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import nltk | |
nltk.download('stopwords') | |
import pandas as pd | |
#classify_abs is a dependency for extract_abs | |
import classify_abs | |
import extract_abs | |
#pd.set_option('display.max_colwidth', None) | |
import streamlit as st | |
########## Title for the Web App ########## | |
st.title("Epidemiology Extraction Pipeline for Rare Diseases") | |
st.subheader("National Center for Advancing Translational Sciences (NIH/NCATS)") | |
#max_results is Maximum number of PubMed ID's to retrieve BEFORE filtering | |
max_results = st.sidebar.number_input("Maximum number of articles to find in PubMed", min_value=1, max_value=None, value=50) | |
filtering = st.sidebar.radio("What type of filtering would you like?",('Strict', 'Lenient', 'None')) | |
extract_diseases = st.sidebar.checkbox("Extract Rare Diseases", value=False) | |
with st.spinner('Loading Epidemiology Models and Dependencies...'): | |
classify_model_vars = classify_abs.init_classify_model() | |
st.success('Epidemiology Classification Model Loaded!') | |
NER_pipeline, entity_classes = extract_abs.init_NER_pipeline() | |
st.success('Epidemiology Extraction Model Loaded!') | |
GARD_dict, max_length = extract_abs.load_GARD_diseases() | |
st.success('All Models and Dependencies Loaded!') | |
GARD_Disease_Id = st.text_input("Input a rare disease term or GARD ID.", value="Fellman syndrome") | |
if text: | |
df = extract_abs.search_term_extraction(disease_or_gard_id, max_results, filtering, | |
NER_pipeline, entity_classes, | |
extract_diseases,GARD_dict, max_length, | |
classify_model_vars) | |
st.dataframe(df) | |
st.balloons() | |
#st.dataframe(data=None, width=None, height=None) | |
# st.code(body, language="python") |