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pages/1_Store Demand Forecasting.py

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+import streamlit as st
+from src.data import StoreDataLoader
+from src.model import Model_Load
+import matplotlib.pyplot as plt
+import seaborn as sns
+import plotly.graph_objects as go
+from sklearn.metrics import mean_absolute_error,mean_squared_error
+import numpy as np
+import pandas as pd
+from src.prediction import test_prediction,val_prediction,create_week_date_featues
+import plotly.express as px
+#-------------------------------------------------------------
+## Load model object
+model_obj=Model_Load()
+#--------------------------------------------------------------
+@st.cache_data
+def convert_df(df):
+   return df.to_csv(index=False).encode('utf-8')
+#-----------------------------------------------------------------
+## Title of Page
+st.markdown("""
+    <div style='text-align: center; margin-top:-70px; margin-bottom: -50px;margin-left: -50px;'>
+    <h2 style='font-size: 20px; font-family: Courier New, monospace;
+                    letter-spacing: 2px; text-decoration: none;'>
+    <img src="https://acis.affineanalytics.co.in/assets/images/logo_small.png" alt="logo" width="70" height="30">
+    <span style='background: linear-gradient(45deg, #ed4965, #c05aaf);
+                            -webkit-background-clip: text;
+                            -webkit-text-fill-color: transparent;
+                            text-shadow: none;'>
+                    Product Demand Forecasting Dashboard
+    </span>
+    <span style='font-size: 40%;'>
+    <sup style='position: relative; top: 5px; color: #ed4965;'>by Affine</sup>
+    </span>
+    </h2>
+    </div>
+    """, unsafe_allow_html=True)
+#---------------------------------------------------------------------------------------------------------------------
+# select the model(Sidebar)
+with st.sidebar:
+    st.markdown("""<div style='text-align: left; margin-top:-230px;margin-left:-40px;'>
+    <img src="https://affine.ai/wp-content/uploads/2023/05/Affine-Logo.svg" alt="logo" width="300" height="60">
+    </div>""", unsafe_allow_html=True)
+    option=st.selectbox("Select Model",['TFT','Prophet'])
+#------------------------------------------------------------------------------------------------------------
+# TFT 
+if option=='TFT':
+    #--------------------------------------------------------------------------------------------------------
+    ## TFT data path and load
+    path='data/train.csv'
+    obj=StoreDataLoader(path)
+    train_dataset,test_dataset,training,validation,earliest_time=obj.tft_data()
+    print(f"TRAINING ::START DATE ::{train_dataset['date'].min()} :: END DATE ::{train_dataset['date'].max()}")
+    print(f"TESTING ::START DATE ::{test_dataset['date'].min()} :: END DATE ::{test_dataset['date'].max()}")
+    list_store=train_dataset['store'].unique()
+    list_items=train_dataset['item'].unique()
+    #---------------------------------------------------------------------------------------------------------
+    try:
+        # load the pre trained tft model
+        model=model_obj.store_model_load(option)
+        with st.sidebar:
+            # st.success('Model Loaded successfully', icon="✅")
+            # select the store id
+            store=st.selectbox("Select Store ID",list_store)
+            # select the item id
+            item=st.selectbox("Select Product ID",list_items)
+    #--------------------------------------------------------------------------------------------------------------
+            ## prediction on testing data
+            testing_results=test_prediction(model,train_dataset=train_dataset,test_dataset=test_dataset
+                            ,earliest_time=earliest_time,store_id=store,item_id=item)
+            # find kpi
+            rmse=np.around(np.sqrt(mean_squared_error(testing_results['Lead_1'],testing_results['prediction'])),2)
+            mae=np.around(mean_absolute_error(testing_results['Lead_1'],testing_results['prediction']),2)
+            print(f"TEST DATA  = Item ID : {item} :: MAE : {mae} :: RMSE : {rmse}")
+ #--------------------------------------tft future prediction-------------------------------------------           
+            final_data=pd.concat([train_dataset,test_dataset])
+            consumer_data=final_data.loc[(final_data['store']==store) & (final_data['item']==item)]
+            consumer_data.fillna(0,inplace=True)
+            date_list=[]
+            demand_prediction=[]
+            for i in range(30):
+                # select last 150 records as an enocer + decoder data
+                encoder_data = consumer_data[lambda x: x.days_from_start > x.days_from_start.max() - 150]
+                last_data =  consumer_data[lambda x: x.days_from_start == x.days_from_start.max()]
+
+                # prediction date and time
+                date_list.append(encoder_data.tail(1).iloc[-1,:]['date'])
+                # prediction for the last 30 records
+                test_prediction = model.predict(encoder_data,
+                                                  mode="prediction",
+                                                  trainer_kwargs=dict(accelerator="cpu"),
+                                                  return_x=True)
+                # create the next day record
+                decoder_data = pd.concat(
