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Summarization_Simple_25Nov.py

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+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))