appStore/info.py

import streamlit as st

def app():
    
    
    with open('style.css') as f:
        st.markdown(f"<style>{f.read()}</style>", unsafe_allow_html=True)
    
    st.markdown("<h2 style='text-align: center;  \
                      color: black;'> Litter Monitoring Cockpit</h2>", 
                      unsafe_allow_html=True)

    
    st.markdown("<div style='text-align: center; \
                    color: grey;'>The Litter Monitoring Cockpit is an open-source\
                         digital tool which aims to assist urban waste management planners and \
                          other users in identifying and tracking relevant \
                            litter hotspots from various non-traditional data sources.</div>",
                        unsafe_allow_html=True)
    footer = """
           <div class="footer-custom">
               GIZ Lead - <a href="https://de.linkedin.com/in/steffen-blume-22450643">Steffen Blume</a> ,
               <a href="https://de.linkedin.com/in/robin-nowok">Robin Nowok</a> &
               <a href="https://www.linkedin.com/in/anika-s-ab3b49189/">Anika Schumann</a> |
               Developer - <a href="https://ch.linkedin.com/in/dominic-santschi">Dominic Santschi</a>  &   
               <a href="https://de.linkedin.com/in/dr-tilman-floehr-36005b11b">Tilman Floehr</a>   
               
           </div>
       """
    st.markdown(footer, unsafe_allow_html=True)

    c1, c2, c3 =  st.columns([8,1,12])
    with c1:
        st.image("docStore/img/vision.png")
    with c3:
        st.markdown('<div style="text-align: justify;">Todays litter monitoring systems often forego \
        the vast potential of data transparency. Here, we present a new approach to data-driven litter \
        monitoring.</div><br>',
    unsafe_allow_html=True)

    intro = """
    <div style="text-align: justify;">

    For this purpose, the PREVENT Waste Alliance formed a project team \
    to develop this AI-powered open-source web application that helps identify and monitor \
    litter hotspots in various regions faster to facilitate \
    evidence-based decision-making processes in sustainable development and beyond.  

    This tool allows urban waste management planners and other users the possibility to rapidly \
    identify litter hotspots in their regions, monitor their evolution, and inform holistic \
    mitigation strategies for waste leakage. To understand the application's functionalities \
    and learn more about the project, see the attached concept note. 

    </div>
    <br>
    """
    st.markdown(intro, unsafe_allow_html=True)