app.py

import streamlit as st
import torch
from transformers import pipeline
from summarizer import Summarizer

pipeline = pipeline(task='text-generation', model="DemocracyStudio/generate_nft_content")
summarize=Summarizer()

def main():
  st.title("Text generation for the marketing content of NFTs")
  st.subheader("Course project 'NLP with transformers' at opencampus.sh, Spring 2022")
    
  st.sidebar.image("bayc crown.png", use_column_width=True)
  topics=["NFT", "Blockchain", "Metaverse"]
  choice = st.sidebar.selectbox("Select one topic", topics)
    
  if choice == 'NFT':
    keywords=st.text_area("Input 4 keywords here: (optional)")
    length=st.text_area("How long should be your text? (default: 512 words)")
    
    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,
      top_k=50,
      max_length = 512,
      top_p=0.95,
      num_return_sequences=1
      )
      for i, sample_output in enumerate(sample_outputs):
        generated_text = tokenizer.decode(sample_output,
        skip_special_tokens=True)
      
      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")