+                    [last_data.assign(date=lambda x: x.date + pd.offsets.DateOffset(i)) for i in range(1, 2)],
+                    ignore_index=True,
+                )
+                # find the hours_from_start & days_from_start
+                decoder_data["hours_from_start"] = (decoder_data["date"] - earliest_time).dt.seconds / 60 / 60 + (decoder_data["date"] - earliest_time).dt.days * 24
+                decoder_data['hours_from_start'] = decoder_data['hours_from_start'].astype('int')
+                decoder_data["hours_from_start"] += encoder_data["hours_from_start"].max() + 1 - decoder_data["hours_from_start"].min()
+                # add time index consistent with "data"
+                decoder_data["days_from_start"] = (decoder_data["date"] - earliest_time).apply(lambda x:x.days)
+                # adding the datetime features
+                decoder_data=create_week_date_featues(decoder_data,'date')
+                # last timestep predicted record as assume next day actual demand(for more day forecasting)
+                decoder_data['sales']=float(test_prediction.output[0][-1])
+                # append this prediction into the list
+                demand_prediction.append(float(test_prediction.output[0][-1]))
+                # update prediction time idx
+                decoder_data['time_idx']=int(test_prediction.x['decoder_time_idx'][0][-1])
+                # add the next day record into the original data
+                consumer_data=pd.concat([consumer_data,decoder_data])
+                # fina lag and update
+                consumer_data['lag_1']=consumer_data['sales'].shift(1)
+                consumer_data['lag_5']=consumer_data['sales'].shift(5)
+                # reset the index
+                consumer_data=consumer_data.reset_index(drop=True)
+            # forecast values for the next 30 days/timesteps
+            d2=pd.DataFrame({"date":date_list,"prediction":demand_prediction})[['date','prediction']]
+            # update the store and item ids
+            d2['store']=store
+            d2['item']=item  
+#----------------------------TFT and Prophet model KPI----------------------------------------
+            with st.sidebar:
+                st.markdown(f"""
+                            <style>
+                            /* Sidebar header style */
+                            .sidebar-header {{
+                            padding: 1px;
+                            background-color: #9966FF;
+                            text-align: center;
+                            font-size: 13px;
+                            font-weight: bold;
+                            color: #FFF ;
+                            }}
+                            </style>
+
+                            <div class="sidebar-header">
+                            Models Evalution
+                            </div>
+                            """,unsafe_allow_html=True)
+                st.dataframe(pd.DataFrame({"KPI":['RMSE','MAE'],"TFT":[7.73,6.17],"Prophet":[7.32,6.01]}).set_index('KPI'),width=300) 
+                # d2=pd.DataFrame({"KPI":['RMSE','MAE','RMSE','MAE'],"model":['TFT','TFT','Prophet','Prophet'],"Score":[7.73,6.17,7.32,6.01]})
+                # fig = px.bar(d2, x="KPI", y="Score",
+                #  color='model', barmode='group',
+                #  height=200,width=300,text_auto=True,)
+                # st.plotly_chart(fig) 
+#------------------------------------Prophet model KPI---------------------------------------------------------
+                st.markdown(f"""
+                            <style>
+                            /* Sidebar header style */
+                            .sidebar-header {{
+                                padding: 3px;
+                                background-color:linear-gradient(45deg, #ed4965, #c05aaf);
+                                text-align: center;
+                                font-size: 13px;
+                                font-weight: bold;
+                                color: #FFF ;
+                            }}
+                            </style>
+
+                            <div class="sidebar-header">
+                            KPI :: {item}
+                            </div>
+                            """,unsafe_allow_html=True)
+                st.dataframe(pd.DataFrame({"KPI":['RMSE','MAE'],"TFT":[rmse,mae]}).set_index('KPI'),width=300)
+           
+    #--------------------------------------------------------------------------------------------------------------
+        # tabs
+        tab1,tab2=st.tabs(['📈Forecast Plot','🗃Forecast Table'])
+    #------------------------------------------------Tab-1-----------------------------------------------------------
+        tab1.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;'>
+                        Forecast Plot
+                        </span>
+                        <span style='font-size: 40%;'>
+                        <sup style='position: relative; top: 5px; color: #ed4965;'></sup>
+                        </span>
+                        </h2>
+                        </div>
+                        """, unsafe_allow_html=True)
+        # change dtype on prediction column
+        testing_results['prediction']=testing_results['prediction'].apply(lambda x:round(x))
+        testing_results['date']=testing_results['date'].dt.date
+        d2['prediction']=d2['prediction'].apply(lambda x:round(x))
+        d2['date']=d2['date'].dt.date
