Update app.py

app.py

@@ -1,64 +1,14 @@
-#import appStore.doc_processing as processing
-from utils.uploadAndExample import add_upload
+#import appStore.sdg_analysis as sdg_analysis
+#import appStore.info as info
+from appStore.multiapp import MultiApp
 import streamlit as st
 
-####################################### Dashboard ######################################################
-
-# App 
-
-st.set_page_config(page_title = 'Vulnerable Groups Identification', 
+st.set_page_config(page_title = 'Vulnerability Classifier', 
                    initial_sidebar_state='expanded', layout="wide") 
 
-with st.sidebar:
-    # upload and example doc
-    choice = st.sidebar.radio(label = 'Select the Document',
-                            help = 'You can upload the document \
-                            or else you can try a example document', 
-                            options = ('Upload Document', 'Try Example'), 
-                            horizontal = True)
-    add_upload(choice)
-
-with st.container():
-        st.markdown("<h2 style='text-align: center; color: black;'> Vulnerable Groups Identification </h2>", unsafe_allow_html=True)
-        st.write(' ')
-
-with st.expander("ℹ️ - About this app", expanded=False):
-    st.write(
-        """
-        The Vulnerable Groups Identification App is an open-source\
-        digital tool which aims to assist policy analysts and \
-        other users in extracting and filtering relevant \
-        information from public documents.
-        """)
-    st.write('**Definitions**')
-
-    st.caption("""
-            - **Place holder**: Place holder \
-            Place holder \
-            Place holder \
-            Place holder \ 
-            Place holder
-              """)
-   
-    st.write("""
-           What happens in the background?
-            
-           - Step 1: Once the document is provided to app, it undergoes *Pre-processing*.\
-           In this step the document is broken into smaller paragraphs \
-           (based on word/sentence count).
-           - Step 2: The paragraphs are fed to **Target Classifier** which detects if
-           the paragraph contains any *Target* related information or not.
-           - Step 3: The paragraphs which are detected containing some target \
-           related information are then fed to multiple classifier to enrich the 
-           Information Extraction.
-    
-           The Step 2 and 3 are repated then similarly for Action and  Policies & Plans.
-           """)
-                  
-    st.write("")
+app = MultiApp()
 
+app.add_app("About","house", info.app)
+app.add_app("SDG Analysis","gear",sdg_analysis.app)
 
-     
-if st.button("Analyze Document"):
-    print("Analyse")    
-    #groups_extraction.identify_groups()
+app.run()