Update app.py

app.py

@@ -6,40 +6,38 @@ model=GPT2LMHeadModel.from_pretrained("DemocracyStudio/generate_nft_content")
 tokenizer=GPT2Tokenizer.from_pretrained("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.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)
+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}")
+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)")
 
-  else:
-    st.write("Topic not available yet")
+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")