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import streamlit as st | |
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline | |
# from huggingface_hub import snapshot_download | |
page = st.sidebar.selectbox("Model ", ["Pretrained GPT2", "Finetuned on News data"]) | |
def load_model(model_name): | |
with st.spinner('Waiting for the model to load.....'): | |
# snapshot_download('flax-community/Sinhala-gpt2') | |
model = AutoModelForCausalLM.from_pretrained(model_name) | |
tokenizer = AutoTokenizer.from_pretrained(model_name) | |
st.success('Model loaded!!') | |
return model, tokenizer | |
seed = st.sidebar.text_input('Starting text', 'ආයුබෝවන්') | |
seq_num = st.sidebar.number_input('Number of sentences to generate ', 1, 20, 5) | |
max_len = st.sidebar.number_input('Length of the sentence ', 5, 300, 100) | |
if page == "Finetuned on News data": | |
st.title('Sinhala Text generation with Finetuned GPT2') | |
st.markdown('This model has been finetuned Sinhala-gpt2 model with 6000 news articles(~12MB)') | |
# seed = st.text_input('Starting text', 'ආයුබෝවන්') | |
# seq_num = st.number_input('Number of sentences to generate ', 1, 20, 5) | |
# max_len = st.number_input('Length of the sentence ', 5, 300, 100) | |
gen_news = st.button('Generate') | |
model, tokenizer = load_model('keshan/sinhala-gpt2-newswire') | |
if gen_news: | |
try: | |
with st.spinner('Generating...'): | |
generator = pipeline('text-generation', model=model, tokenizer=tokenizer) | |
seqs = generator(seed, max_length=max_len, num_return_sequences=seq_num) | |
st.write(seqs) | |
except Exception as e: | |
st.exception(f'Exception: {e}') | |
else: | |
st.title('Sinhala Text generation with GPT2') | |
st.markdown('A simple demo using Sinhala-gpt2 model trained during hf-flax week') | |
gen_gpt2 = st.button('Generate') | |
model, tokenizer = load_model('flax-community/Sinhala-gpt2') | |
if gen_gpt2: | |
try: | |
with st.spinner('Generating...'): | |
generator = pipeline('text-generation', model=model, tokenizer=tokenizer) | |
seqs = generator(seed, max_length=max_len, num_return_sequences=seq_num) | |
st.write(seqs) | |
except Exception as e: | |
st.exception(f'Exception: {e}') | |
st.markdown('____________') | |
st.markdown('by Keshan with Flax Community') | |