Update app.py

app.py

@@ -1,9 +1,8 @@
 import streamlit as st
-from transformers import pipeline, GPT2LMHeadModel, AutoTokenizer, SummarizationPipeline, AutoModelWithLMHead
+from transformers import pipeline, GPT2LMHeadModel, AutoTokenizer#, SummarizationPipeline, AutoModelWithLMHead
 
 generate = pipeline(task='text-generation', model=GPT2LMHeadModel.from_pretrained("DemocracyStudio/generate_nft_content"), tokenizer=AutoTokenizer.from_pretrained("DemocracyStudio/generate_nft_content"))
-summarize = SummarizationPipeline(model=AutoModelWithLMHead.from_pretrained("SEBIS/code_trans_t5_small_program_synthese_transfer_learning_finetune"),
-    tokenizer=AutoTokenizer.from_pretrained("SEBIS/code_trans_t5_small_program_synthese_transfer_learning_finetune", skip_special_tokens=True),device=0)
+#summarize = SummarizationPipeline(model=AutoModelWithLMHead.from_pretrained("SEBIS/code_trans_t5_small_program_synthese_transfer_learning_finetune"),tokenizer=AutoTokenizer.from_pretrained("SEBIS/code_trans_t5_small_program_synthese_transfer_learning_finetune", skip_special_tokens=True),device=0)
 
 st.title("Text generation for the marketing content of NFTs")
     
@@ -21,8 +20,8 @@ if choice == 'NFT':
         #st.text("Length in number of words: {}\n".format(length))
         generated = generate(manual_input, max_length = 512, num_return_sequences=1)
         st.write(generated)
-        tweet = summarize(generated)
-        st.write(tweet)
+        #tweet = summarize(generated)
+        #st.write(tweet)
 else:
     st.write("Topic not available yet")