import pandas as pd
import numpy as np
from neuralprophet import NeuralProphet
from neuralprophet import set_random_seed
import matplotlib.pyplot as plt
import streamlit as st
from datetime import datetime as dt
# import os
# os.environ["KMP_DUPLICATE_LIB_OK"]="TRUE"

st.title('Time Series Forecasting with Neural Prophet')
option=st.selectbox('Choose from the following',['Forecasting without events','Forecasting with events'])

# try:
uploaded_file = st.sidebar.file_uploader("Upload your CSV file", type=["csv"])
@st.cache_data
def read_file(uploaded_file):
    data2=pd.read_csv(uploaded_file)
    return data2

df=read_file(uploaded_file)

##################################### Option 1 #####################################
if option=='Forecasting without events':

    daily_seasonality_btn = st.sidebar.select_slider('Daily Seasonality',options=[True, False],value=False)
    weekly_seasonality_btn = st.sidebar.select_slider('Weekly Seasonality',options=[True, False],value=True)
    yearly_seasonality_btn = st.sidebar.select_slider('Yearly Seasonality',options=[True, False],value=True)
    n_hist_pred_btn=st.sidebar.number_input('No. of Historical Data Points',0,360,30)
    epochs_btn=st.sidebar.number_input('Epochs',1,20,5)
    n_hidden_layers_btn=st.sidebar.number_input('No. of Hidden Layers',1,5,1)
    loss_fn_btn=st.sidebar.selectbox('Loss Function',['MAE','MSE','Huber'])
    seasonality_mode_btn=st.sidebar.selectbox('Seasonality Mode',['Additive','Multiplicative'])
    n_change_points_btn=st.sidebar.number_input('No. of Trend Change Points',0,360,30)

    with st.expander("Select Date & Observed Value",expanded=True):
        c1, c2 = st.columns((1, 1))
        x=c1.selectbox('Date',df.columns)
        ycols=[cols for cols in df.columns if cols!=df.columns[0] and df.dtypes[cols]!='object']
        y=c2.selectbox('Observed Value',ycols)

    with st.expander("Choose the Forecast Period with its Frequency"):
        c8, c9 = st.columns((1, 1))
        periods=int(c8.number_input('Forecast Period',0,365,60))
        freq=c9.selectbox('Frequency',["D","M","Y","s","min","H"])

    df1=df[[x,y]]
    df['ds'],df['y']=df[x],df[y]
    df=df[['ds','y']]
    df.dropna(inplace=True)
    df.drop_duplicates(subset=['ds'],inplace=True)
    df['ds']=pd.to_datetime(df['ds'])
    df.sort_values(by=['ds'],inplace=True)
    df=df.reset_index(drop=True)

    st.header('Dataset')
    st.dataframe(df1.head())
    rmp=st.radio('Run Model',['n','y'])

    if rmp=='y':
        set_random_seed(40)
        m = NeuralProphet(n_changepoints=n_change_points_btn,daily_seasonality=daily_seasonality_btn,weekly_seasonality=weekly_seasonality_btn,yearly_seasonality=yearly_seasonality_btn,seasonality_mode=seasonality_mode_btn,num_hidden_layers=n_hidden_layers_btn,loss_func=loss_fn_btn,epochs=epochs_btn,)
        # split into train & test dataset
        df_train, df_test = m.split_df(df, freq=freq,valid_p=0.2)
        train_metrics = m.fit(df_train, freq=freq,)
        test_metrics = m.test(df_test,)

        import warnings
        warnings.filterwarnings("ignore")
        future = m.make_future_dataframe(df=df, n_historic_predictions=n_hist_pred_btn,periods=periods)
        forecast = m.predict(df=future) 
        final_train_metrics=train_metrics.iloc[len(train_metrics)-1:len(train_metrics)].reset_index(drop=True)
        final_test_metrics=test_metrics.iloc[len(test_metrics)-1:len(test_metrics)].reset_index(drop=True)    

        fig = m.plot(forecast)
        fig_comp = m.plot_components(forecast)
        fig_param = m.plot_parameters()

        st.header('Train Dataset Metrics')
        st.dataframe(final_train_metrics)
        st.header('Test Dataset Metrics')
        st.dataframe(final_test_metrics)

        st.header('Forecast Values')
        st.pyplot(fig)

        st.header('Trend & Seasonality')
        st.pyplot(fig_param)
        st.dataframe(forecast)

        @st.cache_data
        def convert_df(df):
            return df.to_csv(index=False).encode('utf-8')

        # try:
        forecast_df = convert_df(forecast)
        if forecast_df is not None:
            st.download_button(label="Download data as CSV",data=forecast_df,file_name='NeuralProphet_with_events_results.csv',mime='text/csv',)
        # except:
        #     st.warning('Choose Something')

