Update evaluator_module.py

evaluator_module.py

@@ -167,60 +167,37 @@ class AetherScoreEvaluator:
         
         return result
     # Batch Evaluation # Input of JSON/CSV file
-   # def evaluate_batch(self, data: List[Dict], mode: str = "comprehensive") -> List[Dict]:
-   #      """Process a batch of evaluations in parallel."""
+   def evaluate_batch(self, data: List[Dict], mode: str = "comprehensive") -> List[Dict]:
+        """Process a batch of evaluations in parallel."""
         
-   #      results = []
+        results = []
 
-   #      # Get Item function
-   #      def process_item(item):
-   #          # Calling our Evalution function for Single prompt response pair
-   #          eval_result = self.evaluate_single(
-   #              prompt=item.get('prompt', ''),
-   #              response=item.get('response', ''),
-   #              expected_answer=item.get('expected_answer',''),
-   #              task_type=item.get('task_type', 'general')
-   #          )
-   #          # Combining with original metadata
-   #          eval_result.update({
-   #              'task_id': item.get('task_id', eval_result['scores']['eval_id']),
-   #              'agent_name': item.get('agent_name', 'Unknown'),
-   #          })
-   #          return eval_result
+        # Get Item function
+        def process_item(item):
+            # Calling our Evalution function for Single prompt response pair
+            eval_result = self.evaluate_single(
+                prompt=item.get('prompt', ''),
+                response=item.get('response', ''),
+                expected_answer=item.get('expected_answer',''),
+                task_type=item.get('task_type', 'general')
+            )
+            # Combining with original metadata
+            eval_result.update({
+                'task_id': item.get('task_id', eval_result['scores']['eval_id']),
+                'agent_name': item.get('agent_name', 'Unknown'),
+            })
+            return eval_result
         
-   #      with concurrent.futures.ThreadPoolExecutor() as executor:
-   #          future_to_item = {executor.submit(process_item, item): item for item in data}
-   #          for future in concurrent.futures.as_completed(future_to_item):
-   #              try:
-   #                  results.append(future.result())
-   #              except Exception as exc:
-   #                  print(f'An item generated an exception: {exc}')
+        with concurrent.futures.ThreadPoolExecutor() as executor:
+            future_to_item = {executor.submit(process_item, item): item for item in data}
+            for future in concurrent.futures.as_completed(future_to_item):
+                try:
+                    results.append(future.result())
+                except Exception as exc:
+                    print(f'An item generated an exception: {exc}')
 
-   #      return results
-    def evaluate_batch(self, data: List[Dict], mode: str = "comprehensive") -> List[Dict]:
-        """Process a batch of evaluations sequentially (no threading)."""
-        
-        results = []
-    
-        for item in data:
-            try:
-                # Evaluate a single prompt-response pair
-                eval_result = self.evaluate_single(
-                    prompt=item.get('prompt', ''),
-                    response=item.get('response', ''),
-                    expected_answer=item.get('expected_answer', ''),
-                    task_type=item.get('task_type', 'general')
-                )
-                # Combine with original metadata
-                eval_result.update({
-                    'task_id': item.get('task_id', eval_result['scores']['eval_id']),
-                    'agent_name': item.get('agent_name', 'Unknown'),
-                })
-                results.append(eval_result)
-            except Exception as exc:
-                print(f'An item generated an exception: {exc}')
-    
         return results
+ 
         
     # Instruction Following Evaluation (Prompt, Response)
     def _evaluate_instruction_following(self, prompt: str, response: str) -> Tuple[float, str]: