Add some tqa results

.gitattributes

@@ -35,3 +35,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
 evaluation_p_np_metrics.csv filter=lfs diff=lfs merge=lfs -text
 qatch_logo.png filter=lfs diff=lfs merge=lfs -text
+concatenated_output_tqa.csv filter=lfs diff=lfs merge=lfs -text

app.py

@@ -32,6 +32,7 @@ import utilities as us
 #pnp_path = os.path.join("data", "evaluation_p_np_metrics.csv")
 pnp_path = "concatenated_output.csv"
 PATH_PKL_TABLES = 'tables_dict_beaver.pkl'
+PNP_TQA_PATH = 'concatenated_output_tqa.csv'
 js_func = """
 function refresh() {
     const url = new URL(window.location);
@@ -694,7 +695,8 @@ with gr.Blocks(theme='shivi/calm_seafoam', css_paths='style.css', js=js_func) as
             metrics_conc = pd.DataFrame()
             columns_to_visulize = ["db_path", "tbl_name", "test_category", "sql_tag", "query", "question", "predicted_sql", "time", "price", "answer"]
             if (input_data['input_method']=="default"):
-                target_df = us.load_csv(pnp_path)                 #target_df = us.load_csv("priority_non_priority_metrics.csv")
+                #target_df = us.load_csv(pnp_path)                 #target_df = us.load_csv("priority_non_priority_metrics.csv")
+                target_df = us.load_csv(pnp_path) if not flag_TQA else us.load_csv(PNP_TQA_PATH)
                 #predictions_dict = {model: pd.DataFrame(columns=target_df.columns) for model in model_list}
                 target_df = target_df[target_df["tbl_name"].isin(input_data['data']['selected_tables'])]
                 target_df = target_df[target_df["model"].isin(input_data['models'])]
@@ -1023,7 +1025,8 @@ with gr.Blocks(theme='shivi/calm_seafoam', css_paths='style.css', js=js_func) as
                 
                 if input_data["input_method"]=="default":  
                     global flag_TQA
-                    df = pd.read_csv(pnp_path)
+                    #df = pd.read_csv(pnp_path)
+                    df = us.load_csv(pnp_path) if not flag_TQA else us.load_csv(PNP_TQA_PATH)
                     df = df[df['model'].isin(input_data["models"])]
                     df = df[df['tbl_name'].isin(input_data["data"]["selected_tables"])]
                     
@@ -1033,7 +1036,7 @@ with gr.Blocks(theme='shivi/calm_seafoam', css_paths='style.css', js=js_func) as
                     df['model'] = df['model'].replace('llama-70', 'Llama-70B')
                     df['model'] = df['model'].replace('llama-8', 'Llama-8B')
                     df['test_category'] = df['test_category'].replace('many-to-many-generator', 'MANY-TO-MANY')
-                    if (flag_TQA) : flag_TQA = False #TODO delete after make pred
+                    #if (flag_TQA) : flag_TQA = False #TODO delete after make pred
                     return df
                 return metrics_df_out
             

concatenated_output_tqa.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ba5eeda2f030e9f23c43c25b6dee26f7afaf1b1488b796bb1bc9f901ab619aba
+size 18662081