Update app.py

app.py

@@ -44,8 +44,15 @@ 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')
+#Loading in Admission chief Complaint and diagnosis
+df2 = pd.read_csv('cohort_cc_adm_diag.csv')
+
+#combining both data into one 
+df = pd.merge(df, df2, on=['HADM_ID','SUBJECT_ID'])
+
 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',
@@ -60,7 +67,7 @@ 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) 
+HospitalAdmission = st.sidebar.selectbox(' ', admissionid) 
 
 # List of Model available
 model = st.sidebar.selectbox('Select Model', ('BertSummarizer','BertGPT2','t5seq2eq','t5','gensim','pysummarizer'))
@@ -75,31 +82,47 @@ original_text =  df.query(
     "Patient_ID  == @patient & Admission_ID == @HospitalAdmission"
 )
 original_text2 = original_text['Original_Text'].values
+AdmissionChiefCom = original_text['Admission_Chief_Complaint'].values
+diagnosis =original_text['DIAGNOSIS'].values
 reference_text = original_text['Reference_text'].values
 
-##========= Buttons to the 4 tabs ========
-col1, col2, col3, col4 = st.columns(4)
-
-with col1:
-    if st.button("🏥 Admission"):
-        #nav_page('Admission')
-        inputNote = "Input Admission Note"
-    
-with col2:
-    if st.button('📆Daily Narrative'):
-        #nav_page('Daily Narrative')
-        inputNote = "Input Daily Narrative Note"
-with col3:
-    if st.button('🗒️Discharge Plan'):
-        #nav_page('Discharge Plan')  
-        inputNote = "Input Discharge Plan"
-with col4:
-    if st.button('📝Social Notes'):
-        #nav_page('Social Notes')
-        inputNote = "Input Social Note"
 
+##========= Buttons to the 4 tabs ========
+col1, col2, col3, col4, col5 = st.columns([1,1,1,1,1])
+col6, col7 =st.columns([2,2])
+with st.container():
+    with col1:
+        btnAdmission = st.button("🏥 Admission")
+        if btnAdmission:
+            #nav_page('Admission')
+            inputNote = "Input Admission Note"
+    with col2:
+        btnDailyNarrative = st.button('📆Daily Narrative')
+        if btnDailyNarrative:
+            inputNote = "Input Daily Narrative Note"
+    with col3:
+        btnDischargePlan = st.button('🗒️Discharge Plan')
+        if btnDischargePlan:
+            inputNote = "Input Discharge Plan"
+    with col4:
+        btnSocialNotes = st.button('📝Social Notes')
+        if btnSocialNotes: 
+            inputNote = "Input Social Note"
+    with col5:
+        btnPastHistory = st.button('📇Past History (6 Mths)')
+        if btnPastHistory:          
+            inputNote = "Input History records"
+            
+with st.container(): 
+    if btnPastHistory:
+        with col6: 
+            st.markdown('**No. of admission past 6 months: xx**')
+        with col7:
+            st.date_input('Select Admission Date')
+            
 runtext =st.text_area(inputNote, str(original_text2), height=300)
 
+
 # Extract words associated with each entity
 def genEntities(ann, entity):
               # entity colour dict
@@ -112,27 +135,12 @@ def genEntities(ann, entity):
                              ent = list(trans_df[trans_df['Class']==entity]['Entity'].unique())
                              entlist = ",".join(ent)
                              st.markdown(f'<p style="background-color:{ent_col[entity]};color:#080808;font-size:16px;">{entlist}</p>', unsafe_allow_html=True)
-                             #for i in ent:
-                                           #st.markdown(f'<p style="color:{ent_col[entity]};font-size:20px;">{i}</p>', unsafe_allow_html=True)
-                                          
+                                                                       
 
 def visualize (run_text,output):
     text =''
     splitruntext = [x for x in runtext.split('.')]
     splitoutput = [x for x in output.split('.')]
-#    best_sentences = []
-#    for sentence in output:
-#       best_sentences.append(str(sentence))
-
-#    text = ''
-
-#    #display(HTML(f'<h1>Summary - {title}</h1>'))
-#    for sentence in run_text:
-#        if sentence in best_sentences:
-#            text += ' ' + str(sentence).replace(sentence, f"<mark>{sentence}</mark>")
-#        else:
-#            text += ' ' + sentence
-    #    display(HTML(f""" {text} """))
     return splitoutput,splitruntext
     
 
@@ -162,19 +170,19 @@ def run_model(input_text):
         output = original_text['pysummarizer'].values
         st.write('Summary')
 
-    #st.text_area(visualize (runtext,output))
+
     st.success(output)
-   # return output
+
 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)
 
-col1, col2 = st.columns([1,2])
+col1, col2 = st.columns([1,1])
 with col1:
     st.button('Summarize')
     run_model(runtext)
-    sentences=runtext.split('.')
+    #sentences=runtext.split('.')
     st.text_area('Reference text', str(reference_text), height=150)
     ##====== Storing the Diseases/Text
     table= {"Entity":[], "Class":[]}
@@ -188,13 +196,18 @@ with col1:
 
 with col2:
     st.button('NER')
-    st.markdown('**DISEASE**')
+    st.markdown('**CHIEF COMPLAINT:**')
+    st.write(str(AdmissionChiefCom))
+    st.markdown('**ADMISSION DIAGNOSIS:**')
+    st.markdown(str(diagnosis))
+    st.markdown('**PROBLEM/ISSUE**')
     genEntities(trans_df, 'DISEASE')
-    st.markdown('**CHEMICAL**')
+    st.markdown('**MEDICATION**')
     genEntities(trans_df, 'CHEMICAL')
     #st.table(trans_df)   
     st.markdown('**NER**')
     st.markdown(ent_html, unsafe_allow_html=True) 
+