Delete app.py

app.py

@@ -1,106 +0,0 @@
-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
-
-nlp = spacy.load("en_ner_bc5cdr_md")
-#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('Summarize'):
-    run_model(runtext)
-    
-    sentences=runtext.split('.')
-    
-  
-    st.text_area('Reference text', str(reference_text))
-
-if 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)