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import streamlit as st
import torch
import spacy
# from spacy.lang.en import English
# from utils import spacy_function, make_predictions, example_input
from Dataset import SkimlitDataset
from Embeddings import get_embeddings
from Model import SkimlitModel
from Tokenizer import Tokenizer
from LabelEncoder import LabelEncoder
from MakePredictions import make_skimlit_predictions
from RandomAbstract import Choose_Random_text
MODEL_PATH = 'skimlit-model-final-1.pt'
TOKENIZER_PATH = 'tokenizer.json'
LABEL_ENOCDER_PATH = "label_encoder.json"
EMBEDDING_FILE_PATH = 'glove.6B.300d.txt'
@st.cache()
def create_utils(model_path, tokenizer_path, label_encoder_path, embedding_file_path):
tokenizer = Tokenizer.load(fp=tokenizer_path)
label_encoder = LabelEncoder.load(fp=label_encoder_path)
embedding_matrix = get_embeddings(embedding_file_path, tokenizer, 300)
model = SkimlitModel(embedding_dim=300, vocab_size=len(tokenizer), hidden_dim=128, n_layers=3, linear_output=128, num_classes=len(label_encoder), pretrained_embeddings=embedding_matrix)
model.load_state_dict(torch.load(model_path, map_location='cpu'))
print(model)
return model, tokenizer, label_encoder
def model_prediction(abstract, model, tokenizer, label_encoder):
objective = ''
background = ''
method = ''
conclusion = ''
result = ''
lines, pred = make_skimlit_predictions(abstract, model, tokenizer, label_encoder)
# pred, lines = make_predictions(abstract)
for i, line in enumerate(lines):
if pred[i] == 'OBJECTIVE':
objective = objective + line
elif pred[i] == 'BACKGROUND':
background = background + line
elif pred[i] == 'METHODS':
method = method + line
elif pred[i] == 'RESULTS':
result = result + line
elif pred[i] == 'CONCLUSIONS':
conclusion = conclusion + line
return objective, background, method, conclusion, result
def main():
st.set_page_config(
page_title="SkimLit",
page_icon="📄",
layout="wide",
initial_sidebar_state="expanded"
)
st.title('SkimLit📄🔥')
st.caption('An NLP model to classify medical abstract sentences into the role they play (e.g. objective, methods, results, etc..) to enable researchers to skim through the literature and dive deeper when necessary.')
# creating model, tokenizer and labelEncoder
# if PREP_MODEL:
# skimlit_model, tokenizer, label_encoder = create_utils(MODEL_PATH, TOKENIZER_PATH, LABEL_ENOCDER_PATH, EMBEDDING_FILE_PATH)
# PREP_MODEL = False
col1, col2 = st.columns(2)
with col1:
st.write('#### Entre Abstract Here !!')
abstract = st.text_area(label='', height=200)
agree = st.checkbox('Show Example Abstract')
predict = st.button('Extract !')
if agree:
example_input = Choose_Random_text()
st.info(example_input)
# make prediction button logic
if predict:
with col2:
with st.spinner('Wait for prediction....'):
skimlit_model, tokenizer, label_encoder = create_utils(MODEL_PATH, TOKENIZER_PATH, LABEL_ENOCDER_PATH, EMBEDDING_FILE_PATH)
objective, background, methods, conclusion, result = model_prediction(abstract, skimlit_model, tokenizer, label_encoder)
st.markdown(f'### Objective : ')
st.info(objective)
# st.write(f'{objective}')
st.markdown(f'### Background : ')
st.info(background)
# st.write(f'{background}')
st.markdown(f'### Methods : ')
st.info(methods)
# st.write(f'{methods}')
st.markdown(f'### Result : ')
st.info(result)
# st.write(f'{result}')
st.markdown(f'### Conclusion : ')
st.info(conclusion)
# st.write(f'{conclusion}')
if __name__=='__main__':
main() |