Update pages/1_Store Demand Forecasting.py

pages/1_Store Demand Forecasting.py

@@ -177,7 +177,7 @@ if option=='TFT':
            
     #--------------------------------------------------------------------------------------------------------------
         # tabs
-        tab1,tab2,tab3=st.tabs(['📈Forecast Plot','🗃Forecast Table','🗃Actual Table']) #
+        tab1,tab2=st.tabs(['📈Forecast Plot','🗃Forecast Table']) #tab3-'🗃Actual Table'
     #------------------------------------------------Tab-1-----------------------------------------------------------
         tab1.markdown("""
                         <div style='text-align: left; margin-top:-10px;margin-bottom:-10px;'>
@@ -246,28 +246,28 @@ if option=='TFT':
                             key='download-csv'
                             )
     #--------------------------------Tab-3----------------------------------------------
-        tab3.markdown("""
-                        <div style='text-align: left; margin-top:-10px;margin-bottom:-10px;'>
-                        <h2 style='font-size: 30px; font-family: Palatino, serif;
-                        letter-spacing: 2px; text-decoration: none;'>
-                        &#x1F4C8;
-                        <span style='background: linear-gradient(45deg, #ed4965, #c05aaf);
-                              -webkit-background-clip: text;
-                              -webkit-text-fill-color: transparent;
-                              text-shadow: none;'>
-                        Actual Dataset
-                        </span>
-                        <span style='font-size: 40%;'>
-                        <sup style='position: relative; top: 5px; color: #ed4965;'></sup>
-                        </span>
-                        </h2>
-                        </div>
-                        """, unsafe_allow_html=True)
-        train_a=train_dataset.loc[(train_dataset['store']==store) & (train_dataset['item']==item)][['date','store','item','sales']]
-        test_a=test_dataset.loc[(test_dataset['store']==store) & (test_dataset['item']==item)][['date','store','item','sales']]
-        actual_final_data=pd.concat([train_a,test_a])
-        actual_final_data['date']=actual_final_data['date'].dt.date
-        tab3.dataframe(actual_final_data,width=500)
+        # tab3.markdown("""
+        #                 <div style='text-align: left; margin-top:-10px;margin-bottom:-10px;'>
+        #                 <h2 style='font-size: 30px; font-family: Palatino, serif;
+        #                 letter-spacing: 2px; text-decoration: none;'>
+        #                 &#x1F4C8;
+        #                 <span style='background: linear-gradient(45deg, #ed4965, #c05aaf);
+        #                       -webkit-background-clip: text;
+        #                       -webkit-text-fill-color: transparent;
+        #                       text-shadow: none;'>
+        #                 Actual Dataset
+        #                 </span>
+        #                 <span style='font-size: 40%;'>
+        #                 <sup style='position: relative; top: 5px; color: #ed4965;'></sup>
+        #                 </span>
+        #                 </h2>
+        #                 </div>
+        #                 """, unsafe_allow_html=True)
+        # train_a=train_dataset.loc[(train_dataset['store']==store) & (train_dataset['item']==item)][['date','store','item','sales']]
+        # test_a=test_dataset.loc[(test_dataset['store']==store) & (test_dataset['item']==item)][['date','store','item','sales']]
+        # actual_final_data=pd.concat([train_a,test_a])
+        # actual_final_data['date']=actual_final_data['date'].dt.date
+        # tab3.dataframe(actual_final_data,width=500)
         
     except:
         st.sidebar.error('Model Not Loaded successfully!',icon="🚨")           
@@ -397,7 +397,7 @@ elif option=='Prophet':
             st.dataframe(pd.DataFrame({"KPI":['RMSE','MAE'],"Prophet":[rmse,mae]}).set_index('KPI'),width=300)
        
         #---------------------------------------Tabs-----------------------------------------------------------------------
-        tab1,tab2,tab3=st.tabs(['📈Forecast Plot','🗃Forecast Table','🗃Actual Table']) # '🗃Actual Table'
+        tab1,tab2=st.tabs(['📈Forecast Plot','🗃Forecast Table']) #tab3- '🗃Actual Table'
         #-------------------------------------------Tab-1=Forecast plot---------------------------------------------------
         tab1.markdown("""
         <div style='text-align: left; margin-top:-10px;margin-bottom:-10px;'>
@@ -477,18 +477,18 @@ elif option=='Prophet':
                             )
 
     #------------------------------------------Tab-3--------------------------------------------------
-        train_a=fb_train_data.loc[fb_train_data[item]==1][['ds','sales']]
-        # train_a['store']=1
-        # train_a['item']=item
-        test_a=fb_test_data.loc[fb_test_data[item]==1][['ds','sales']]
-        # test_a['store']=1
-        # test_a['item']=item.split('_')[-1]
-        actual_final_data=pd.concat([train_a,test_a])
-        actual_final_data['store']=1
-        actual_final_data['item']=item.split('_')[-1]
-        actual_final_data['ds']=actual_final_data['ds'].dt.date
-        actual_final_data.rename({"ds":'date'},inplace=True)
-        tab3.dataframe(actual_final_data[['date','store','item','sales']],width=500)
+        # train_a=fb_train_data.loc[fb_train_data[item]==1][['ds','sales']]
+        # # train_a['store']=1
+        # # train_a['item']=item
+        # test_a=fb_test_data.loc[fb_test_data[item]==1][['ds','sales']]
+        # # test_a['store']=1
+        # # test_a['item']=item.split('_')[-1]
+        # actual_final_data=pd.concat([train_a,test_a])
+        # actual_final_data['store']=1
+        # actual_final_data['item']=item.split('_')[-1]
+        # actual_final_data['ds']=actual_final_data['ds'].dt.date
+        # actual_final_data.rename({"ds":'date'},inplace=True)
+        # tab3.dataframe(actual_final_data[['date','store','item','sales']],width=500)