+        # training_data=train_dataset.loc[(train_dataset['store']==store)&(train_dataset['item']==item)][['date','Lead_1']].iloc[-60:,:]
+#---------------------------------------------forecast plot---------------------------------------------
+        fig = go.Figure([
+        # go.Scatter(x=training_data['date'],y=training_data['Lead_1'],name='Train Observed',line=dict(color='rgba(50, 205, 50, 0.7)')),
+        #go.Scatter(x=y_train_pred['ds'],y=y_train_pred['yhat'],name='Prophet Pred.(10 Item)',line=dict(color='blue', dash='dot')),
+        go.Scatter(x=testing_results['date'], y=testing_results['Lead_1'],name='Observed',line=dict(color='rgba(218, 112, 214, 0.5)')),
+        go.Scatter(x=testing_results['date'],y=testing_results['prediction'],name='Historical Forecast',line=dict(color='#9400D3', dash='dash')),
+         go.Scatter(x=d2['date'],y=d2['prediction'],name='Future Forecast',line=dict(color='Dark Orange', dash='dot'))])
+        fig.update_layout(
+        xaxis_title='Date',
+        yaxis_title='Order Demand',
+        margin=dict(l=0, r=0, t=50, b=0),
+        xaxis=dict(title_font=dict(size=20)),
+        yaxis=dict(title_font=dict(size=20)))
+        fig.update_layout(width=900,height=400)
+        tab1.plotly_chart(fig)
+    #----------------------------------------------Tab-2------------------------------------------------------------        
+        tab2.markdown("""
+        <div style='text-align: left; margin-top:-10px;'>
+        <h2 style='font-size: 30px; font-family: Palatino, serif;
+                        letter-spacing: 2px; text-decoration: none;'>
+                        &#x1F4C3;
+        <span style='background: linear-gradient(45deg, #ed4965, #c05aaf);
+                                -webkit-background-clip: text;
+                                -webkit-text-fill-color: transparent;
+                                text-shadow: none;'>
+                    Forecast Table
+        </span>
+        <span style='font-size: 40%;'>
+        <sup style='position: relative; top: 5px; color: #ed4965;'></sup>
+        </span>
+        </h2>
+        </div>
+        """, unsafe_allow_html=True)
+        final_r=pd.concat([d2[['date','store','item','prediction']],testing_results[['date','store','item','prediction']]]).sort_values('date').drop_duplicates().reset_index(drop=True)
+        csv = convert_df(final_r)
+        tab2.dataframe(final_r,width=500)
+        tab2.download_button(
+                            "Download",
+                            csv,
+                            "file.csv",
+                            "text/csv",
+                            key='download-csv'
+                            )
+    except:
+        st.sidebar.error('Model Not Loaded successfully!',icon="🚨")           
+        
+#+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
+#+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
+
+elif option=='Prophet':
+    print("prophet")
+    #---------------------------------------------------Data----------------------------------------------------
+    # Prophet data
+    path='data/train.csv'
+    obj=StoreDataLoader(path)
+    fb_train_data,fb_test_data,item_dummay,store_dummay=obj.fb_data()
+    # st.write(fb_train_data.columns)
+    # st.write(fb_test_data.columns)
+    # print(fb_test_data.columns)
+    print(f"TRAINING ::START DATE ::{fb_train_data['ds'].min()} :: END DATE ::{fb_train_data['ds'].max()}")
+    print(f"TESTING ::START DATE ::{fb_test_data['ds'].min()} :: END DATE ::{fb_test_data['ds'].max()}")
+    train_new=fb_train_data.drop('y',axis=1)
+    test_new=fb_test_data.drop('y',axis=1)
+    #----------------------------------------------model Load----------------------------------------------------
+    try:
+        fb_model=model_obj.store_model_load(option)
+        # with st.sidebar:
+            # st.success('Model Loaded successfully', icon="✅")
+    #-------------------------------------select store & item ---------------------------------------------------
+        list_items=item_dummay.columns
+        list_store=store_dummay.columns
+        with st.sidebar:
+            store=st.selectbox("Select Store",list_store)
+            item=st.selectbox("Select Product",list_items)
+        #------------------------------------------prediction---------------------------------------------------------------
+        test_prediction=fb_model.predict(test_new.loc[test_new[item]==1])
+        train_prediction=fb_model.predict(train_new.loc[train_new[item]==1])
+
+        y_true_test=fb_test_data.loc[fb_test_data[item]==1]
+        y_true_train=fb_train_data.loc[fb_train_data[item]==1]
+
+        y_train_pred=train_prediction[['ds','yhat']].iloc[-60:,:]
+        y_train_true=y_true_train[['ds','y']].iloc[-60:,:]
+
+        y_test_pred=test_prediction[['ds','yhat']]
+        y_test_true=y_true_test[['ds','y']]
+        #----------------------------------------KPI---------------------------------------------------------------
+        rmse=np.sqrt(mean_squared_error(y_test_true['y'],y_test_pred['yhat']))
+        mae=mean_absolute_error(y_test_true['y'],y_test_pred['yhat'])
+#---------------------------------future prediction---------------------------------------
+        fb_final=pd.concat([fb_train_data,fb_test_data])
+        # extract the data for selected store and item
+        fb_consumer=fb_final.loc[(fb_final[store]==1) & (fb_final[item]==1)]
+
+        # list of dates and prediction
