coherence country dropdown

appStore/coherence.py

@@ -3,6 +3,58 @@ import glob, os, sys;
 sys.path.append('../utils')
 
 import streamlit as st
+import ast
+
+# Reading data and Declaring necessary variables
+with open('ndcs/countryList.txt') as dfile:
+        countryList = dfile.read()
+countryList = ast.literal_eval(countryList)
+countrynames = list(countryList.keys())
+    
+with open('ndcs/cca.txt', encoding='utf-8', errors='ignore') as dfile:
+            cca_sent = dfile.read()
+cca_sent = ast.literal_eval(cca_sent)
+            
+with open('ndcs/ccm.txt', encoding='utf-8', errors='ignore') as dfile:
+    ccm_sent = dfile.read()
+ccm_sent = ast.literal_eval(ccm_sent)
 
 def app():
-    st.write("Coming soon")
+
+    #### APP INFO #####
+    with st.container():
+        st.markdown("<h1 style='text-align: center;  \
+                      color: black;'> Check NDC Coherence</h1>", 
+                      unsafe_allow_html=True)
+        st.write(' ')
+        st.write(' ')
+    with st.expander("ℹ️ - About this app", expanded=False):
+
+        st.write(
+            """     
+            The *Check NDC Coherence* application provides easy evaluation of 
+            coherence between a given policy document and a country’s (Intended)\
+            Nationally Determined Contribution (INDCs/NDCs) using open-source \
+            data from the German Institute of Development and Sustainability’s \
+            (IDOS) [NDC Explorer](https://klimalog.idos-research.de/ndc/#NDCExplorer/worldMap?NewAndUpdatedNDC??income???catIncome).\
+            """)
+        st.write("")
+        st.write(""" User can select a country context via the drop-down menu \
+            on the left-hand side of the application. Subsequently, the user is \
+            given the opportunity to manually upload another policy document \
+            from the same national context or to select a pre-loaded example \
+            document. Thereafter, the user can choose between two categories \
+            to compare coherence between the documents: climate change adaptation \
+            and climate change mitigation. Based on the selected information, \
+            the application identifies relevant paragraphs in the uploaded \
+            document and assigns them to the respective indicator from the NDC \
+            Explorer. Currently, the NDC Explorer has 20 indicators under \
+            climate change mitigation (e.g., fossil fuel production, REDD+) and \
+            22 indicators under climate change adaptation (e.g., sea level rise,\
+            investment needs). The assignment of the paragraph to a corresponding\
+            indicator is based on vector similarities in which only paragraphs \
+            with similarity above 0.55  to the indicators are considered. """)
+    
+    option = st.sidebar.selectbox('Select Country', (countrynames))
+    countryCode = countryList[option]
+    

appStore/keyword_search.py

@@ -47,20 +47,21 @@ def app():
             policy document - developed by GIZ Data and the \
             Sustainable Development Solution Network.
             """)
+        st.write("")
         st.write(""" The application allows its user to perform a keyword search\
-             based on two options: a lexical (TFIDF) search and semantic  \
-             bi-encoder search. The difference between both approaches is quite \
-            straightforward; while the lexical search only displays paragraphs \
-            in the document with exact matching results, the semantic search \
-            shows paragraphs with meaningful connections (e.g., synonyms) based\
-            on the context as well. The semantic search allows for a personalized\
-            experience in using the application. Both methods employ a \
-            probabilistic retrieval framework in its identification of relevant \
-            paragraphs. By defualt the search is performed using 'Semantic Search'
-            to find 'Exact/Lexical Matches' please tick the checkbox provided, which will \
-            by pass semantic search.. Furthermore, the application allows the \
-            user to search for pre-defined keywords from different thematic buckets\
-            present in sidebar.""")
+             based on two options: a lexical ([TFIDF](https://en.wikipedia.org/wiki/Tf%E2%80%93idf))\
+             search and semantic bi-encoder search. The difference between both \
+            approaches is quite straightforward; while the lexical search only \
+            displays paragraphs in the document with exact matching results, \
+            the semantic search shows paragraphs with meaningful connections \
+            (e.g., synonyms) based on the context as well. The semantic search \
+            allows for a personalized experience in using the application. Both \
+            methods employ a probabilistic retrieval framework in its identification\
+            of relevant paragraphs. By defualt the search is performed using \
+            'Semantic Search' to find 'Exact/Lexical Matches' please tick the \
+            checkbox provided, which will by pass semantic search.. Furthermore,\
+            the application allows the user to search for pre-defined keywords \
+            from different thematic buckets present in sidebar.""")
 
     
     with st.sidebar: