import streamlit as st
import pandas as pd
import numpy as np
from math import ceil
from collections import Counter
from string import punctuation
import spacy
from spacy import displacy
import en_ner_bc5cdr_md

from streamlit.components.v1 import html

def nav_page(page_name, timeout_secs=3):
    nav_script = """
        <script type="text/javascript">
            function attempt_nav_page(page_name, start_time, timeout_secs) {
                var links = window.parent.document.getElementsByTagName("a");
                for (var i = 0; i < links.length; i++) {
                    if (links[i].href.toLowerCase().endsWith("/" + page_name.toLowerCase())) {
                        links[i].click();
                        return;
                    }
                }
                var elasped = new Date() - start_time;
                if (elasped < timeout_secs * 1000) {
                    setTimeout(attempt_nav_page, 100, page_name, start_time, timeout_secs);
                } else {
                    alert("Unable to navigate to page '" + page_name + "' after " + timeout_secs + " second(s).");
                }
            }
            window.addEventListener("load", function() {
                attempt_nav_page("%s", new Date(), %d);
            });
        </script>
    """ % (page_name, timeout_secs)
    html(nav_script)



# Store the initial value of widgets in session state
if "visibility" not in st.session_state:
    st.session_state.visibility = "visible"
    st.session_state.disabled = False

#nlp = en_core_web_lg.load()
nlp = spacy.load("en_ner_bc5cdr_md")

st.set_page_config(page_title ='📝Social Notes', 
                   layout='wide')

st.title('Clinical Note Summarization - 📝Social Notes')
st.markdown(
    """
    <style>
    [data-testid="stSidebar"][aria-expanded="true"] > div:first-child {
        width: 400px;
    }
    [data-testid="stSidebar"][aria-expanded="false"] > div:first-child {
        width: 400px;
        margin-left: -230px;
    }
    </style>
    """,
    unsafe_allow_html=True,
)
st.sidebar.markdown('Using transformer model')

## Loading in dataset
#df = pd.read_csv('mtsamples_small.csv',index_col=0)
df = pd.read_csv('shpi_w_rouge21Nov.csv')
df['HADM_ID'] = df['HADM_ID'].astype(str).apply(lambda x: x.replace('.0',''))

#Renaming column
df.rename(columns={'SUBJECT_ID':'Patient_ID',
                  'HADM_ID':'Admission_ID',
                  'hpi_input_text':'Original_Text',
                  'hpi_reference_summary':'Reference_text'}, inplace = True)
 
 #data.rename(columns={'gdp':'log(gdp)'}, inplace=True)

#Filter selection 
st.sidebar.header("Search for Patient:")

patientid = df['Patient_ID']
patient = st.sidebar.selectbox('Select Patient ID:', patientid)
admissionid = df['Admission_ID'].loc[df['Patient_ID'] == patient]
HospitalAdmission = st.sidebar.selectbox('', admissionid) 

# List of Model available
model = st.sidebar.selectbox('Select Model', ('BertSummarizer','BertGPT2','t5seq2eq','t5','gensim','pysummarizer'))

col3,col4 = st.columns(2) 
patientid = col3.write(f"Patient ID:  {patient} ")
admissionid =col4.write(f"Admission ID:  {HospitalAdmission} ")


##========= Buttons to the 4 tabs ========
col1, col2, col3, col4 = st.columns(4)
with col1:
#    st.button('Admission')
    if st.button("🏥 Admission"):
        nav_page('Admission')

with col2:
    if st.button('📆Daily Narrative'):
        nav_page('Daily Narrative')
  
with col3:
    if st.button('Discharge Plan'):
        nav_page('Discharge Plan')
with col4:
    if st.button('📝Social Notes'):
        nav_page('Social Notes')
    


# Query out relevant Clinical notes
original_text =  df.query(
    "Patient_ID  == @patient & Admission_ID == @HospitalAdmission"
)

original_text2 = original_text['Original_Text'].values

runtext =st.text_area('Input Clinical Note here:', str(original_text2), height=300)

reference_text = original_text['Reference_text'].values

def run_model(input_text):

    if model == "BertSummarizer":
        output = original_text['BertSummarizer'].values
        st.write('Summary')
        st.success(output[0])

    elif model == "BertGPT2":
        output = original_text['BertGPT2'].values
        st.write('Summary')
        st.success(output[0])
        
      
    elif model == "t5seq2eq": 
        output = original_text['t5seq2eq'].values
        st.write('Summary')
        st.success(output)
        
    elif model == "t5":
        output = original_text['t5'].values
        st.write('Summary')
        st.success(output)
        
    elif model == "gensim": 
        output = original_text['gensim'].values
        st.write('Summary')
        st.success(output)
        
    elif model == "pysummarizer": 
        output = original_text['pysummarizer'].values
        st.write('Summary')
        st.success(output)

col1, col2 = st.columns([1,1])

with col1:
    st.button('Summarize')
    run_model(runtext)
    sentences=runtext.split('.')
    st.text_area('Reference text', str(reference_text))#,label_visibility="hidden")
with col2:
    st.button('NER')
    doc = nlp(str(original_text2))
    colors = { "DISEASE": "pink","CHEMICAL": "orange"}
    options = {"ents": [ "DISEASE", "CHEMICAL"],"colors": colors}
    ent_html = displacy.render(doc, style="ent", options=options)
    st.markdown(ent_html, unsafe_allow_html=True)