Update app.py

app.py

@@ -46,8 +46,11 @@ def predict(input, history=[]):
     # tokenize the new input sentence
     new_user_input_ids = tokenizer.encode(input + tokenizer.eos_token, return_tensors='pt')
 
+     # Convert history tensor to a list
+    history_list = history.tolist() if isinstance(history, torch.Tensor) else history
+    
     # append the new user input tokens to the chat history
-    bot_input_ids = torch.cat([torch.LongTensor(history), new_user_input_ids], dim=-1)
+    bot_input_ids = torch.cat([torch.LongTensor(history_list), new_user_input_ids], dim=-1)
       
     # generate a response
     history = model.generate(bot_input_ids, max_length=1000, pad_token_id=tokenizer.eos_token_id).tolist()
@@ -60,15 +63,11 @@ def predict(input, history=[]):
 
     # Add the SQL model's response to the chat history
     history.extend(response_sql)
-
-    
+  
     # convert the tokens to text, and then split the responses into the right format
     response = tokenizer.decode(history[0]).split("<|endoftext|>")
     response = [(response[i], response[i + 1]) for i in range(0, len(response) - 1, 2)]  # convert to tuples of list
-    
 
-    
-    
     return response, history