import streamlit as st import time from transformers import pipeline import torch trust_remote_code=True st.markdown('## Text-generation gpt Muse from Breadlicker45') use_auth_token=True @st.cache(allow_output_mutation=True, suppress_st_warning =True, show_spinner=False) def get_model(): return pipeline('text-generation', model=model, do_sample=True) col1, col2 = st.columns([2,1]) with st.sidebar: st.markdown('## Model Parameters') max_length = st.slider('Max text length', 80, 2000, 80) min_length = st.slider('Min text length', 80, 500, 80) num_beams = st.slider('N° tree beams search', 1, 15, 1) top_k = st.slider('top_k', 1, 10, 1) temperature = st.slider('temperature', 0.0, 1.0, 0.5, 0.1) early_stopping = st.selectbox( 'Early stopping text generation', ('True', 'False'), key={'True' : True, 'False': False}, index=0) no_ngram_repeat = st.slider('Max repetition limit', 1, 3, 1) st.markdown('## how to convert it into midi. go to this site https://mrcheeze.github.io/musenet-midi/ and then paste the numbers/musenet encoders you get from the ai into the big box and then click export midi') with col1: prompt= st.text_area('Your prompt here', '''2623 2619 3970 3976 2607 3973 2735 3973 2598 3985 2726 3973 2607 4009 2735 3973 2598 3973 2726 3973 2607 3973 2735 4009''') with col2: select_model = st.radio( "Select the model to use:", ('MusePy', 'MuseMini', 'MusePy-1-1', 'MuseCan', 'MuseCan-1-2'), index = 4) if select_model == 'MusePy': model = 'breadlicker45/MusePy' elif select_model == 'MuseNeo': model = 'BreadAi/MuseMini' elif select_model == 'MusePy-1-1': model = 'BreadAi/MusePy-1-1' elif select_model == 'MuseCan': model = 'BreadAi/MuseCan' elif select_model == 'MuseCan-1-2': model = 'BreadAi/MuseCan-1-2' with st.spinner('Loading Model... (This may take a while)'): generator = get_model() st.success('Model loaded correctly!') gen = st.info('Generating text...') answer = generator(prompt,max_length=max_length, no_repeat_ngram_size=no_ngram_repeat,early_stopping=early_stopping, num_beams=num_beams, min_length=min_length, temperature=temperature, top_k=top_k) gen.empty() lst = answer[0]['generated_text'] t = st.empty() for i in range(len(lst)): t.markdown("#### %s" % lst[0:i]) time.sleep(0.04)