import nltk
nltk.data.path.append("/home/user/app/nltk_data")
#nltk.download('stopwords')
#nltk.download('punkt')
from epi_pipeline import (
    streamlit_extraction,
    NER_Pipeline,
    GARD_Search,
    Classify_Pipeline
    )
import pandas as pd
#pd.set_option('display.max_colwidth', None)
import streamlit as st
st.set_page_config(layout="wide")
#import spacy
#import tensorflow as tf
#import pickle
import re
import plotly.graph_objects as go

#### LOGO ####
st.markdown('''<img src="https://huggingface.co/spaces/ncats/EpiPipeline4RD/raw/main/ncats.png" alt="National Center for Advancing Translational Sciences Logo">''',unsafe_allow_html=True)
st.markdown("")
st.markdown('''<img src="https://huggingface.co/spaces/ncats/EpiPipeline4RD/resolve/main/Logo_GARD_fullres.png" alt="NIH Genetic and Rare Diseases Information Center Logo"  width=400>''',unsafe_allow_html=True)

#st.markdown('''<img src="https://huggingface.co/spaces/ncats/EpiPipeline4GARD/raw/main/ncats.svg" alt="National Center for Advancing Translational Sciences Logo" width=800>''',unsafe_allow_html=True)
#st.markdown("")
#st.markdown('''<img src="https://huggingface.co/spaces/ncats/EpiPipeline4GARD/resolve/main/Logo_GARD_fullres.png" alt="NIH Genetic and Rare Diseases Information Center Logo" width=800>''',unsafe_allow_html=True)
#st.markdown("![National Center for Advancing Translational Sciences (NCATS) Logo](https://huggingface.co/spaces/ncats/EpiPipeline4GARD/resolve/main/NCATS_logo.png)")

#### TITLE ####
st.title("Epidemiological Information Extraction Pipeline for Rare Diseases")
#st.subheader("National Center for Advancing Translational Sciences (NIH/NCATS)")

#### CHANGE SIDEBAR WIDTH ###
st.markdown(
    """
    <style>
    [data-testid="stSidebar"][aria-expanded="true"] > div:first-child {
        width: 250px;
    }
    [data-testid="stSidebar"][aria-expanded="false"] > div:first-child {
        width: 250px;
        margin-left: -350px;
    }
    </style>
    """,
    unsafe_allow_html=True,
)

### Remove 'Made with Streamlit' Footer
st.markdown("""
            <style>
            #MainMenu {visibility: hidden;}
            footer {visibility: hidden;}
            header {visibility: hidden;}
            </style>
            """, unsafe_allow_html=True)

### ADD NEW FOOTER
footer="""<style>
a:link , a:visited{
color: blue;
background-color: transparent;
text-decoration: underline;
}

a:hover,  a:active {
color: red;
background-color: transparent;
text-decoration: underline;
}

.footer {
position: fixed;
left: 10;
bottom: 0;
width: 100%;
background-color: transparent;
color: black;
text-align: left;
}
</style>
<div class="footer">
<p>Developed by <a style='display: block; text-align: center;' href="https://github.com/wzkariampuzha" target="_blank">William Kariampuzha at NIH/NCATS</a></p>
</div>
"""
#st.markdown(footer,unsafe_allow_html=True)


#### DESCRIPTION ####
st.markdown("This application was built by the [National Center for Advancing Translational Sciences (NCATS)](https://ncats.nih.gov/) to automatically search and extract rare disease epidemiology information from PubMed abstracts.")

#### SIDEBAR WIDGETS ####

#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')).lower()

extract_diseases = st.sidebar.checkbox("Extract Rare Diseases", value=False)

#### MODEL LOADING ####
@st.experimental_singleton(show_spinner=False)
def load_models_experimental():
    epi_classify = Classify_Pipeline()
    epi_extract = NER_Pipeline()
    rd_identify = GARD_Search()
    return epi_classify, epi_extract, rd_identify

#### DOWNLOAD FUNCTION ####
@st.cache
def convert_df(df):
    # IMPORTANT: Cache the conversion to prevent computation on every rerun
    return df.to_csv().encode('utf-8')

