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import transformers
import streamlit as st
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import json
with open('testbook.json') as f:
test_book = json.load(f)
tokenizer = AutoTokenizer.from_pretrained("UNIST-Eunchan/bart-dnc-booksum")
def load_model(model_name):
model = AutoModelForSeq2SeqLM.from_pretrained("UNIST-Eunchan/bart-dnc-booksum")
return model
model = load_model("UNIST-Eunchan/bart-dnc-booksum")
def infer(input_ids, max_length, temperature, top_k, top_p):
output_sequences = model.generate(
input_ids=input_ids,
max_length=max_length,
temperature=temperature,
top_k=top_k,
top_p=top_p,
do_sample=True,
num_return_sequences=1,
num_beams=4,
no_repeat_ngram_size=2
)
return output_sequences
def chunking(book_text):
segments = []
#sentences, token_lens
current_segment = ""
total_token_lens = 0
for i in range(len(sentences)):
if total_token_lens < 512:
total_token_lens += token_lens[i]
current_segment += (sentences[i] + " ")
elif total_token_lens > 768:
segments.append(current_segment)
current_segment = sentences[i]
total_token_lens = token_lens[i]
else:
#make next_pseudo_segment
next_pseudo_segment = ""
next_token_len = 0
for t in range(30):
if (i+t < len(sentences)) and (next_token_len + token_lens[i+t] < 512):
next_token_len += token_lens[i+t]
next_pseudo_segment += sentences[i+t]
embs = model.encode([current_segment, next_pseudo_segment, sentences[i]]) # current, next, sent
if cos_similarity(embs[1],embs[2]) > cos_similarity(embs[0],embs[2]):
segments.append(current_segment)
current_segment = sentences[i]
total_token_lens = token_lens[i]
else:
total_token_lens += token_lens[i]
current_segment += (sentences[i] + " ")
return segments
chunked_segments = chunking(test_book[0]['book'])
'''
'''
#prompts
st.title("Book Summarization 📚")
st.write("The almighty king of text generation, GPT-2 comes in four available sizes, only three of which have been publicly made available. Feared for its fake news generation capabilities, it currently stands as the most syntactically coherent model. A direct successor to the original GPT, it reinforces the already established pre-training/fine-tuning killer duo. From the paper: Language Models are Unsupervised Multitask Learners by Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei and Ilya Sutskever.")
book_index = st.sidebar.slider("Select Book Example", value = 0,min_value = 0, max_value=4)
_book = test_book[book_index]['book']
chunked_segments = chunking(_book)
sent = st.text_area("Text", _book, height = 550)
max_length = st.sidebar.slider("Max Length", value = 512,min_value = 10, max_value=1024)
temperature = st.sidebar.slider("Temperature", value = 1.0, min_value = 0.0, max_value=1.0, step=0.05)
top_k = st.sidebar.slider("Top-k", min_value = 0, max_value=5, value = 0)
top_p = st.sidebar.slider("Top-p", min_value = 0.0, max_value=1.0, step = 0.05, value = 0.92)
for segment in range(len(chunked_segments)):
encoded_prompt = tokenizer.encode(segment, add_special_tokens=False, return_tensors="pt")
if encoded_prompt.size()[-1] == 0:
input_ids = None
else:
input_ids = encoded_prompt
output_sequences = infer(input_ids, max_length, temperature, top_k, top_p)
for generated_sequence_idx, generated_sequence in enumerate(output_sequences):
print(f"=== GENERATED SEQUENCE {generated_sequence_idx + 1} ===")
generated_sequences = generated_sequence.tolist()
# Decode text
text = tokenizer.decode(generated_sequence, clean_up_tokenization_spaces=True)
# Remove all text after the stop token
#text = text[: text.find(args.stop_token) if args.stop_token else None]
# Add the prompt at the beginning of the sequence. Remove the excess text that was used for pre-processing
total_sequence = (
sent + text[len(tokenizer.decode(encoded_prompt[0], clean_up_tokenization_spaces=True)) :]
)
generated_sequences.append(total_sequence)
print(total_sequence)
st.write(generated_sequences[-1])
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