Update summary.py

summary.py

@@ -2,16 +2,28 @@ import torch
 import streamlit as st
 from transformers import PegasusForConditionalGeneration, AutoTokenizer
 
-model_name = 'google/pegasus-cnn_dailymail'
-device = 'cuda' if torch.cuda.is_available() else 'cpu'
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-model = PegasusForConditionalGeneration.from_pretrained(model_name).to(device)
+@st.cache(allow_output_mutation=True)
+def do_summary(model_name):
+    model = PegasusForConditionalGeneration.from_pretrained(model_name)
+    return model
+
+@st.cache(allow_output_mutation=True)
+def do_tokenize(model_name):
+    tokenizer = AutoTokenizer.from_pretrained(model_name)
+    return tokenizer
+
+model = do_summary("google/pegasus-cnn_dailymail")
+tokenizer = do_tokenize("google/pegasus-cnn_dailymail")
 
-# @st.cache(allow_output_mutation=True)
 
 def summarize(passage):
     txt = " ".join(passage)
+    #model_name = 'google/pegasus-cnn_dailymail'
+    device = 'cuda' if torch.cuda.is_available() else 'cpu'
+    #tokenizer = AutoTokenizer.from_pretrained(model_name)
+    #model = PegasusForConditionalGeneration.from_pretrained(model_name).to(device)
     batch = tokenizer(txt, truncation=True, padding='longest', return_tensors="pt").to(device)
     translated = model.generate(**batch)
     summy = tokenizer.batch_decode(translated, skip_special_tokens=True)
+    print("summ end")
     return summy