#### SANKEY FUNCTION ####
#@st.cache(allow_output_mutation=True)
@st.experimental_singleton()
def epi_sankey(sankey_data, disease_or_gard_id):
    found, relevant, epidemiologic = sankey_data

    fig = go.Figure(data=[go.Sankey(
        node = dict(
          pad = 15,
          thickness = 20,
          line = dict(color = "white", width = 0.5),
          label = ["PubMed IDs Gathered", "Irrelevant Abstracts","Relevant Abstracts Gathered","Epidemiologic Abstracts","Not Epidemiologic"],
          color = "purple"
        ),
        #label = ["A1", "A2", "B1", "B2", "C1", "C2"]
        link = dict(
          source = [0, 0, 2, 2],
          target = [2, 1, 3, 4],
          value = [relevant, found-relevant, epidemiologic, relevant-epidemiologic]
      ))])
    fig.update_layout(
    hovermode = 'x',
    title="Search for the Epidemiology of "+disease_or_gard_id,
    font=dict(size = 10, color = 'black'),
)

    return fig

#### BEGIN APP ####
with st.spinner('Loading Epidemiology Models and Dependencies...'):
    epi_classify, epi_extract, rd_identify = load_models_experimental()
loaded = st.success('All Models and Dependencies Loaded!')

disease_or_gard_id = st.text_input("Input a rare disease term or NIH GARD ID.")

loaded.empty()

st.markdown("Examples of rare diseases include [**Fellman syndrome**](https://rarediseases.info.nih.gov/diseases/1/gracile-syndrome), [**Classic Homocystinuria**](https://rarediseases.info.nih.gov/diseases/6667/classic-homocystinuria), [**7383**](https://rarediseases.info.nih.gov/diseases/7383/phenylketonuria), and [**GARD:0009941**](https://rarediseases.info.nih.gov/diseases/9941/fshmd1a). A full list of rare diseases tracked by the NIH Genetic and Rare Diseases Information Center (GARD) can be found [here](https://rarediseases.info.nih.gov/diseases/browse-by-first-letter).")

if disease_or_gard_id:
    df, sankey_data, disease_gardid = streamlit_extraction(disease_or_gard_id, max_results, filtering,
                                                        epi_extract, rd_identify, extract_diseases, epi_classify)
    #IF it returns something, then continue.
    if sankey_data:
        df.replace(to_replace='None', value="None")
        st.dataframe(df, height=200)
        csv = convert_df(df)
        #if the user input does not have a number in it (i.e. weak proxy for if it is a GARD ID), then preserve the user input as the disease term.
        disease, gardID = disease_gardid
        if not bool(re.search(r'\d', disease_or_gard_id)):
            disease = disease_or_gard_id
            
        st.download_button(
            label="Download epidemiology results for "+disease+" as CSV",
            data = csv,
            file_name=disease+'.csv',
            mime='text/csv',
            )
        if gardID:
            st.markdown('See the NIH GARD page for ['+disease+'](https://rarediseases.info.nih.gov/diseases/'+str(re.sub('GARD:|0','',gardID))+'/'+str('-'.join(disease.split()))+')')
        
        fig = epi_sankey(sankey_data, disease)
        st.plotly_chart(fig, use_container_width=True)
        
        if 'IDS' in list(df.columns):
            st.markdown('''COLUMNS: \\
                       - PROB_OF_EPI: Probability that the paper is an epidemiologic study based on its abstract. \\
                       - IsEpi: If it is an epidemiologic study (If PROB_OF_EPI >0.5) \\
                       - DIS: Rare disease terms or synonyms identified in the abstract from the GARD Dictionary
                       - IDS: GARD IDs identified in the abstract from the GARD Dictionary \\
                       - EPI: Epidemiology Types are the metrics used to estimate disease burden such as "incidence", "prevalence rate", or "occurrence"
                       - STAT: Epidemiology Rates describe how many people are afflicted by a disease.
                       - DATE: The dates when the epidemiologic studies were conducted
                       - LOC: Where the epidemiologic studies were conducted.
                       - SEX: The biological sexes mentioned in the abstract. Useful for diseases that disproportionately affect one sex over the other or may provide context to composition of the study population
                       - ETHN: Ethnicities, races, and nationalities of those represented in the epidemiologic study.
                    ''')
        else:
            st.subheader("Categories of Results")
            st.markdown("    - **PROB_OF_EPI**: Probability that the paper is an epidemiologic study based on its abstract.  \n  - **IsEpi**: If it is an epidemiologic study (If PROB_OF_EPI >0.5)  \n  - **EPI**: Epidemiology Types are the metrics used to estimate disease burden such as 'incidence', 'prevalence rate', or 'occurrence'  \n  - **STAT**: Epidemiology Rates describe how many people are afflicted by a disease.  \n  - **DATE**: The dates when the epidemiologic studies were conducted  \n  - **LOC**: Where the epidemiologic studies were conducted.  \n  - **SEX**: The biological sexes mentioned in the abstract. Useful for diseases that disproportionately affect one sex over the other or may provide context to composition of the study population  \n  - **ETHN**: Ethnicities, races, and nationalities of those represented in the epidemiologic study.")
        #st.dataframe(data=None, width=None, height=None)