Update app.py

app.py

@@ -150,7 +150,10 @@ def run_model(input_text):
     #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([1,1])
 with col1:
@@ -158,19 +161,23 @@ with col1:
     run_model(runtext)
     sentences=runtext.split('.')
     st.text_area('Reference text', str(reference_text), height=150)
-    test = pd.DataFrame(
-        np.random.randn(10, 5),
-       columns=('col %d' % i for i in range(5)))
-
-    st.table(test)
-  
+    ##====== 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)
+    st.table(trans_df)    
 
 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)
+#    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) 
-    
-    
+    #st.write(doc.ents)
+