+        date_list=[]
+        prediction_list=[]
+
+        # predicting the next 30 days product demand
+        for i in range(30):
+            # select only date record
+            next_prediction=fb_consumer.tail(1).drop('y',axis=1) # drop target of last  01/01/2015 00:00:00
+            # predict next timestep demand
+            prediction=fb_model.predict(next_prediction) # pass other feature value to the model
+            
+            # append date and predicted demand
+            date_list.append(prediction['ds'][0]) ## append the datetime of prediction
+            prediction_list.append(prediction['yhat'][0]) ## append the next timestep prediction
+            
+            
+            #--------------------------next timestep data simulate-------------------------------------------------------------
+            last_data =  fb_consumer[lambda x: x.ds == x.ds.max()] # last date present in data
+            # next timestep
+            decoder_data = pd.concat(
+                [last_data.assign(ds=lambda x: x.ds + pd.offsets.DateOffset(i)) for i in range(1, 2)],
+                ignore_index=True,
+            )
+            # update next timestep datetime covariates
+            decoder_data=create_week_date_featues(decoder_data,'ds')
+            # update last day demand prediction to the here as an actual demand value(using for more future timestep prediction)
+            decoder_data['sales']=prediction['yhat'][0] # assume next timestep prediction as actual
+            # update this next record into the original data
+            fb_consumer=pd.concat([fb_consumer,decoder_data]) # append that next timestep data to into main data
+            # find shift of power usage and update into the datset
+            fb_consumer['lag_1']=fb_consumer['sales'].shift(1)  
+            fb_consumer['lag_5']=fb_consumer['sales'].shift(5)  
+            fb_consumer=fb_consumer.reset_index(drop=True)  # reset_index
+        future_prediction=pd.DataFrame({"ds":date_list,"yhat":prediction_list})
+        future_prediction['store']=store
+        future_prediction['item']=item
+
+        with st.sidebar:
+            st.markdown(f"""
+                        <style>
+                        /* Sidebar header style */
+                        .sidebar-header {{
+                        padding: 1px;
+                        background-color: #9966FF;
+                        text-align: center;
+                        font-size: 13px;
+                        font-weight: bold;
+                        color: #FFF ;
+                        }}
+                        </style>
+
+                        <div class="sidebar-header">
+                        Models Evalution
+                        </div>
+                        """,unsafe_allow_html=True)
+            st.dataframe(pd.DataFrame({"KPI":['RMSE','MAE'],"TFT":[7.73,6.17],"Prophet":[7.32,6.01]}).set_index('KPI'),width=300) 
+            st.markdown(f"""
+            <style>
+            /* Sidebar header style */
+            .sidebar-header {{
+                padding: 3px;
+                background-color:linear-gradient(45deg, #ed4965, #c05aaf);
+                text-align: center;
+                font-size: 13px;
+                font-weight: bold;
+                color: #FFF ;
+            }}
+            </style>
+
+            <div class="sidebar-header">
+            KPI :: {item}
+            </div>
+            """,unsafe_allow_html=True)
+            
+            st.dataframe(pd.DataFrame({"KPI":['RMSE','MAE'],"Prophet":[rmse,mae]}).set_index('KPI'),width=300)
+       
+        #---------------------------------------Tabs-----------------------------------------------------------------------
+        tab1,tab2=st.tabs(['📈Forecast Plot','🗃Forecast Table'])
+        #-------------------------------------------Tab-1=Forecast plot---------------------------------------------------
+        tab1.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;'>
+                        Forecast Plot
+        </span>
+        <span style='font-size: 40%;'>
+        <sup style='position: relative; top: 5px; color: #ed4965;'></sup>
+        </span>
+        </h2>
+        </div>
+        """, unsafe_allow_html=True)
+
+        ## round fig.
+        y_train_true['y']=y_train_true['y'].astype('int')
+        y_train_pred['yhat']=y_train_pred['yhat'].astype('int')
+        y_test_true['y']=y_test_true['y'].astype('int')
+        y_test_pred['yhat']=y_test_pred['yhat'].astype('int')
+        future_prediction['yhat']=future_prediction['yhat'].astype('int')
+        y_train_true['ds']=y_train_true['ds'].dt.date
+        y_train_pred['ds']=y_train_pred['ds'].dt.date
+        y_test_true['ds']=y_test_true['ds'].dt.date
+        y_test_pred['ds']=y_test_pred['ds'].dt.date
+        future_prediction['ds']=future_prediction['ds'].dt.date
+
+        #-----------------------------plot---------------------------------------------------------------------------------------------
+        fig = go.Figure([
+        # go.Scatter(x=y_train_true['ds'],y=y_train_true['y'],name='Train Observed',line=dict(color='rgba(50, 205, 50, 0.7)' )),
+        # go.Scatter(x=y_train_pred['ds'],y=y_train_pred['yhat'],name='Prophet Pred.(10 Item)',line=dict(color='#32CD32', dash='dot')),
+        go.Scatter(x=y_test_true['ds'], y=y_test_true['y'],name='Observed',line=dict(color='rgba(218, 112, 214, 0.5)')),
