emoji

App/class_input_box/input_box.py

@@ -60,3 +60,25 @@ class InputsBox:
             conditions["Show data with ratios"] = countries
 
         return conditions
+    
+
+    def conditions_(self):
+        col1, col2, col3 = st.columns(3)
+        conditions = {}
+
+        with col1:
+            nb_countries = st.number_input("Number of countries", min_value=1, max_value=20, value=1, key="Number of countries")
+            conditions['Number of countries'] = nb_countries
+        
+        with col2 :
+            proportion = st.number_input("Proportion", min_value=0.10, max_value=1.00, value=0.75, key="proportion")
+            conditions['Proportion'] = proportion
+
+        with col3 :
+            countries = st.multiselect("Zone : ", options=("FRA", "BEL" ,"ESP","ITA", "BRA","ATA","ARG", "POL", "ROU", "BIG","SAM", 
+           "UAE","SAU","KWT","OMN","BHR","QAT","JOR","EGY","ARM","UZB","IRN","KEN","GEO","LEB","UGA","PAK","IRQ",
+           "MTQ","GLP","REU","GUA","MTS","GLS","GUF","MTA","GLA","GUS","SXM","DOM",
+           "MAR","AMA","TUN","DZA","TUR","IAP","IET","TWN"), key="countries")
+            conditions["Countries"] = countries
+
+        return conditions

app.py

@@ -1,213 +1,33 @@
 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 *
+import requests
 
-
-# Page configuration
+# Configuration
 st.set_page_config(
-    page_title="Gestion des ruptures",
-    page_icon="images/Carrefour_logo.png",
-    layout="wide"
+    page_title="Recherche",
+    page_icon="images/logo.png", 
+     layout="wide", 
+     initial_sidebar_state="auto"
+
 )
-hide_streamlit_style = """
+change_footer_style = """
             <style>
+                #MainMenu {visibility: hidden;}
                 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()
+st.markdown(change_footer_style, unsafe_allow_html=True)
+
+
+def get_product_info(EAN):
+    url = f"https://world.openfoodfacts.org/api/v0/product/{EAN}.json"
+    response = requests.get(url)
+    if response.status_code == 200:
+        return response.json()
+    else:
+        return {"error": "Product not found"}
+
+# Test de la fonction
+EAN =st.text_input("EAN", '0737628064502') # remplacer par l'EAN du produit
+if EAN :
+    product_info = get_product_info(EAN)
+    st.json(product_info)

app.yaml

@@ -1,2 +0,0 @@
-runtime: python310
-service: default

pages/recherche.py

@@ -1,33 +0,0 @@
-import streamlit as st 
-import requests
-
-# Configuration
-st.set_page_config(
-    page_title="Recherche",
-    page_icon="logo.png", 
-     layout="wide", 
-     initial_sidebar_state="auto"
-
-)
-change_footer_style = """
-            <style>
-                #MainMenu {visibility: hidden;}
-                footer {visibility: hidden;}
-            </style>
-            """
-st.markdown(change_footer_style, unsafe_allow_html=True)
-
-
-def get_product_info(EAN):
-    url = f"https://world.openfoodfacts.org/api/v0/product/{EAN}.json"
-    response = requests.get(url)
-    if response.status_code == 200:
-        return response.json()
-    else:
-        return {"error": "Product not found"}
-
-# Test de la fonction
-EAN =st.text_input("EAN", '0737628064502') # remplacer par l'EAN du produit
-if EAN :
-    product_info = get_product_info(EAN)
-    st.json(product_info)

pages/🦾_Gestion_de_rupture_sous_famille.py

@@ -0,0 +1,203 @@
+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 sous famille")
+
+        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 )
+                    data = data[data.COUNTRY_KEY.isin(conditions["Countries"])]
+                    Country, merged = nouvelle_data(data, 
+                                                    str(product_id), 
+                                                    str(class_id))
+
+                    merged_final = finale_merged(merged, 
+                                                 Country, 
+                                                 product_id, 
+                                                 class_id)            
+
+
+                    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()