app.py

import streamlit as st 
import pandas as pd 
import time
from App.class_input_box.input_box import *
from App.functions_rupture.functions_gestion import *
from App.utils.divers_function import *
from App.utils.filter_dataframe import *
from App.utils.filter_dataframe import *


# Page configuration
st.set_page_config(
    page_title="Gestion des ruptures",
    page_icon="images/Carrefour_logo.png",
    layout="wide"
)
hide_streamlit_style = """
            <style>
                footer {visibility: hidden;}
            </style>
            """
st.markdown(hide_streamlit_style, unsafe_allow_html=True)


def app():
        st.title("Gestion des ruptures ")

        input_box = InputsBox()
        
        data = input_box.get_data()

        try:
            if data.shape[0] != 0 :
                st.header("Data")

                st.dataframe(filter_dataframe(data))

                "## Parameters"

                col1, col2 = st.columns(2)

                with col1 :
                    product_id = input_box.get_product_id()

                with col2 :
                    class_id = input_box.get_class_id()

                '## Filters'
                col1, col2 = st.columns(2)
                
                with col1 :
                    min_product_id = input_box.valid_produict_id()

                with col2 :
                    vaind_class_id = input_box.valid_class_id()

                conditions = input_box.conditions()

                if st.button("RUN ", key="run_button"):
                    data = valide_key(data, product_id, class_id, min_product_id, vaind_class_id )
                    Country, merged = nouvelle_data(data, 
                                                    str(product_id), 
                                                    str(class_id))

                    merged_final = finale_merged(merged, 
                                                 Country, 
                                                 product_id, 
                                                 class_id)            

                    if conditions["Show data with ratios"]:
                        st.subheader("Show data with ratios")
                        merged_final.loc[:, "Evaluation"]= True
                        merged_final  = st.data_editor(merged_final)
                        

                        csv = convert_df(merged_final)
                        st.download_button(label="Download data as CSV",
                                           data=csv,
                                           file_name='sample_df.csv',
                                           mime='text/csv',)

                    data_countries_ratio = cond_pays_proportion(merged_final, 
                                                                conditions["Number of countries"], 
                                                                conditions["Proportion"], 
                                                                product_id)

                    df = supprime_country(data_countries_ratio)
                    csv = convert_df(df)

                    """## The data below is filtered as follows: """
                    "- Number of countries greater than or equal to ", conditions["Number of countries"]
                    "- The proportion with the highest ", class_id ," is greater than or equal to ",conditions["Proportion"]

                    DF = df[((df.Proportion >= conditions["Proportion"]) & (df.total_by_ligne >= conditions["Number of countries"]))]

                    finale_df = Merger(data,
                                       DF,
                                       product_id, 
                                       class_id)

                    tab1, tab2 = st.tabs(["Data without decision-making", "Data with proposed changes"])

                    with tab1 :
                        st.subheader("Data without decision-making")
                        df.loc[:, "Evaluation"] = True
                        df = st.data_editor(df)
                        
                        st.download_button(label="Download data as CSV",
                                           data=csv,
                                           file_name='sample_df.csv',
                                           mime='text/csv',)

                    with tab2 :
                        st.subheader("Data with proposed changes")
                        finale_df.loc[:, "Evaluation"] = True
                        finale_df = st.data_editor(finale_df)
                        
                        csv_f = convert_df(finale_df)
                        st.download_button(label="Download data as CSV",
                                               data=csv_f,
                                               file_name='sample_df.csv',
                                               mime='text/csv',)

                    "## Country priority "

                    priority_data, df_equa, df_nequa = cond_pays_priorite(merged_final, product_id)
                    tab1, tab2, tab3, tab4 = st.tabs(["Data without decision-making", "Equality case and mt1", "Cases of inequality", "Data with proposed changes mt2"])

                    with tab1 :
                        st.subheader("Data without decision-making")
                        priority_data.loc[:, "Evaluation"] = True
                        priority_data = st.data_editor(priority_data)
                        
                        csv_f = convert_df(priority_data)
                        st.download_button(label="Download data as CSV",
                                               data=csv_f,
                                               file_name='sample_df.csv',
                                               mime='text/csv',)

                    with tab2 :
                        st.subheader("Equality case")
                        df_equa.loc[:, "Evaluation"]= True
                        df_equa = st.data_editor(df_equa)
                        
                        csv_f = convert_df(df_equa)
                        st.download_button(label="Download data as CSV",
                                               data=csv_f,
                                               file_name='sample_df.csv',
                                               mime='text/csv',)

                    with tab3 :
                        st.subheader("Cases of inequality")
                        df_nequa_ = df_nequa[(df_nequa.total_by_ligne.apply(lambda x: int(x) > 2))]
                        df_nequa_.loc[:, "Evaluation"]= True
                        df_nequa_ = st.data_editor(df_nequa_)
                        
                        csv_f = convert_df(df_nequa_)
                        st.download_button(label="Download data as CSV",
                                               data=csv_f,
                                               file_name='sample_df.csv',
                                               mime='text/csv',)
                        

                     
                    max_poids_index = df_nequa_.groupby('BARCODE')['Poids'].idxmax()

                    
                    df_max_poids = df_nequa_.loc[max_poids_index]
                    df_max_poids.drop(["COUNTRY_KEY"], axis = 1, inplace= True)
                    finale_df_  =  Merger(data,df_max_poids,  product_id, class_id)    
                    with tab4 :
                        st.subheader("Cases of inequality")
                        finale_df_.loc[:, "Evaluation"]= True
                        finale_df_ = st.data_editor(finale_df_)
                        
                        csv_f = convert_df(finale_df_)
                        st.download_button(label="Download data as CSV",
                                               data=csv_f,
                                               file_name='sample_df.csv',
                                               mime='text/csv',)

                    st.subheader(" One vs One with similarity score")
                    df_nequa_1 = df_nequa[(df_nequa.total_by_ligne.apply(lambda x: int(x) == 2))]

                    max_poids_index1 = df_nequa_1.groupby('BARCODE')['Poids'].idxmax()

                    # Récupérer les lignes correspondantes
                    df_max_poids1 = df_nequa_1.loc[max_poids_index1]
                    df_max_poids1.drop(["COUNTRY_KEY"], axis = 1, inplace= True)
                    finale_df_1  =  ajout_simularite(Merger(data,df_max_poids1,  product_id, class_id))
                    finale_df_1.loc[:, "Evaluation"]= True
                    finale_df_1 = st.data_editor(finale_df_1)
                    csv_f = convert_df(finale_df_1)
                    st.download_button(label="Download data as CSV",
                                               data=csv_f,
                                               file_name='sample_df.csv',
                                               mime='text/csv',)
                    st.success('Done!', icon="✅")
                    st.balloons()

        except:
            pass
            #st.error('This is an error', icon="🚨")
            st.info('Ensure that column names are capitalized and that product_id and class_id descriptions are present, as well as a country column.', icon="ℹ️")


if __name__ == "__main__":
    lien_label = "# Example of input"
    lien_url = "https://docs.google.com/spreadsheets/d/123hVTOFpBT-C6mCnrOBh8fFIhSi8FxiuyHZJAQu8bDc/edit#gid=1220891905"
    lien_html = f'<a href="{lien_url}">{lien_label}</a>'

    st.sidebar.markdown(lien_html, unsafe_allow_html=True)
    app()