+        go.Scatter(x=y_test_pred['ds'],y=y_test_pred['yhat'],name='Historical Forecast',line=dict(color='#9400D3', dash='dash')),
+        go.Scatter(x=future_prediction['ds'],y=future_prediction['yhat'],name='Future Forecast',line=dict(color='Dark Orange', dash='dot'))])
+        fig.update_layout(
+            xaxis_title='Date',
+        yaxis_title='Order Demand',
+        margin=dict(l=0, r=0, t=50, b=0),
+        xaxis=dict(title_font=dict(size=20)),
+        yaxis=dict(title_font=dict(size=20)))
+        fig.update_layout(width=900,height=400)
+        tab1.plotly_chart(fig)
+        #----------------------------------------Tab-2------------------------------------------------------------
+        results=y_test_pred.reset_index()
+        results['store']='store_1'
+        results['item']=item
+        tab2.markdown("""
+        <div style='text-align: left; margin-top:-10px;'>
+        <h2 style='font-size: 30px; font-family: Palatino, serif;
+                        letter-spacing: 2px; text-decoration: none;'>
+                        &#x1F4C3;
+        <span style='background: linear-gradient(45deg, #ed4965, #c05aaf);
+                                -webkit-background-clip: text;
+                                -webkit-text-fill-color: transparent;
+                                text-shadow: none;'>
+                    Forecast Table
+        </span>
+        <span style='font-size: 40%;'>
+        <sup style='position: relative; top: 5px; color: #ed4965;'></sup>
+        </span>
+        </h2>
+        </div>
+        """, unsafe_allow_html=True)
+        final_r=pd.concat([future_prediction[['ds','store','item','yhat']],results[['ds','store','item','yhat']]]).sort_values('ds').drop_duplicates().reset_index(drop=True)
+        csv = convert_df(final_r)
+        tab2.dataframe(final_r,width=500)
+        tab2.download_button(
+                            "Download",
+                            csv,
+                            "file.csv",
+                            "text/csv",
+                            key='download-csv'
+                            )
+    except:
+        st.sidebar.error('Model Not Loaded successfully!',icon="🚨")
+
+
+

pages/2_Energy Demand Forecasting.py

@@ -0,0 +1,436 @@
+import streamlit as st
+from src.data import Energy_DataLoader
+from src.model import Model_Load
+import matplotlib.pyplot as plt
+import seaborn as sns
+import plotly.graph_objects as go
+from sklearn.metrics import mean_absolute_error,mean_squared_error
+import numpy as np
+import pandas as pd
+from streamlit.components.v1 import html
+from src.prediction import test_pred,val_pred
+## Load model object
+model_obj=Model_Load()
+
+
+path='data/LD2011_2014.txt'
+obj=Energy_DataLoader(path)
+
+@st.cache_data
+def convert_df(df):
+   return df.to_csv(index=False).encode('utf-8')
+
+
+st.markdown("""
+    <div style='text-align: center; margin-top:-70px; margin-bottom: 5px;margin-left: -50px;'>
+    <h2 style='font-size: 20px; font-family: Courier New, monospace;
+                    letter-spacing: 2px; text-decoration: none;'>
+    <img src="https://acis.affineanalytics.co.in/assets/images/logo_small.png" alt="logo" width="70" height="30">
+    <span style='background: linear-gradient(45deg, #ed4965, #c05aaf);
+                            -webkit-background-clip: text;
+                            -webkit-text-fill-color: transparent;
+                            text-shadow: none;'>
+                    Energy Demand Forecasting Dashboard
+    </span>
+    <span style='font-size: 40%;'>
+    <sup style='position: relative; top: 5px; color: #ed4965;'>by Affine</sup>
+    </span>
+    </h2>
+    </div>
+    """, unsafe_allow_html=True)
+
+with st.sidebar:
+    st.markdown("""<div style='text-align: left; margin-top:-230px;margin-left:-40px;'>
+    <img src="https://affine.ai/wp-content/uploads/2023/05/Affine-Logo.svg" alt="logo" width="300" height="60">
+    </div>""", unsafe_allow_html=True)
+#     st.markdown(f"""<style>
+#   /* Sidebar header style */
+#   .sidebar-header {{
+#     padding: 1px;
+#     background-color: #9966FF;
+#     text-align: center;
+#     font-size: 13px;
+#     font-weight: bold;
+#     color: #FFF ;
+#   }}
+# </style>
+
+# <div class="sidebar-header" >
+#   Select Model
+# </div>
+# """,unsafe_allow_html=True)
+    option=st.selectbox("Select Model",['TFT','Prophet'])
+
+if option=='TFT':
+    print("TFT")
+    ## TFT data
+    train_dataset,test_dataset,training,validation,earliest_time=obj.tft_data()
+    # st.write(earliest_time)
+    print(f"TRAINING ::START DATE ::{train_dataset['date'].min()} :: END DATE ::{train_dataset['date'].max()}")
+    print(f"TESTING ::START DATE ::{test_dataset['date'].min()} :: END DATE ::{test_dataset['date'].max()}")
+    consumer_list=train_dataset['consumer_id'].unique()
+    model=model_obj.energy_model_load(option)
+    with st.sidebar:
+        # st.success('Model Loaded successfully', icon="✅")
+        # st.markdown(f"""
+        #                 <style>
+        #                 /* Sidebar header style */
+        #                 .sidebar-header {{
+        #                 padding: 1px;
+        #                 background-color: #9966FF;
+        #                 text-align: center;
+        #                 font-size: 13px;
+        #                 font-weight: bold;
+        #                 color: #FFF ;
+        #                 }}
+        #                 </style>
+
