Update handler.py

handler.py

@@ -20,43 +20,46 @@ class EndpointHandler:
         except Exception as e:
             raise ValueError(f"Error during preprocessing: {str(e)}")
 
-def inference(self, text_input):
-    """
-    Perform inference using the BERTopic model.
-    - Combine all sentences into a single document and find shared topics.
-    """
-    try:
-        # Split text into sentences (assuming one sentence per line)
-        sentences = text_input.strip().split('\n')
-
-        # Combine all sentences into a single document
-        combined_document = " ".join(sentences)
-
-        # Perform topic inference on the combined document
-        topics, probabilities = self.topic_model.transform([combined_document])
-
-        # Prepare the results
-        results = []
-        for topic, prob in zip(topics, probabilities):
-            topic_info = self.topic_model.get_topic(topic)
-            topic_words = [word for word, _ in topic_info] if topic_info else []
-
-            # Get custom label for the topic
-            if hasattr(self.topic_model, "custom_labels_") and self.topic_model.custom_labels_ is not None:
-                custom_label = self.topic_model.custom_labels_[topic + 1]
-            else:
-                custom_label = f"Topic {topic}"  # Fallback label
-
-            results.append({
-                "topic": int(topic),
-                "probability": float(prob),
-                "top_words": topic_words[:5],  # Top 5 words
-                "customLabel": custom_label  # Add custom label
-            })
+    def inference(self, text_input):
+        """
+        Perform inference using the BERTopic model.
+        - Combine all sentences into a single document and find shared topics.
+        """
+        try:
+            # Split text into sentences (assuming one sentence per line)
+            sentences = text_input.strip().split('\n')
+    
+            # Combine all sentences into a single document
+            combined_document = " ".join(sentences)
+    
+            # Perform topic inference on the combined document
+            topics, probabilities = self.topic_model.transform([combined_document])
+    
+            # Prepare the results
+            results = []
+            for topic, prob in zip(topics, probabilities):
+                topic_info = self.topic_model.get_topic(topic)
+                topic_words = [word for word, _ in topic_info] if topic_info else []
+    
+                # Get custom label for the topic
+                if hasattr(self.topic_model, "custom_labels_") and self.topic_model.custom_labels_ is not None:
+                    custom_label = self.topic_model.custom_labels_[topic + 1]
+                else:
+                    custom_label = f"Topic {topic}"  # Fallback label
+    
+                results.append({
+                    "topic": int(topic),
+                    "probability": float(prob),
+                    "top_words": topic_words[:5],  # Top 5 words
+                    "customLabel": custom_label  # Add custom label
+                })
+    
+            return results
+        except Exception as e:
+            raise ValueError(f"Error during inference: {str(e)}")
 
-        return results
-    except Exception as e:
-        raise ValueError(f"Error during inference: {str(e)}")    def postprocess(self, results):
+    
+    def postprocess(self, results):
         """
         Postprocess the inference results into a JSON-serializable list.
         """