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

#nlp = en_core_web_lg.load()

st.set_page_config(layout='wide')
st.title('Clinical Note Summarization')
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} ")
   
#text = st.text_area('Input Clinical Note here')

# 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)
        
if st.button('Submit'):
    run_model(runtext)
    
    sentences=runtext.split('.')
def visualize(title, sentence_list, best_sentences):
  text = ''

  #display(HTML(f'<h1>Summary - {title}</h1>'))
  for sentence in sentence_list:
    if sentence in best_sentences:
      #text += ' ' + str(sentence).replace(sentence, f"<mark>{sentence}</mark>")
      text += ' ' + str(sentence).replace(sentence, f"<span class='highlight yellow'>{sentence}</span>")
    else:
      text += ' ' + sentence
      display(HTML(f""" {text} """))

    output = ''   
    best_sentences = []
    for sentence in output:
       #print(sentence)
       best_sentences.append(str(sentence))
    return text     

    t = "<div>Hello there my <span class='highlight blue'>name <span class='bold'>yo</span> </span> is <span class='highlight red'>Fanilo <span class='bold'>Name</span></span></div>"

    st.write("<div>Hello there my <span class='highlight blue'>name <span class='bold'>yo</span> </span> is <span class='highlight red'>Fanilo <span class='bold'>Name</span></span></div>")  
  
    st.text_area('Reference text', str(reference_text))