+        #                 <div class="sidebar-header">
+        #                 Select Consumer ID
+        #                 </div>
+        #                 """,unsafe_allow_html=True)
+        consumer=st.selectbox("Select Consumer ID",consumer_list)
+        testing_results=test_pred(model,train_dataset=train_dataset,test_dataset=test_dataset
+                        ,consumer_id=consumer)
+        rmse=np.around(np.sqrt(mean_squared_error(testing_results['Lead_1'],testing_results['prediction'])),2)
+        mae=np.around(mean_absolute_error(testing_results['Lead_1'],testing_results['prediction']),2)
+#-----------------------------------future prediction-----------------------------------------------
+        final_data=pd.concat([train_dataset,test_dataset])
+        consumer_data=final_data.loc[final_data['consumer_id']==consumer]
+        consumer_data.fillna(0,inplace=True)
+        date_list=[]
+        demand_prediction=[]
+        for i in range(24):
+            encoder_data = consumer_data[lambda x: x.hours_from_start > x.hours_from_start.max() - 192]
+            last_data =  consumer_data[lambda x: x.hours_from_start == x.hours_from_start.max()]
+
+            # prediction date and time
+            date_list.append(encoder_data.tail(1).iloc[-1,:]['date'])
+
+            test_prediction = model.predict(encoder_data,
+                                              mode="prediction",
+                                              trainer_kwargs=dict(accelerator="cpu"),
+                                              return_x=True)
+            decoder_data = pd.concat(
+                [last_data.assign(date=lambda x: x.date + pd.offsets.Hour(i)) for i in range(1, 2)],
+                ignore_index=True,
+            )
+            decoder_data['hours_from_start']=decoder_data['hours_from_start'].max()+1
+            decoder_data["days_from_start"] = (decoder_data["date"] - earliest_time).apply(lambda x:x.days)
+            decoder_data['hour'] = decoder_data['date'].dt.hour
+            decoder_data['day'] = decoder_data['date'].dt.day
+            decoder_data['day_of_week'] = decoder_data['date'].dt.dayofweek
+            decoder_data['month'] = decoder_data['date'].dt.month
+            decoder_data['power_usage']=float(test_prediction.output[0][-1])
+            demand_prediction.append(float(test_prediction.output[0][-1]))
+            decoder_data['time_idx']=int(test_prediction.x['decoder_time_idx'][0][-1])
+            consumer_data=pd.concat([consumer_data,decoder_data])
+            consumer_data['lag_1']=consumer_data['power_usage'].shift(1)
+            consumer_data['lag_5']=consumer_data['power_usage'].shift(5)
+            consumer_data=consumer_data.reset_index(drop=True)
+        d2=pd.DataFrame({"date":date_list,"prediction":demand_prediction})[['date','prediction']]
+        d2['consumer_id']=consumer
+        print(f"TEST DATA  = Consumer ID : {consumer} :: MAE : {mae} :: RMSE : {rmse}")
+        with st.sidebar:
+            st.markdown(f"""
+                        <style>
+                        /* Sidebar header style */
+                        .sidebar-header {{
+                        padding: 1px;
+                        background-color: #9966FF;
+                        text-align: center;
+                        font-size: 13px;
+                        font-weight: bold;
+                        color: #FFF ;
+                        }}
+                        </style>
+
+                        <div class="sidebar-header">
+                        Models Evalution
+                        </div>
+                        """,unsafe_allow_html=True)
+            # st.write("Models Evalution")
+            st.dataframe(pd.DataFrame({"KPI":['RMSE','MAE'],"TFT":[8.67,6.48],"Prophet":[12.82,9.79]}).set_index('KPI'),width=300) 
+            st.markdown(f"""
+                        <style>
+                        /* Sidebar header style */
+                        .sidebar-header {{
+                            padding: 1px;
+                            background-color:linear-gradient(45deg, #ed4965, #c05aaf);
+                            text-align: center;
+                            font-size: 13px;
+                            font-weight: bold;
+                            color: #FFF ;
+                        }}
+                        </style>
+
+                        <div class="sidebar-header">
+                        KPI :: {consumer}
+                        </div>
+                        """,unsafe_allow_html=True)
+            st.dataframe(pd.DataFrame({"KPI":['RMSE','MAE'],"TFT":[rmse,mae]}).set_index('KPI'),width=300)
+#--------------------------------------------------------------------------------------------------------------
+    # tabs
+    tab1,tab2=st.tabs(['📈Forecast Plot','🗃Forecast Table'])
+#------------------------------------------------Tab-1-----------------------------------------------------------
+    # tab2.write(testing_results)
+    tab1.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;'>
+                    Forecast Plot
+    </span>
+    <span style='font-size: 40%;'>
+    <sup style='position: relative; top: 5px; color: #ed4965;'></sup>
+    </span>
+    </h2>
+    </div>
+    """, unsafe_allow_html=True)
+    # testing_results['prediction']=testing_results['prediction'].astype('int')
+    training_data=train_dataset.loc[(train_dataset['consumer_id']==consumer)][['date','Lead_1']].iloc[-100:,:]
