Last active
March 18, 2024 02:49
-
-
Save chetanambi/d54d83443df5f131c6bd0ca5dffa5742 to your computer and use it in GitHub Desktop.
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
import torch | |
import streamlit as st | |
from transformers import BartTokenizer, BartForConditionalGeneration | |
from transformers import T5Tokenizer, T5ForConditionalGeneration | |
st.title('Text Summarization Demo') | |
st.markdown('Using BART and T5 transformer model') | |
model = st.selectbox('Select the model', ('BART', 'T5')) | |
if model == 'BART': | |
_num_beams = 4 | |
_no_repeat_ngram_size = 3 | |
_length_penalty = 1 | |
_min_length = 12 | |
_max_length = 128 | |
_early_stopping = True | |
else: | |
_num_beams = 4 | |
_no_repeat_ngram_size = 3 | |
_length_penalty = 2 | |
_min_length = 30 | |
_max_length = 200 | |
_early_stopping = True | |
col1, col2, col3 = st.beta_columns(3) | |
_num_beams = col1.number_input("num_beams", value=_num_beams) | |
_no_repeat_ngram_size = col2.number_input("no_repeat_ngram_size", value=_no_repeat_ngram_size) | |
_length_penalty = col3.number_input("length_penalty", value=_length_penalty) | |
col1, col2, col3 = st.beta_columns(3) | |
_min_length = col1.number_input("min_length", value=_min_length) | |
_max_length = col2.number_input("max_length", value=_max_length) | |
_early_stopping = col3.number_input("early_stopping", value=_early_stopping) | |
text = st.text_area('Text Input') | |
def run_model(input_text): | |
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') | |
if model == "BART": | |
bart_model = BartForConditionalGeneration.from_pretrained("facebook/bart-base") | |
bart_tokenizer = BartTokenizer.from_pretrained("facebook/bart-base") | |
input_text = str(input_text) | |
input_text = ' '.join(input_text.split()) | |
input_tokenized = bart_tokenizer.encode(input_text, return_tensors='pt').to(device) | |
summary_ids = bart_model.generate(input_tokenized, | |
num_beams=_num_beams, | |
no_repeat_ngram_size=_no_repeat_ngram_size, | |
length_penalty=_length_penalty, | |
min_length=_min_length, | |
max_length=_max_length, | |
early_stopping=_early_stopping) | |
output = [bart_tokenizer.decode(g, skip_special_tokens=True, clean_up_tokenization_spaces=False) for g in | |
summary_ids] | |
st.write('Summary') | |
st.success(output[0]) | |
else: | |
t5_model = T5ForConditionalGeneration.from_pretrained("t5-base") | |
t5_tokenizer = T5Tokenizer.from_pretrained("t5-base") | |
input_text = str(input_text).replace('\n', '') | |
input_text = ' '.join(input_text.split()) | |
input_tokenized = t5_tokenizer.encode(input_text, return_tensors="pt").to(device) | |
summary_task = torch.tensor([[21603, 10]]).to(device) | |
input_tokenized = torch.cat([summary_task, input_tokenized], dim=-1).to(device) | |
summary_ids = t5_model.generate(input_tokenized, | |
num_beams=_num_beams, | |
no_repeat_ngram_size=_no_repeat_ngram_size, | |
length_penalty=_length_penalty, | |
min_length=_min_length, | |
max_length=_max_length, | |
early_stopping=_early_stopping) | |
output = [t5_tokenizer.decode(g, skip_special_tokens=True, clean_up_tokenization_spaces=False) for g in | |
summary_ids] | |
st.write('Summary') | |
st.success(output[0]) | |
if st.button('Submit'): | |
run_model(text) |
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment