Update handler.py

handler.py

@@ -20,42 +20,43 @@ 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.
-        - Process the text input and generate topic predictions.
-        """
-        try:
-            # Split text into documents (assuming one document per line)
-            docs = text_input.strip().split('\n')
+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')
 
-            # Perform topic inference
-            topics, probabilities = self.topic_model.transform(docs)
+        # Combine all sentences into a single document
+        combined_document = " ".join(sentences)
 
-            # 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 []
+        # Perform topic inference on the combined document
+        topics, probabilities = self.topic_model.transform([combined_document])
 
-                # Get custom label for the topic (with fallback if custom_labels_ is not available)
-                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
+        # 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 []
 
-                results.append({
-                    "topic": int(topic),
-                    "probability": float(prob),
-                    "top_words": topic_words[:5],  # Top 5 words
-                    "customLabel": custom_label  # Add custom label
-                })
+            # 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
 
-            return results
-        except Exception as e:
-            raise ValueError(f"Error during inference: {str(e)}")
+            results.append({
+                "topic": int(topic),
+                "probability": float(prob),
+                "top_words": topic_words[:5],  # Top 5 words
+                "customLabel": custom_label  # Add custom label
+            })
 
-    def postprocess(self, results):
+        return results
+    except Exception as e:
+        raise ValueError(f"Error during inference: {str(e)}")    def postprocess(self, results):
         """
         Postprocess the inference results into a JSON-serializable list.
         """