Fix requirements

app.py

@@ -734,6 +734,8 @@ with gr.Blocks(theme='shivi/calm_seafoam', css_paths='style.css', js=js_func) as
                 metrics_conc = target_df    
                 if 'valid_efficency_score' not in metrics_conc.columns:
                     metrics_conc['valid_efficency_score'] = metrics_conc['VES']
+                if 'VES' not in metrics_conc.columns:
+                    metrics_conc['VES'] = metrics_conc['valid_efficency_score']
                 eval_text = generate_eval_text("End evaluation")
                 yield gr.Markdown(eval_text, visible=True), gr.Image(), gr.Markdown(), gr.Markdown(), gr.Markdown(), metrics_conc, *[predictions_dict[model][columns_to_visulize] for model in model_list]
             
@@ -868,14 +870,15 @@ with gr.Blocks(theme='shivi/calm_seafoam', css_paths='style.css', js=js_func) as
                             db_path_name="db_path"
                         )
                     else: 
+                        
                         metrics_df_model = us.evaluate_answer(predictions_dict[model])
                     metrics_df_model['model'] = model
                     metrics_conc = pd.concat([metrics_conc, metrics_df_model], ignore_index=True)
 
-                if 'valid_efficency_score' not in metrics_conc.columns and 'VES' in metrics_conc.columns:
+                if 'valid_efficency_score' not in metrics_conc.columns:
                     metrics_conc['valid_efficency_score'] = metrics_conc['VES']
 
-                if 'VES' not in metrics_conc.columns and 'valid_efficency_score' in metrics_conc.columns:
+                if 'VES' not in metrics_conc.columns:
                     metrics_conc['VES'] = metrics_conc['valid_efficency_score']
                 
                 eval_text = generate_eval_text("End evaluation")

requirements.txt

@@ -22,7 +22,7 @@ statistics>=1.0.3.5
 transformers>=4.49.0
 fsspec==2024.12.0
 pytest
-
+json
 # Docs dependencies
 mkdocs>=1.5.3
 mkdocs-material>=9.4.7

utilities.py

@@ -10,6 +10,7 @@ from qatch.evaluate_dataset.metrics_evaluators import CellPrecision, CellRecall,
 import qatch.evaluate_dataset.orchestrator_evaluator as eva
 import utils_get_db_tables_info
 #import tiktoken
+import json 
 from transformers import AutoTokenizer
 
 def extract_tables(file_path):