Update app.py

app.py

@@ -32,10 +32,15 @@ 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)