##################################### Option 2 #####################################
if option=='Forecasting with events':

    daily_seasonality_btn = st.sidebar.select_slider('Daily Seasonality',options=[True, False],value=False)
    weekly_seasonality_btn = st.sidebar.select_slider('Weekly Seasonality',options=[True, False],value=True)
    yearly_seasonality_btn = st.sidebar.select_slider('Yearly Seasonality',options=[True, False],value=True)
    n_hist_pred=st.sidebar.number_input('No. of Historical Data Points',0,360,30)
    epochs_btn=st.sidebar.number_input('Epochs',1,20,5)
    n_hidden_layers_btn=st.sidebar.number_input('No. of Hidden Layers',1,5,1)
    loss_fn_btn=st.sidebar.selectbox('Loss Function',['MAE','MSE','Huber'])
    n_change_points_btn=st.sidebar.number_input('No. of Trend Change Points',0,360,30)

    with st.expander("Select Date & Observed Value",expanded=True):
        c1, c2 = st.columns((1, 1))
        x=c1.selectbox('Date',df.columns)
        ycols=[cols for cols in df.columns if cols!=df.columns[0] and df.dtypes[cols]!='object']
        y=c2.selectbox('Observed Value',ycols)

    with st.expander("Select Event Names & their Dates"):
        c3, c4 = st.columns((1, 1))
        events1=c3.text_input(label='Event 1 Name',value='New Year Eve')
        eventd1=c3.date_input(label='Event 1 Date Range: ',value=(dt(year=1900, month=1, day=1), 
                            dt(year=2030, month=1, day=30)),)
        events2=c4.text_input(label='Event 2 Name',value='Christmas')
        eventd2=c4.date_input(label='Event 2 Date Range: ',value=(dt(year=1900, month=1, day=1), 
                            dt(year=2030, month=1, day=30)),)

    with st.expander("Select the Lower & Upper Window for the Events & Seasonality Factor"):
        c5, c6, c7 = st.columns((1, 1, 1))
        lw=c5.number_input('Lower Window',-10,0,-1)
        uw=c6.number_input('Upper Window',0,10,0)
        mode=c7.selectbox('Seasonality',['Additive','Multiplicative'])

    with st.expander("Choose the Forecast Period with its Frequency"):
        c8, c9 = st.columns((1, 1))
        periods=int(c8.number_input('Forecast Period',0,365,60))
        freq=c9.selectbox('Frequency',["D","M","Y","s","min","H"])

    df1=df[[x,y]]
    df['ds'],df['y']=df[x],df[y]
    df=df[['ds','y']]
    df.dropna(inplace=True)
    df.drop_duplicates(subset=['ds'],inplace=True)
    df['ds']=pd.to_datetime(df['ds'])
    df.sort_values(by=['ds'],inplace=True)
    df=df.reset_index(drop=True)

    st.header('Dataset')
    st.dataframe(df1.head())
    rmp=st.radio('Run Model',['n','y'])

    if rmp=='y':
        set_random_seed(40)
        m = NeuralProphet(n_changepoints=n_change_points_btn,daily_seasonality=daily_seasonality_btn,weekly_seasonality=weekly_seasonality_btn,yearly_seasonality=yearly_seasonality_btn,num_hidden_layers=n_hidden_layers_btn,loss_func=loss_fn_btn,epochs=epochs_btn,)
        event1 = pd.DataFrame({'event': events1,'ds': pd.to_datetime(eventd1).date})
        event2 = pd.DataFrame({'event': events2,'ds': pd.to_datetime(eventd2).date})
        if events2=='':
            enames=[events1]
            events_df = pd.concat([event1])
        else:
            enames=[events1,events2]
            events_df = pd.concat([event1,event2])

        events_df=events_df[events_df['event']!='']
        for i in range(len(enames)):
            if enames[i]!='':
                m=m.add_events([enames[i]],lower_window=lw,upper_window=uw,mode=mode)
        history_df = m.create_df_with_events(df, events_df)
        metrics=m.fit(history_df, freq=freq,)
        import warnings
        warnings.filterwarnings("ignore")            
        future = m.make_future_dataframe(df=history_df, events_df=events_df,n_historic_predictions=n_hist_pred,periods=periods)
        forecast = m.predict(df=future) 
        fig = m.plot(forecast)
        fig_comp = m.plot_components(forecast)
        fig_param = m.plot_parameters()

        final_metrics=metrics.iloc[len(metrics)-1:len(metrics)].reset_index(drop=True)

        st.header('Model Metrics')
        st.dataframe(final_metrics)

        st.header('Forecast Values')
        st.pyplot(fig)

        st.header('Trend & Seasonality')
        st.pyplot(fig_param)
        st.dataframe(forecast)

        @st.cache_data
        def convert_df(df):
            return df.to_csv(index=False).encode('utf-8')

        # try:
        forecast_df = convert_df(forecast)
        if forecast_df is not None:
            st.download_button(label="Download data as CSV",data=forecast_df,file_name='NeuralProphet_with_events_results.csv',mime='text/csv',)
        # except:
        #     st.warning('Choose Something')

#####################################################        
# except:
#     st.warning('Choose Something')

st.sidebar.write('### **About**')
st.sidebar.info(
 """
            Created by:
            [Parthasarathy Ramamoorthy](https://www.linkedin.com/in/parthasarathyr97/) (Data Scientist @ Walmart Global Tech)
        """)