Update app.py

app.py

@@ -7,6 +7,10 @@ model=GPT2LMHeadModel.from_pretrained("DemocracyStudio/generate_nft_content")
 tokenizer=GPT2Tokenizer.from_pretrained("DemocracyStudio/generate_nft_content")
 summarize=Summarizer()
 
+device = torch.device("cuda")
+model.cuda()
+model.to(device)
+
 st.title("Text generation for the marketing content of NFTs")
 st.subheader("Course project 'NLP with transformers' at opencampus.sh, Spring 2022")
     
@@ -21,6 +25,8 @@ if choice == 'NFT':
     if st.button("Generate"):
         prompt = "<|startoftext|>"
         generated = torch.tensor(tokenizer.encode(prompt)).unsqueeze(0)
+        generated = generated.to(device)
+        
         sample_outputs = model.generate(
         generated,
         do_sample=True,
@@ -31,11 +37,11 @@ if choice == 'NFT':
         )
     for i, sample_output in enumerate(sample_outputs):
         generated_text = tokenizer.decode(sample_output, skip_special_tokens=True)
+        summary = summarize(generated_text, num_sentences=1)
       
     #st.text("Keywords: {}\n".format(keywords))
     #st.text("Length in number of words: {}\n".format(length))
     st.text("This is your tailored blog article {generated_text}")
-    summary = summarize(generated_text, num_sentences=1)
     st.text("This is a tweet-sized summary of your article {summary}")
 else:
     st.write("Topic not available yet")