Update evaluation.py

evaluation.py

@@ -203,15 +203,8 @@ def calculate_metrics(question, q_dataset, response, docs, time_taken):
         print(f"Overall RMSE: {overall_rmse}")
     else:
         print("Invalid RMSE calculation due to non-numeric values.")
-    '''    
-    if isinstance(predicted_metrics_rmse, (int, float)) and isinstance(ground_truth_metrics, (int, float)):
-        rmse_value = compute_rmse(predicted_metrics_rmse.values(), ground_truth_metrics.values())
-        predicted_metrics_rmse["rmse"] = rmse_value  # Adding RMSE to metrics
-    else:
-        predicted_metrics_rmse["rmse"] = "Invalid RMSE calculation"
-        
-     '''   
-     predicted_metrics = {
+
+    predicted_metrics = {
         "RAG_model_response": response,
         "ground_truth": ground_truth_answer,
         "context_relevance": context_relevance(question, docs),
@@ -222,6 +215,15 @@ def calculate_metrics(question, q_dataset, response, docs, time_taken):
         "ground truth completeness": ground_truth_completeness,
         "rmse": overall_rmse
     }
+    '''    
+    if isinstance(predicted_metrics_rmse, (int, float)) and isinstance(ground_truth_metrics, (int, float)):
+        rmse_value = compute_rmse(predicted_metrics_rmse.values(), ground_truth_metrics.values())
+        predicted_metrics_rmse["rmse"] = rmse_value  # Adding RMSE to metrics
+    else:
+        predicted_metrics_rmse["rmse"] = "Invalid RMSE calculation"
+        
+     '''   
+     
     return predicted_metrics
 
 ''' def retrieve_ground_truths(question, dataset):