Update app.py

app.py

@@ -1,15 +1,11 @@
 import streamlit as st
-import torch
-from transformers import GPT2Tokenizer, GPT2LMHeadModel
-from summarizer import Summarizer
+from transformers import AutoTokenizer, AutoModelForCausalLM
+#from summarizer import Summarizer
 
-model=GPT2LMHeadModel.from_pretrained("DemocracyStudio/generate_nft_content")
-tokenizer=GPT2Tokenizer.from_pretrained("DemocracyStudio/generate_nft_content")
-summarize=Summarizer()
+tokenizer = AutoTokenizer.from_pretrained("DemocracyStudio/generate_nft_content")
+model = AutoModelForCausalLM.from_pretrained("DemocracyStudio/generate_nft_content")
 
-device = torch.device("cuda")
-model.cuda()
-model.to(device)
+#summarize=Summarizer()
 
 st.title("Text generation for the marketing content of NFTs")
 st.subheader("Course project 'NLP with transformers' at opencampus.sh, Spring 2022")
@@ -24,9 +20,7 @@ if choice == 'NFT':
 
     if st.button("Generate"):
         prompt = "<|startoftext|>"
-        generated = torch.tensor(tokenizer.encode(prompt)).unsqueeze(0)
-        generated = generated.to(device)
-        
+        generated = torch.tensor(tokenizer.encode(prompt)).unsqueeze(0)        
         sample_outputs = model.generate(
         generated,
         do_sample=True,