Update app.py

app.py

@@ -32,15 +32,12 @@ if predictions[0,0] >= 0.4:
 
 readout = "This news is probably a "+ judge + f" one. The fake probability is {100*predictions[0,0]:.4f}%."
 
-#from transformers import AutoModelWithLMHead, AutoTokenizer
+from transformers import AutoModelWithLMHead, AutoTokenizer
 
-#tokenizer = AutoTokenizer.from_pretrained("mrm8488/t5-base-finetuned-summarize-news",use_fast=False)
-#model = AutoModelWithLMHead.from_pretrained("mrm8488/t5-base-finetuned-summarize-news")
+tokenizer = AutoTokenizer.from_pretrained("mrm8488/t5-base-finetuned-summarize-news",use_fast=False)
+model = AutoModelWithLMHead.from_pretrained("mrm8488/t5-base-finetuned-summarize-news")
 
-from transformers import AutoTokenizer, AutoModelForCausalLM
 
-tokenizer = AutoTokenizer.from_pretrained("pvduy/pythia-1B-sft-summarize-tldr")
-model = AutoModelForCausalLM.from_pretrained("pvduy/pythia-1B-sft-summarize-tldr")
 
 def summarize(text, max_length=150):
   input_ids = tokenizer.encode(text, return_tensors="pt", add_special_tokens=True)