Update app.py

app.py

@@ -1,8 +1,8 @@
 import streamlit as st
-from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer
+from transformers import pipeline, GPT2LMHeadModel, AutoTokenizer
 #from summarizer import Summarizer
 
-generate = pipeline(task='text-generation', model=AutoModelForCausalLM.from_pretrained("DemocracyStudio/generate_nft_content"), tokenizer=AutoTokenizer.from_pretrained("DemocracyStudio/generate_nft_content"))
+generate = pipeline(task='text-generation', model=GPT2LMHeadModel.from_pretrained("DemocracyStudio/generate_nft_content"), tokenizer=AutoTokenizer.from_pretrained("DemocracyStudio/generate_nft_content"))
 #summarize=Summarizer()
 
 st.title("Text generation for the marketing content of NFTs")
@@ -19,7 +19,8 @@ if choice == 'NFT':
     if st.button("Generate"):
         #st.text("Keywords: {}\n".format(keywords))
         #st.text("Length in number of words: {}\n".format(length))
-        st.text("This is your tailored blog article:", generate("", num_return_sequences=1))
+        generated_text = generate("", num_return_sequences=1)
+        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: