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import streamlit as st
import time
from transformers import pipeline
import torch
trust_remote_code=True
st.markdown('## story-generation 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)
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)
with col1:
prompt= st.text_area('Your prompt here',
'''in a world''')
with col2:
select_model = st.radio(
"Select the model to use:",
('StoryPy', 'null'), index = 0)
if select_model == 'StoryPy':
model = 'BreadAi/StoryPy'
elif select_model == 'null':
model = 'BreadAi/StoryPy'
elif select_model == 'MuseNeo':
model = 'breadlicker45/MuseNeo'
elif select_model == 'MusePy-1-1':
model = 'BreadAi/MusePy-1-1'
elif select_model == 'MuseCan':
model = 'BreadAi/MuseCan'
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)
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) |