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999ecf4
Update app.py
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app.py
CHANGED
@@ -21,18 +21,7 @@ disease_or_gard_id = st.text_input('Input a rare disease term or a GARD ID.', 'F
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# st.code(body, language="python")
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#LSTM RNN Epi Classifier Model
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classify_model_vars = classify_abs.init_classify_model()
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#GARD Dictionary - For filtering and exact match disease/GARD ID identification
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GARD_dict, max_length = extract_abs.load_GARD_diseases()
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#BioBERT-based NER pipeline, open `entities` to see
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NER_pipeline, entity_classes = extract_abs.init_NER_pipeline()
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#max_results is Maximum number of PubMed ID's to retrieve BEFORE filtering
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max_results = st.sidebar.number_input(label, min_value=1, max_value=None, value=50)
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# https://docs.streamlit.io/library/api-reference/widgets/st.number_input
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# st.radio(label, options, index=0, format_func=special_internal_function, key=None, help=None, on_change=None, args=None, kwargs=None, *, disabled=False)
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# https://docs.streamlit.io/library/api-reference/widgets/st.radio
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@@ -43,6 +32,26 @@ filtering = st.sidebar.radio(
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extract_diseases = st.sidebar.checkbox("Extract Rare Diseases", value=False)
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# https://docs.streamlit.io/library/api-reference/widgets/st.checkbox
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#filtering options are 'strict','lenient'(default), 'none'
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if text:
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df = extract_abs.search_term_extraction(disease_or_gard_id, max_results, filtering,
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# st.code(body, language="python")
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# st.radio(label, options, index=0, format_func=special_internal_function, key=None, help=None, on_change=None, args=None, kwargs=None, *, disabled=False)
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# https://docs.streamlit.io/library/api-reference/widgets/st.radio
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extract_diseases = st.sidebar.checkbox("Extract Rare Diseases", value=False)
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# https://docs.streamlit.io/library/api-reference/widgets/st.checkbox
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#LSTM RNN Epi Classifier Model
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with st.spinner('Loading Epidemiology Classification Model...'):
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classify_model_vars = classify_abs.init_classify_model()
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st.success('Epidemiology Classification Model Loaded!')
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#GARD Dictionary - For filtering and exact match disease/GARD ID identification
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with st.spinner('Loading GARD Rare Disease Dictionary...'):
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GARD_dict, max_length = extract_abs.load_GARD_diseases()
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st.success('GARD Rare Disease Dictionary Loaded!')
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#BioBERT-based NER pipeline, open `entities` to see
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with st.spinner('Loading Epidemiology Extraction Model...'):
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NER_pipeline, entity_classes = extract_abs.init_NER_pipeline()
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st.success('Epidemiology Extraction Model Loaded!')
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#max_results is Maximum number of PubMed ID's to retrieve BEFORE filtering
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max_results = st.sidebar.number_input(label, min_value=1, max_value=None, value=50)
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# https://docs.streamlit.io/library/api-reference/widgets/st.number_input
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#filtering options are 'strict','lenient'(default), 'none'
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if text:
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df = extract_abs.search_term_extraction(disease_or_gard_id, max_results, filtering,
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