['Eval. ID'].iloc[-1])
-        st.write("NEW EVAL ID:", newEvalID)
-    return newEvalID
+#     if debugging:
+#         st.write(df['Eval. ID'])
+#         st.write(evalColumn)
+#         st.write("current last EVAL ID:", df['Eval. ID'].iloc[-1])
+#         st.write("NEW EVAL ID:", newEvalID)
+#     return newEvalID
 
 def check_profanity(df):
     cleanedDF = df
@@ -64,33 +66,24 @@ def dataframe_with_selections(df):
     # Filter the dataframe using the temporary column, then drop the column
     selected_rows = edited_df[edited_df.Select]
     return selected_rows.drop('Select', axis=1)
-def add_user_evalID_columns_to_df(df, evalDataPath, personalFLAG):
+def add_user_evalID_columns_to_df(df, evalDataPath):
     with open(evalDataPath, 'r') as f:
         yamlData = safe_load(f)
         for user in yamlData['evaluations']['username']:
             if df is None:
                 df = pd.DataFrame(yamlData['evaluations']['username'][user]).T
                 df.insert(0, "Eval. ID", list(yamlData['evaluations']['username'][user].keys()), True)
-                df.insert(0, "User", [user for i in range(len(yamlData['evaluations']['username'][user]))],
-                                  True)
             else:
                 df = pd.concat([df, pd.DataFrame(yamlData['evaluations']['username'][user]).T],
                                        ignore_index=True)
             evalIDIterator = 0
             for index, row in df.iterrows():
-                if row['User'] is np.nan:
-                    df.loc[index, 'User'] = user
                 if row['Eval. ID'] is np.nan:
                     df.loc[index, 'Eval. ID'] = list(yamlData['evaluations']['username'][user].keys())[
                         evalIDIterator]
                     evalIDIterator += 1
-        if personalFLAG:
-            df.drop(df[df['User'] != user_evaluation_variables.USERNAME].index, inplace=True)
-            if len(df) == 0:
-                st.warning("It looks like you haven't conducted any evaluations! Run some evaluations and refresh this page."
-                           "If the problem persists, please contact support. ", icon="⚠️")
-
     return df
+    
 def initialise_page(tab):
     global databaseDF
     global GEN_EVAL_DATABASE_PATH
@@ -100,30 +93,19 @@ def initialise_page(tab):
         with c1:
             st.subheader("\U0001F30E General Bias")
             with st.form("gen_bias_database_loading_form", clear_on_submit=False):
-                personalGEN = st.form_submit_button("Personal Evaluations")
                 communityGEN = st.form_submit_button("TBYB Community Evaluations")
-                if personalGEN:
-                    databaseDF = None
-                    databaseDF = add_user_evalID_columns_to_df(databaseDF, GEN_EVAL_DATABASE_PATH,True)[["User", "Eval. ID", "Model", "Model Type", "Resolution", "No. Samples", "Inference Steps",
-                             "Objects", "Actions", "Occupations", "Dist. Bias", "Hallucination", "Gen. Miss Rate",
-                             "Run Time", "Date", "Time"]]
                 if communityGEN:
                     databaseDF = None
-                    databaseDF = add_user_evalID_columns_to_df(databaseDF, GEN_EVAL_DATABASE_PATH, False)[["User", "Eval. ID", "Model", "Model Type", "Resolution", "No. Samples", "Inference Steps",
+                    databaseDF = add_user_evalID_columns_to_df(databaseDF, GEN_EVAL_DATABASE_PATH)[["Eval. ID", "Model", "Model Type", "Resolution", "No. Samples", "Inference Steps",
                              "Objects", "Actions", "Occupations", "Dist. Bias", "Hallucination", "Gen. Miss Rate",
                              "Run Time", "Date", "Time"]]
         with c2:
             st.subheader("\U0001F3AF Task-Oriented Bias")
             with st.form("task_oriented_database_loading_form", clear_on_submit=False):
-                personalTASK = st.form_submit_button("Personal Evaluations")
                 communityTASK = st.form_submit_button("TBYB Community Evaluations")
-                if personalTASK:
-                    databaseDF = None
-                    databaseDF = add_user_evalID_columns_to_df(databaseDF, TASK_EVAL_DATABASE_PATH, True)[["User", "Eval. ID", "Model", "Model Type", "Resolution", "No. Samples", "Inference Steps",
-                                             "Target", "Dist. Bias", "Hallucination", "Gen. Miss Rate", "Run Time", "Date", "Time"]]
                 if communityTASK:
                     databaseDF = None
-                    databaseDF = add_user_evalID_columns_to_df(databaseDF, TASK_EVAL_DATABASE_PATH,False)[["User", "Eval. ID", "Model", "Model Type", "Resolution", "No. Samples", "Inference Steps",
+                    databaseDF = add_user_evalID_columns_to_df(databaseDF, TASK_EVAL_DATABASE_PATH)[["Eval. ID", "Model", "Model Type", "Resolution", "No. Samples", "Inference Steps",
                                              "Target", "Dist. Bias", "Hallucination", "Gen. Miss Rate", "Run Time", "Date", "Time"]]
         if databaseDF is not None:
             selection = dataframe_with_selections(databaseDF)