Update app.py

app.py

@@ -1,28 +1,139 @@
 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
 
-st.set_page_config(
-    page_title="Hello",
-    page_icon="👋",
-)
-
-st.write("# Welcome to Streamlit! 👋")
+# 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
 
-st.sidebar.success("Select a demo above.")
+#nlp = en_core_web_lg.load()
+nlp = spacy.load("en_ner_bc5cdr_md")
 
+st.set_page_config(page_title ='Clinical Note Summarization', 
+                   #page_icon= "Notes",
+                   layout='wide')
+st.title('Clinical Note Summarization')
 st.markdown(
     """
-    Streamlit is an open-source app framework built specifically for
-    Machine Learning and Data Science projects.
-    **👈 Select a demo from the sidebar** to see some examples
-    of what Streamlit can do!
-    ### Want to learn more?
-    - Check out [streamlit.io](https://streamlit.io)
-    - Jump into our [documentation](https://docs.streamlit.io)
-    - Ask a question in our [community
-        forums](https://discuss.streamlit.io)
-    ### See more complex demos
-    - Use a neural net to [analyze the Udacity Self-driving Car Image
-        Dataset](https://github.com/streamlit/demo-self-driving)
-    - Explore a [New York City rideshare dataset](https://github.com/streamlit/demo-uber-nyc-pickups)
-"""
-)
+    <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')
+    st.button("🏥 Admission")
+with col2:
+    st.button('📆Daily Narrative')
+with col3:
+    st.button('Discharge Plan')
+with col4:
+    st.button('📝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) 
+