Update app.py

app.py

@@ -23,8 +23,6 @@ if "visibility" not in st.session_state:
 
 #nlp = en_core_web_lg.load()
 nlp = spacy.load("en_ner_bc5cdr_md")
-#nlp0 = spacy.load("en_ner_bc5cdr_md")
-#nlp1 = spacy.load("en_ner_bc5cdr_md")
 
 st.set_page_config(page_title ='Clinical Note Summarization', 
                    #page_icon= "Notes",
@@ -194,11 +192,8 @@ def get_entity_options():
     return options
 
 #adding a new pipeline component to identify negation
-def neg_model(nlp_model):
-    nlp = spacy.load(nlp_model, disable = ['parser'])
-#     nlp.add_pipe(nlp.create_pipe('sentencizer'))
+def neg_model():
     nlp.add_pipe('sentencizer')
-#     negex = Negex(nlp)
     nlp.add_pipe(
     "negex",
     config={
@@ -207,9 +202,9 @@ def neg_model(nlp_model):
     last=True)
     return nlp
 
-def negation_handling(nlp_model, note, neg_model):
+def negation_handling(note, neg_model):
     results = []
-    nlp = neg_model(nlp_model) 
+    nlp = neg_model() 
     note = note.split(".") #sentence tokenizing based on delimeter 
     note = [n.strip() for n in note] #removing extra spaces at the begining and end of sentence
     for t in note:
@@ -252,11 +247,11 @@ def dedupe(items):
 
 lem_clinical_note= lemmatize(runtext, nlp0)
 #creating a doc object using BC5CDR model
-doc = nlp1(lem_clinical_note)
+doc = nlp(lem_clinical_note)
 options = get_entity_options()
 
 #list of negative concepts from clinical note identified by negspacy
-results0 = negation_handling("en_ner_bc5cdr_md", lem_clinical_note, neg_model)
+results0 = negation_handling(lem_clinical_note, neg_model)
 
 matcher = match(nlp, results0,"NEG_ENTITY")
 
@@ -315,15 +310,18 @@ with col1:
             fulldischargesummary = historyAdmission['TEXT'].values
             st.write( str(fulldischargesummary))
     ##====== Storing the Diseases/Text
-    table= {"Entity":[], "Class":[]}
-    ent_bc = {}
-    for x in doc.ents:
-        ent_bc[x.text] = x.label_
-    for key in ent_bc:
-        table["Entity"].append(key)
-        table["Class"].append(ent_bc[key])
-    trans_df = pd.DataFrame(table)
-
+    # table= {"Entity":[], "Class":[]}
+    # ent_bc = {}
+    # for x in doc.ents:
+        # ent_bc[x.text] = x.label_
+    # for key in ent_bc:
+        # table["Entity"].append(key)
+        # table["Class"].append(ent_bc[key])
+    # trans_df = pd.DataFrame(table)
+    
+    problem_entities = list(dedupe([t for t in doc0.ents if t.label_ == 'DISEASE']))
+    medication_entities = list(dedupe([t for t in doc0.ents if t.label_ == 'CHEMICAL']))
+    
 with col2:
     st.button('NER')
     st.markdown('**CHIEF COMPLAINT:**')
@@ -331,13 +329,11 @@ with col2:
     st.markdown('**ADMISSION DIAGNOSIS:**')
     st.markdown(str(diagnosis))
     st.markdown('**PROBLEM/ISSUE**')
+    st.markdown(f'<p style="background-color:{problem_entities};color:#080808;font-size:16px;">{entlist}</p>', unsafe_allow_html=True)
     genEntities(trans_df, 'DISEASE')
     st.markdown('**MEDICATION**')
     genEntities(trans_df, 'CHEMICAL')
     #st.table(trans_df)   
     st.markdown('**NER**')
     with st.expander("See NER Details"):
-        st.markdown(ent_html, unsafe_allow_html=True) 
-    
-    
-
+        st.markdown(ent_html, unsafe_allow_html=True)