+    fig = go.Figure([
+    # go.Scatter(x=training_data['date'],y=training_data['Lead_1'],name='Train Observed',line=dict(color='blue')),
+    #go.Scatter(x=y_train_pred['ds'],y=y_train_pred['yhat'],name='Prophet Pred.(10 Item)',line=dict(color='blue', dash='dot')),
+    go.Scatter(x=testing_results['date'], y=testing_results['Lead_1'],name='Observed',line=dict(color='purple')),
+    go.Scatter(x=testing_results['date'],y=testing_results['prediction'],name='Historical Forecast',line=dict(color='purple', dash='dot')),
+    go.Scatter(x=d2['date'],y=d2['prediction'],name='Future Forecast',line=dict(color='Dark Orange', dash='dot'))])
+    fig.update_layout(
+    xaxis_title='Date',
+    yaxis_title='Energy Demand',
+    margin=dict(l=0, r=0, t=50, b=0),
+    xaxis=dict(title_font=dict(size=20)),
+    yaxis=dict(title_font=dict(size=20)))
+    fig.update_layout(width=900,height=400)
+    tab1.plotly_chart(fig)
+#----------------------------------------------Tab-2------------------------------------------------------------        
+    tab2.markdown("""
+    <div style='text-align: left; margin-top:-10px;'>
+    <h2 style='font-size: 30px; font-family: Palatino, serif;
+                    letter-spacing: 2px; text-decoration: none;'>
+                    &#x1F4C3;
+    <span style='background: linear-gradient(45deg, #ed4965, #c05aaf);
+                            -webkit-background-clip: text;
+                            -webkit-text-fill-color: transparent;
+                            text-shadow: none;'>
+                Forecast Table
+    </span>
+    <span style='font-size: 40%;'>
+    <sup style='position: relative; top: 5px; color: #ed4965;'></sup>
+    </span>
+    </h2>
+    </div>
+    """, unsafe_allow_html=True)
+    final_r=pd.concat([d2[['date','consumer_id','prediction']],testing_results[['date','consumer_id','prediction']]]).sort_values('date').reset_index(drop=True)
+    csv = convert_df(final_r)
+    tab2.dataframe(final_r,width=500)
+    tab2.download_button(
+                        "Download",
+                        csv,
+                        "file.csv",
+                        "text/csv",
+                        key='download-csv'
+                        )
+# except:
+#     st.sidebar.error('Model Not Loaded successfully!',icon="🚨") 
+
+
+
+
+
+elif option=='Prophet':
+    print("prophet")
+    # Prophet data
+    fb_train_data,fb_test_data,consumer_dummay=obj.fb_data()
+    # print('*'*50)
+    # fb_test_data
+    # print('*'*50)
+    print(f"TRAINING ::START DATE ::{fb_train_data['ds'].min()} :: END DATE ::{fb_train_data['ds'].max()}")
+    print(f"TESTING ::START DATE ::{fb_test_data['ds'].min()} :: END DATE ::{fb_test_data['ds'].max()}")
+    train_new=fb_train_data.drop('y',axis=1)
+    test_new=fb_test_data.drop('y',axis=1)
+    try:
+        model=model_obj.energy_model_load(option)
+        # with st.sidebar:
+        #     st.success('Model Loaded successfully.', icon="✅")
+    except:
+        st.error('Model Not Loaded successfully!',icon="🚨")
+    with st.sidebar:
+        # st.markdown(f"""
+        #                 <style>
+        #                 /* Sidebar header style */
+        #                 .sidebar-header {{
+        #                 padding: 2px;
+        #                 background-color: #9966FF;
+        #                 text-align: center;
+        #                 font-size: 8px;
+        #                 font-weight: bold;
+        #                 color: #FFF ;
+        #                 }}
+        #                 </style>
+
+        #                 <div class="sidebar-header">
+        #                 Select Consumer ID
+        #                 </div>
+        #                 """,unsafe_allow_html=True)
+        consumer=st.selectbox("Select Consumer ID",consumer_dummay)
+
+    test_prediction=model.predict(test_new.loc[test_new[consumer]==1])
+    # train_prediction=model.predict(train_new.loc[train_new[consumer]==1])
+
+    y_true_test=fb_test_data.loc[fb_test_data[consumer]==1]
+    y_true_train=fb_train_data.loc[fb_train_data[consumer]==1]
+
+    # y_train_pred=train_prediction[['ds','yhat']].iloc[-60:,:]
+    y_train_true=y_true_train[['ds','y']].iloc[-60:,:]
+
+    y_test_pred=test_prediction[['ds','yhat']]
+    y_test_true=y_true_test[['ds','y']]
+
+    fb_final=pd.concat([fb_train_data,fb_test_data])
+    fb_consumer=fb_final.loc[fb_final[consumer]==1]
+    date_list=[]
+    prediction_list=[]
+    for i in range(24):
+        next_prediction=fb_consumer.tail(1).drop('y',axis=1) # drop target of last  01/01/2015 00:00:00
+    #     print(next_prediction)
+        prediction=model.predict(next_prediction) # pass other feature value to the model
+    #     print('*'*20)
+    #     print("DateTime :: ",prediction['ds'][0])
+    #     print("Prediction ::",prediction['yhat'][0])
+        date_list.append(prediction['ds'][0]) ## append the datetime of prediction
+        prediction_list.append(prediction['yhat'][0]) ## append the next timestep prediction
+        
+        last_data =  fb_consumer[lambda x: x.ds == x.ds.max()] # last date present in data
+        
+        #--------------------------next timestep data simulate-------------------------------------------------------------
+        decoder_data = pd.concat(
+            [last_data.assign(ds=lambda x: x.ds + pd.offsets.Hour(i)) for i in range(1, 2)],
+            ignore_index=True,
+        )
