Create app.py

app.py

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+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")
+