Update app.py

app.py

@@ -1,5 +1,5 @@
 import streamlit as st
-from transformers import AutoTokenizer, AutoModelForCausalLM
+from transformers import AutoTokenizer, AutoModelForCausalLM, AutoModel
 import torch
 
 first = """informal english: corn fields are all across illinois, visible once you leave chicago.\nTranslated into the Style of Abraham Lincoln: corn fields ( permeate illinois / span the state of illinois / ( occupy / persist in ) all corners of illinois / line the horizon of illinois / envelop the landscape of illinois ), manifesting themselves visibly as one ventures beyond chicago.\n\ninformal english: """
@@ -10,6 +10,7 @@ def get_model():
     #model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln21")
     #model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln40")
     #model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln41")
+    #model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln41")
     #model = AutoModelForCausalLM.from_pretrained("BigSalmon/GPT2InformalToFormalLincoln42")
     #model = AutoModelForCausalLM.from_pretrained("BigSalmon/Points3")
     #model = AutoModelForCausalLM.from_pretrained("BigSalmon/GPTNeo1.3BPointsLincolnFormalInformal")
@@ -21,8 +22,8 @@ def get_model():
     #model = AutoModelForCausalLM.from_pretrained("BigSalmon/MediumInformalToFormalLincoln4")
     #model = AutoModelForCausalLM.from_pretrained("BigSalmon/GPT2Neo1.3BPoints2")
     #model = AutoModelForCausalLM.from_pretrained("BigSalmon/GPT2Neo1.3BPoints3")
-    model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln41")
-    tokenizer = AutoTokenizer.from_pretrained("BigSalmon/Points2")
+    model = AutoModel.from_pretrained("facebook/opt-125m")
+    tokenizer = AutoTokenizer.from_pretrained("facebook/opt-125m")
     return model, tokenizer
     
 model, tokenizer = get_model()