Spaces:
Build error
Build error
| import streamlit as st | |
| 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) | |
| st.title("Text generation for the marketing content of NFTs") | |
| st.sidebar.image("bayc crown.png", use_column_width=True) | |
| st.sidebar.write("image credits: bayc") | |
| topics=["NFT", "Blockchain", "Metaverse"] | |
| choice = st.sidebar.selectbox("Select one topic", topics) | |
| st.sidebar.write("Course project 'NLP with transformers' at opencampus.sh, Spring 2022") | |
| if choice == 'NFT': | |
| manual_input = st.text_area("Manual input: (optional)") | |
| #num_sequences = st.text_area("Number of sequences: (default: 1)") | |
| if st.button("Generate"): | |
| #st.text("Keywords: {}\n".format(keywords)) | |
| #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) | |
| else: | |
| st.write("Topic not available yet") | |