Update app.py

app.py

@@ -27,7 +27,7 @@ embeddings_text = embeddings_data["text_with_context"].tolist()
 
 #Importing the classifier/router (deberta)
 classifier_model = AutoModelForSequenceClassification.from_pretrained("AgentPublic/chatrag-deberta")
-tokenizer = AutoTokenizer.from_pretrained("AgentPublic/chatrag-deberta")
+classifier_tokenizer = AutoTokenizer.from_pretrained("AgentPublic/chatrag-deberta")
 
 #Importing the actual generative LLM (llama-based)
 model_name = "Pclanglais/Tchap"
@@ -40,7 +40,7 @@ system_prompt = "<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n\n
 #Function to guess whether we use the RAG or not.
 def classification_chatrag(query):
   print(query)
-  encoding = tokenizer(query, return_tensors="pt")
+  encoding = classifier_tokenizer(query, return_tensors="pt")
   encoding = {k: v.to(classifier_model.device) for k,v in encoding.items()}
 
   outputs = classifier_model(**encoding)