updating info

appStore/info.py

@@ -2,16 +2,23 @@ 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)
     footer = """
            <div class="footer-custom">
+               Guidance & Feedback - <a href="https://www.linkedin.com/in/maren-bernlöhr-149891222" target="_blank">Maren Bernlöhr</a> |
+               <a href="https://www.linkedin.com/in/manuelkuhm" target="_blank">Manuel Kuhm</a> |
                Developer - <a href="https://www.linkedin.com/in/erik-lehmann-giz/" target="_blank">Erik Lehmann</a>  |   
                <a href="https://www.linkedin.com/in/jonas-nothnagel-bb42b114b/" target="_blank">Jonas Nothnagel</a>   |
                <a href="https://www.linkedin.com/in/prashantpsingh/" target="_blank">Prashant Singh</a> |
-               Guidance & Feedback - Maren Bernlöhr | Manuel Kuhn </a>
+               
            </div>
        """
     st.markdown(footer, unsafe_allow_html=True)
@@ -34,7 +41,7 @@ The collaboration aims to determine the potential of NLP methods for tracking po
     <br>
     """
     st.markdown(intro, unsafe_allow_html=True)
-    st.image("docStore/img/pic1.png", caption="NDC Coherence")
+    st.image("appStore/img/pic1.png", caption="")
     #st.subheader("Methodology")
     #st.write("Each sentence in the generated answer ends with a coloured tooltip; the colour ranges from red to green. "
     #         "The tooltip contains a value representing answer sentence similarity to a specific sentence in the "