updated to handle database inferencing

model_comparison.py

@@ -5,14 +5,23 @@ import plotly.express as px
 from yaml import safe_load
 import user_evaluation_variables
 databaseDF = None
+from pathlib import Path
+EVAL_DATABASE_DIR = Path("data")
+EVAL_DATABASE_DIR.mkdir(parents=True, exist_ok=True)
+
+GEN_EVAL_DATABASE_PATH = EVAL_DATABASE_DIR / f"general_eval_database.yaml"
+TASK_EVAL_DATABASE_PATH = EVAL_DATABASE_DIR / f"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 = './data/general_eval_database.yaml'
+        DFPath = GEN_EVAL_DATABASE_PATH
     else:
-        DFPath = './data/task_oriented_eval_database.yaml'
-    df = add_user_evalID_columns_to_df(None, DFPath,
-                                               False)
+        DFPath = 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'])]
 
     newEvalID = max(evalColumn) + 1
@@ -73,6 +82,8 @@ def add_user_evalID_columns_to_df(df, evalDataPath, personalFLAG):
     return df
 def initialise_page(tab):
     global databaseDF
+    global GEN_EVAL_DATABASE_PATH
+    global TASK_EVAL_DATABASE_PATH
     with tab:
         c1, c2 = st.columns(2)
         with c1:
@@ -82,12 +93,12 @@ def initialise_page(tab):
                 communityGEN = st.form_submit_button("TBYB Community Evaluations")
                 if personalGEN:
                     databaseDF = None
-                    databaseDF = add_user_evalID_columns_to_df(databaseDF, './data/general_eval_database.yaml',True)[["User", "Eval. ID", "Model", "Model Type", "Resolution", "No. Samples", "Inference Steps",
+                    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, './data/general_eval_database.yaml', False)[["User", "Eval. ID", "Model", "Model Type", "Resolution", "No. Samples", "Inference Steps",
+                    databaseDF = add_user_evalID_columns_to_df(databaseDF, GEN_EVAL_DATABASE_PATH, False)[["User", "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:
@@ -97,11 +108,11 @@ def initialise_page(tab):
                 communityTASK = st.form_submit_button("TBYB Community Evaluations")
                 if personalTASK:
                     databaseDF = None
-                    databaseDF = add_user_evalID_columns_to_df(databaseDF, './data/task_oriented_eval_database.yaml', True)[["User", "Eval. ID", "Model", "Model Type", "Resolution", "No. Samples", "Inference Steps",
+                    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, './data/task_oriented_eval_database.yaml',False)[["User", "Eval. ID", "Model", "Model Type", "Resolution", "No. Samples", "Inference Steps",
+                    databaseDF = add_user_evalID_columns_to_df(databaseDF, TASK_EVAL_DATABASE_PATH,False)[["User", "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)