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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/uc?id={ids[key]}" | |
gdown.download(url=url, output=f"model/{key}.ckpt") | |
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 Gen. (English)") | |
st.write("Question Generation, Question Answering and Questions/Answers Generation using Google MT5. ") | |
# Variables | |
# ids = {'mt5-small': st.secrets['model_key']} | |
ids = {'mt5-small': ''} | |
# Download all models from drive | |
# download_models(ids) | |
# Task selection | |
left, right = st.columns([4, 2]) | |
task = left.selectbox('', options=['Questions/Answers 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=0, help='Model to use. ') | |
model = load_model(model_path=f"model/{model_path}.ckpt") | |
right.write(model.device) | |
if task == 'Questions/Answers 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') | |
if split: | |
# Split into sentences | |
sent_tokenized = nltk.sent_tokenize(inputs) | |
res = {} | |
# 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: | |
# 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': answer, 'answer_bis': 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 | |
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 | |
predictions = model.qg(contexts, max_length=512) | |
# Answer area | |
content = {} | |
for pred, ans in zip(predictions, answers): | |
content[pred] = ans | |
st.write(content) | |