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import os
import gdown as gdown
import nltk
import streamlit as st
import torch
from transformers import AutoTokenizer
from mt5 import MT5
def download_models(ids):
"""
Download all models.
:param ids: name and links of models
:return:
"""
# Download sentence tokenizer
nltk.download('punkt')
# Download model from drive if not stored locally
for key in ids:
if not os.path.isfile(f"model/{key}.ckpt"):
url = f"https://drive.google.com/u/0/uc?id={ids[key]}"
gdown.download(url=url, output=f"model/{key}.ckpt")
@st.cache(allow_output_mutation=True)
def load_model(model_path):
"""
Load model and cache it.
:param model_path: path to model
:return:
"""
device = 'cuda' if torch.cuda.is_available() else 'cpu'
# Loading model and tokenizer
model = MT5.load_from_checkpoint(model_path).eval().to(device)
model.tokenizer = AutoTokenizer.from_pretrained('tokenizer')
return model
# Page config
st.set_page_config(layout="centered")
st.title("Questions/Answers Pairs Gen.")
st.write("Question Generation, Question Answering and Questions/Answers Generation using Google MT5. ")
# Variables
ids = {'mt5-small': st.secrets['small'],
'mt5-base': st.secrets['base']}
maintenance = True
if maintenance:
st.write("Unavailable for now (maintenance). ")
else:
# Download all models from drive
download_models(ids)
# Task selection
left, right = st.columns([4, 2])
task = left.selectbox('Choose the task: ',
options=['Questions/Answers Pairs Generation', 'Question Answering', 'Question Generation'],
help='Choose the task you want to try out')
# Model selection
model_path = right.selectbox('', options=[k for k in ids], index=1, help='Model to use. ')
model = load_model(model_path=f"model/{model_path}.ckpt")
right.write(model.device)
if task == 'Questions/Answers Pairs Generation':
# Input area
inputs = st.text_area('Context:', value="A few years after the First Crusade, in 1107, the Normans under "
"the command of Bohemond, Robert\'s son, landed in Valona and "
"besieged Dyrrachium using the most sophisticated military "
"equipment of the time, but to no avail. Meanwhile, they occupied "
"Petrela, the citadel of Mili at the banks of the river Deabolis, "
"Gllavenica (Ballsh), Kanina and Jericho. This time, "
"the Albanians sided with the Normans, dissatisfied by the heavy "
"taxes the Byzantines had imposed upon them. With their help, "
"the Normans secured the Arbanon passes and opened their way to "
"Dibra. The lack of supplies, disease and Byzantine resistance "
"forced Bohemond to retreat from his campaign and sign a peace "
"treaty with the Byzantines in the city of Deabolis. ", max_chars=2048,
height=250)
split = st.checkbox('Split into sentences', value=True)
if split:
# Split into sentences
sent_tokenized = nltk.sent_tokenize(inputs)
res = {}
with st.spinner('Please wait while the inputs are being processed...'):
# Iterate over sentences
for sentence in sent_tokenized:
predictions = model.multitask([sentence], max_length=512)
questions, answers, answers_bis = predictions['questions'], predictions['answers'], predictions[
'answers_bis']
# Build answer dict
content = {}
for question, answer, answer_bis in zip(questions[0], answers[0], answers_bis[0]):
content[question] = {'answer (extracted)': answer, 'answer (generated)': answer_bis}
res[sentence] = content
# Answer area
st.write(res)
else:
with st.spinner('Please wait while the inputs are being processed...'):
# Prediction
predictions = model.multitask([inputs], max_length=512)
questions, answers, answers_bis = predictions['questions'], predictions['answers'], predictions[
'answers_bis']
# Answer area
zip = zip(questions[0], answers[0], answers_bis[0])
content = {}
for question, answer, answer_bis in zip:
content[question] = {'answer (extracted)': answer, 'answer (generated)': answer_bis}
st.write(content)
elif task == 'Question Answering':
# Input area
inputs = st.text_area('Context:', value="A few years after the First Crusade, in 1107, the Normans under "
"the command of Bohemond, Robert\'s son, landed in Valona and "
"besieged Dyrrachium using the most sophisticated military "
"equipment of the time, but to no avail. Meanwhile, they occupied "
"Petrela, the citadel of Mili at the banks of the river Deabolis, "
"Gllavenica (Ballsh), Kanina and Jericho. This time, "
"the Albanians sided with the Normans, dissatisfied by the heavy "
"taxes the Byzantines had imposed upon them. With their help, "
"the Normans secured the Arbanon passes and opened their way to "
"Dibra. The lack of supplies, disease and Byzantine resistance "
"forced Bohemond to retreat from his campaign and sign a peace "
"treaty with the Byzantines in the city of Deabolis. ", max_chars=2048,
height=250)
question = st.text_input('Question:', value="What forced Bohemond to retreat from his campaign? ")
# Prediction
with st.spinner('Please wait while the inputs are being processed...'):
predictions = model.qa([{'question': question, 'context': inputs}], max_length=512)
answer = {question: predictions[0]}
# Answer area
st.write(answer)
elif task == 'Question Generation':
# Input area
inputs = st.text_area('Context (highlight answers with <hl> tokens): ',
value="A few years after the First Crusade, in <hl> 1107 <hl>, the <hl> Normans <hl> under "
"the command of <hl> Bohemond <hl>, Robert\'s son, landed in Valona and "
"besieged Dyrrachium using the most sophisticated military "
"equipment of the time, but to no avail. Meanwhile, they occupied "
"Petrela, <hl> the citadel of Mili <hl> at the banks of the river Deabolis, "
"Gllavenica (Ballsh), Kanina and Jericho. This time, "
"the Albanians sided with the Normans, dissatisfied by the heavy "
"taxes the Byzantines had imposed upon them. With their help, "
"the Normans secured the Arbanon passes and opened their way to "
"Dibra. The <hl> lack of supplies, disease and Byzantine resistance <hl> "
"forced Bohemond to retreat from his campaign and sign a peace "
"treaty with the Byzantines in the city of Deabolis. ", max_chars=2048,
height=250)
# Split by highlights
hl_index = [i for i in range(len(inputs)) if inputs.startswith('<hl>', i)]
contexts = []
answers = []
# Build a context for each highlight pair
for i in range(0, len(hl_index), 2):
contexts.append(inputs[:hl_index[i]].replace('<hl>', '') +
inputs[hl_index[i]: hl_index[i + 1] + 4] +
inputs[hl_index[i + 1] + 4:].replace('<hl>', ''))
answers.append(inputs[hl_index[i]: hl_index[i + 1] + 4].replace('<hl>', '').strip())
# Prediction
with st.spinner('Please wait while the inputs are being processed...'):
predictions = model.qg(contexts, max_length=512)
# Answer area
content = {}
for pred, ans in zip(predictions, answers):
content[pred] = ans
st.write(content)
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