+        decoder_data['hour'] = decoder_data['ds'].dt.hour
+        decoder_data['day'] = decoder_data['ds'].dt.day
+        decoder_data['day_of_week'] = decoder_data['ds'].dt.dayofweek
+        decoder_data['month'] = decoder_data['ds'].dt.month
+        decoder_data['power_usage']=prediction['yhat'][0] # assume next timestep prediction as actual
+        fb_consumer=pd.concat([fb_consumer,decoder_data]) # append that next timestep data to into main data
+        fb_consumer['lag_1']=fb_consumer['power_usage'].shift(1) # again find shift of power usage and update into the datset
+        fb_consumer['lag_5']=fb_consumer['power_usage'].shift(5) # 
+        fb_consumer=fb_consumer.reset_index(drop=True)
+    future_prediction=pd.DataFrame({'ds':date_list,"yhat":prediction_list}) 
+    future_prediction['consumer_id']=consumer 
+    tab1,tab2=st.tabs(['📈Forecast Plot','🗃Forecast Table'])
+    tab1.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;'>
+            Forecast Plot
+            </span>
+            <span style='font-size: 40%;'>
+            <sup style='position: relative; top: 5px; color: #ed4965;'></sup>
+            </span>
+            </h2>
+            </div>
+            """, unsafe_allow_html=True)
+    y_train_true['y']=y_train_true['y'].astype('float')
+    # y_train_pred['yhat']=y_train_pred['yhat'].astype('float')
+    y_test_true['y']=y_test_true['y'].astype('float')
+    y_test_pred['yhat']=y_test_pred['yhat'].astype('float')
+    
+    fig = go.Figure([
+    # go.Scatter(x=y_train_true['ds'],y=y_train_true['y'],name='Train Observed',line=dict(color='blue')),
+    #go.Scatter(x=y_train_pred['ds'],y=y_train_pred['yhat'],name='Prophet Pred.(10 Consumer)',line=dict(color='blue', dash='dot')),
+    go.Scatter(x=y_test_true['ds'], y=y_test_true['y'],name='Observed',line=dict(color='purple')),
+   go.Scatter(x=y_test_pred['ds'],y=y_test_pred['yhat'],name='Historical Forecast',line=dict(color='purple', dash='dot')),
+   go.Scatter(x=future_prediction['ds'],y=future_prediction['yhat'],name='Future Forecast',line=dict(color='Dark Orange', dash='dot'))
+   ])
+    fig.update_layout(
+        xaxis_title='Date',
+    yaxis_title='Energy Demand',
+    margin=dict(l=0, r=0, t=50, b=0),
+    xaxis=dict(title_font=dict(size=20)),
+    yaxis=dict(title_font=dict(size=20)))
+    fig.update_layout(width=900,height=400)
+    tab1.plotly_chart(fig)
+
+    rmse=np.sqrt(mean_squared_error(y_test_true['y'],y_test_pred['yhat']))
+    mae=mean_absolute_error(y_test_true['y'],y_test_pred['yhat'])
+    with st.sidebar:
+      st.markdown(f"""
+                        <style>
+                        /* Sidebar header style */
+                        .sidebar-header {{
+                        padding: 1px;
+                        background-color: #9966FF;
+                        text-align: center;
+                        font-size: 13px;
+                        font-weight: bold;
+                        color: #FFF ;
+                        }}
+                        </style>
+
+                        <div class="sidebar-header">
+                        Models Evalution
+                        </div>
+                        """,unsafe_allow_html=True)
+            # st.write("Models Evalution")
+      st.dataframe(pd.DataFrame({"KPI":['RMSE','MAE'],"TFT":[8.67,6.48],"Prophet":[12.82,9.79]}).set_index('KPI'),width=300) 
+      st.markdown(f"""
+                <style>
+                  /* Sidebar header style */
+                  .sidebar-header {{
+                    padding: 2px;
+                    background-color:linear-gradient(45deg, #ed4965, #c05aaf);
+                    text-align: center;
+                    font-size: 13px;
+                    font-weight: bold;
+                    color: #FFF ;
+                  }}
+                </style>
+
+                <div class="sidebar-header">
+                  KPI :: {consumer}
+                </div>
+                """,unsafe_allow_html=True)
+      st.dataframe(pd.DataFrame({"KPI":['RMSE','MAE'],"Prophet":[rmse,mae]}), width=300)
+    #----------------------------------------
+    results=y_test_pred.reset_index()
+    # results['y']=y_test_true['y'].reset_index(drop=True)
+    results['consumer_id']=consumer
+    # st.header("Tabular Results")
+    st.divider()
+
+    tab2.markdown("""
+    <div style='text-align: left; margin-top:-10px;'>
+    <h2 style='font-size: 30px; font-family: Palatino, serif;
+                    letter-spacing: 2px; text-decoration: none;'>
+                    &#x1F4C3;
+    <span style='background: linear-gradient(45deg, #ed4965, #c05aaf);
+                            -webkit-background-clip: text;
+                            -webkit-text-fill-color: transparent;
+                            text-shadow: none;'>
+                   Forecast Table
+    </span>
+    <span style='font-size: 40%;'>
+    <sup style='position: relative; top: 5px; color: #ed4965;'></sup>
+    </span>
+    </h2>
+    </div>
+    """, unsafe_allow_html=True)
+    final_results=pd.concat([future_prediction[['ds','consumer_id','yhat']],results[['ds','consumer_id','yhat']]]).sort_values('ds').reset_index(drop=True)
+    csv = convert_df(final_results)
+    tab2.dataframe(final_results,width=500)
+    tab2.download_button("Download",
+                        csv,
+                        "file.csv",
+                        "text/csv",
+                        key='download-csv')