Update app.py

app.py

@@ -1,4 +1,5 @@
 import streamlit as st
+import time
 from annotated_text import annotated_text
 from io import StringIO
 from transformers import AutoTokenizer, AutoModelForTokenClassification
@@ -47,23 +48,23 @@ def init_qa_pipeline():
 
 def get_formatted_text_for_annotation(output):
     colour_map = {'Coreference': '#29D93B',
-    'Severity':'#291923',
- 'Sex': '#330460',
- 'Sign_symptom': '#5448AD',
+    'Severity':'#FCF3CF',
+ 'Sex': '#E9F7EF',
+ 'Sign_symptom': '#EAF2F8',
  'Detailed_description': '#078E8B',
- 'Date': '#C19616',
- 'History': '#1A9903',
- 'Medication': '#16B01A',
- 'Therapeutic_procedure': '#6642A8',
- 'Age': '#F4A936',
- 'Subject': '#38C708',
- 'Biological_structure': '#106C6C',
- 'Activity': '#E017C8',
- 'Lab_value': '#66011C',
- 'Family_history': '#3EEEBB',
- 'Diagnostic_procedure': '#22C61D',
- 'Other_event': '#ACC61D',
- 'Occupation': '#521382'}
+ 'Date': '#F5EEF8',
+ 'History': '#FDEDEC',
+ 'Medication': '#F4F6F6',
+ 'Therapeutic_procedure': '#A3E4D7',
+ 'Age': '#85C1E9',
+ 'Subject': '#D7BDE2',
+ 'Biological_structure': '#AF7AC5',
+ 'Activity': '#B2BABB',
+ 'Lab_value': '#E6B0AA',
+ 'Family_history': '#2471A3',
+ 'Diagnostic_procedure': '#CCD1D1',
+ 'Other_event': '#239B56',
+ 'Occupation': '#B3B6B7'}
     
     annotated_texts = []
     next_index = 0
@@ -223,16 +224,25 @@ if selected_menu == "Upload Document":
     if uploaded_file is not None:
         ocr_text  = get_text_from_ocr_engine()
         st.write("Upload Successful")
+        
 elif selected_menu == "Extract Text":
-    st.write(get_text_from_ocr_engine())
+    with st.spinner("Extracting Text..."):
+        time.sleep(6)
+        st.write(get_text_from_ocr_engine())
+        
 elif selected_menu == "Summarize Document":
     paragraphs= get_paragraphs_for_summaries()
     
-    with st.spinner("Summarizing Document..."):
+    with st.spinner("Finding Topics..."):
         tags_found = ["Injury Details", "Past Medical Conditions", "Injury Management Plan", "GP Correspondence"]
+        time.sleep(5)
         st.write("This document is about:")
-        st.write(";".join(["#" + tag for tag in tags_found]))
+        st.markdown(";".join(["#" + tag + " "  for tag in tags_found])**)
         st.markdown("""---""")
+        
+    with st.spinner("Summarizing Document..."):
+        
+        
         for text in paragraphs:
             summary_text = pipeline_summarization(text, max_length=130, min_length=30, do_sample=False)
             # Show output