Update app.py

app.py

@@ -1,11 +1,8 @@
 import streamlit as st
-import torch
-from transformers import AutoTokenizer, AutoModelForCausalLM
+from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer
 #from summarizer import Summarizer
 
-tokenizer = AutoTokenizer.from_pretrained("DemocracyStudio/generate_nft_content")
-model = AutoModelForCausalLM.from_pretrained("DemocracyStudio/generate_nft_content")
-
+generate = pipeline(task='text-generation', model=AutoModelForCausalLM.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")
@@ -20,12 +17,10 @@ if choice == 'NFT':
     length=st.text_area("How long do you want your text to be? (default: 512 words)")
 
     if st.button("Generate"):
-        generated_text = model("")
-        #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)
+        st.text("This is your tailored blog article:", generate("", num_return_sequences=1))
+        #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")