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import streamlit as st
import time
from transformers import pipeline
import torch
trust_remote_code=True
st.markdown('## Text-generation gpt-ya from Breadlicker45')
use_auth_token=True
tokenizer.padding_side = "left"
@st.cache(allow_output_mutation=True, suppress_st_warning =True, show_spinner=False)
def get_model():
return pipeline('text-generation', model=model, do_sample=False)
col1, col2 = st.columns([2,1])
with st.sidebar:
st.markdown('## Model Parameters')
max_length = st.slider('Max text length', 0, 500, 80)
num_beams = st.slider('N° tree beams search', 1, 15, 2)
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, 5, 2)
with col1:
prompt= st.text_area('Your prompt here',
'''What is the meaning of life?''')
with col2:
select_model = st.radio(
"Select the model to use:",
('gpt-ya', 'gpt-ya-1-1', 'gpt-ya-1-1-160M'), index = 2)
if select_model == 'gpt-ya':
model = 'breadlicker45/gpt-ya'
elif select_model == 'gpt-ya-1-1':
model = 'BreadAi/gpt-YA-1-1_70M'
elif select_model == 'gpt-ya-1-1-160M':
model = 'BreadAi/gpt-YA-1-1_160M'
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, do_sample=False)
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) |