Update Gradio_en.py

Gradio_en.py

@@ -11,7 +11,6 @@ from collections import defaultdict
 from LLM import zero_shot
 from prompt_generate import generate_prompt_with_examples as generate_prompt
 
-# Global KNN retriever instance
 _knn_retriever = None
 
 class EntityLevelRetriever:
@@ -51,8 +50,7 @@ class EntityLevelRetriever:
     def build_index(self, train_path):
         with open(train_path, 'r', encoding='utf-8') as f:
             dataset = json.load(f)
-        
-        # Use data from index 500-999 (501-1000 in 1-based indexing)
+
         self.train_data = dataset[500:1000]
         
         for item in self.train_data:
@@ -77,8 +75,6 @@ class EntityLevelRetriever:
         if not self.train_data:
             return []
             
-        # Extract entities from query text (simplified approach)
-        # For better results, you might want to use NER or other entity extraction methods
         query_entities = self._extract_potential_entities(query_text)
         
         context_scores = defaultdict(float)
@@ -661,7 +657,7 @@ def create_interface():
                 
                 # QA event handling
                 def submit_qa_request(series, name, q_type, template, prompt, geo_text, question):
-                    # Use the selected model name directly (no custom name override)
+
                     return call_qa_model(series, name, prompt, geo_text, question, q_type)
                 
                 def update_qa_prompt_on_type_change(qa_type_value):