updating image and menu

appStore/info.py

@@ -1,13 +1,6 @@
 import streamlit as st
 
-
 def app():
-    # if 'file' in st.session_state:
-    #   file = st.session_state['file']
-    # else:
-    #   st.sidebar.markdown(" :cloud: Upload document ")
-    #   uploaded_file = st.sidebar.file_uploader('', type=['pdf', 'docx', 'txt']) #Upload PDF File
-    #   st.session_state['file'] = uploaded_file
     
     with open('style.css') as f:
         st.markdown(f"<style>{f.read()}</style>", unsafe_allow_html=True)
@@ -26,11 +19,47 @@ def app():
     st.subheader("Policy Action Tracker Manual")
     intro = """
     <div class="text">
-    The manual extraction of relevant information from text documents is a time-consuming task for any policy analysts. As the amount and length of public policy documents in relation to sustainable development (such as National Development Plans and Nationally Determined Contributions) continuously increases, a major challenge for policy action tracking – the evaluation of stated goals and targets and their actual implementation on the ground – arises. Luckily, Artificial Intelligence (AI) and Natural Language Processing (NLP) methods can help in shortening and easing this task for policy analysts.  
+    The manual extraction of relevant information from text documents is a \
+    time-consuming task for any policy analysts. As the amount and length of \
+    public policy documents in relation to sustainable development (such as \
+    National Development Plans and Nationally Determined Contributions) \
+    continuously increases, a major challenge for policy action tracking – the \
+    evaluation of stated goals and targets and their actual implementation on \
+    the ground – arises. Luckily, Artificial Intelligence (AI) and Natural \
+    Language Processing (NLP) methods can help in shortening and easing this \
+    task for policy analysts.  
 
-For this purpose, the United Nations Sustainable Development Solutions Network (SDSN) and the Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ) GmbH are collaborating since 2021 in the development of an AI-powered open-source web application that helps find and extract relevant information from public policy documents faster to facilitate evidence-based decision-making processes in sustainable development and beyond.  
+    For this purpose, the United Nations Sustainable Development Solutions \
+    Network (SDSN) and the Deutsche Gesellschaft für Internationale \
+    Zusammenarbeit (GIZ) GmbH are collaborating since 2021 in the development \
+    of an AI-powered open-source web application that helps find and extract \
+    relevant information from public policy documents faster to facilitate \
+    evidence-based decision-making processes in sustainable development and beyond.  
 
-The collaboration aims to determine the potential of NLP methods for tracking policy implementation and coherence in the context of the Sustainable Development Goals (SDGs) and the Paris Climate Agreement. Nationally determined contributions (NDCs) will serve as a starting point for the analysis and evaluation in a specific national context. Under the Paris Climate Agreement, NDCs embody the efforts of each country to reduce national emissions and thus contribute to the achievement of the long-term goals of the Agreement – to increase the ability to adapt to adverse impacts of climate change and foster climate resilience and low greenhouse gas emissions development, in a manner that does not threaten food production. The Paris Climate Agreement (Article 4, Paragraph 2)1 requires each Party to prepare, communicate and maintain successive NDCs. Thus, they serve as a comparable, accessible, and widely acknowledged starting point for analysis. However, the agreed and communicated goals and measures must also be reflected in national strategies, statements, and other government publications to be implemented timely, as well as effectively. At best, the activities and measures should have an allocated budget. Given the complexity, the manual evaluation of policy documents and other publications has been very time-consuming and has presented a significant challenge for policy analysts and makers alike. In consequence, the open-source web application aims to support the process through suitable AI-powered and NLP methods. In the following, the application’s functionalities are explained in more detail.
+    The collaboration aims to determine the potential of NLP methods for \
+        tracking policy implementation and coherence in the context of the \
+        Sustainable Development Goals (SDGs) and the Paris Climate Agreement. \
+        Nationally determined contributions (NDCs) will serve as a starting \
+        point for the analysis and evaluation in a specific national context. \
+        Under the Paris Climate Agreement, NDCs embody the efforts of each \
+        country to reduce national emissions and thus contribute to the \
+        achievement of the long-term goals of the Agreement – to increase the \
+        ability to adapt to adverse impacts of climate change and foster \
+        climate resilience and low greenhouse gas emissions development, in a \
+        manner that does not threaten food production. The Paris Climate \
+        Agreement (Article 4, Paragraph 2)1 requires each Party to prepare, \
+        communicate and maintain successive NDCs. Thus, they serve as a \
+        comparable, accessible, and widely acknowledged starting point for \
+        analysis. However, the agreed and communicated goals and measures must \
+        also be reflected in national strategies, statements, and other \
+        government publications to be implemented timely, as well as effectively.\
+        At best, the activities and measures should have an allocated budget. \
+        Given the complexity, the manual evaluation of policy documents and \
+        other publications has been very time-consuming and has presented a \
+        significant challenge for policy analysts and makers alike. In \
+        consequence, the open-source web application aims to support the process\
+         through suitable AI-powered and NLP methods. In the following, the \
+        application’s functionalities are explained in more detail.
         <ul>
         <li>Analizing the policy document</li>
         <li>finding SDG related content</li> 

appStore/multiapp.py

@@ -45,17 +45,14 @@ class MultiApp:
     def run(self):
         
         st.sidebar.write(format_func=lambda app: app['title'])
-        image = Image.open('docStore/img/giz_sdsn.jpg')
-        st.sidebar.image(image)
-        #st.sidebar.markdown("##  📌 Pages ")
-        #app = st.sidebar.radio(
-        #    'Pages',
-         #   self.apps,
-         #   from streamlit_option_menu import option_menu
+        image = Image.open('docStore/img/giz_sdsn_small.jpg')
+        st.sidebar.image(image, width =5)
+       
         with st.sidebar:
             selected = option_menu(None, [page["title"] for page in self.apps],
                                    icons=[page["icon"] for page in self.apps],
-                                   menu_icon="cast", default_index=0)          
+                                   menu_icon="cast", default_index=0)
+            st.markdown("---")          
         
         
         for index, item in enumerate(self.apps):
@@ -63,14 +60,11 @@ class MultiApp:
                 self.apps[index]["function"]()
                 break
                 
-        # app['function']()
+   
         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)
-        # st.sidebar.markdown('')
-        # st.sidebar.markdown(" :cloud: Upload document ")
-        # uploaded_file = st.sidebar.file_uploader('', type=['pdf', 'docx', 'txt']) #Upload PDF File
-        # st.session_state['file'] = uploaded_file
+