from statsmodels.tsa.arima_model import ARIMAResults
import streamlit as st
import pandas as pd
import yfinance as yf
import matplotlib.pyplot as plt
import numpy as np
import plotly.express as px
import time
from statsmodels.tsa.arima_model import ARIMA
from sklearn.metrics import mean_absolute_percentage_error, mean_squared_error, mean_absolute_error
import tensorflow as tf
from tensorflow import keras
from keras.models import Sequential
from keras.layers import Dense, LSTM
from keras import layers
from keras import wrappers
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.callbacks import ModelCheckpoint
from st_aggrid import GridOptionsBuilder, AgGrid
from style import add_logo

hide_menu_style = """
<style>
#MainMenu{visibility: hidden;}
footer{visibility:hidden;}
</style>
"""
# page expands to full width
st.set_page_config(page_title="Predicta.oil | Make a Model",
                   layout='wide', page_icon="⛽")
st.markdown(hide_menu_style, unsafe_allow_html=True)
# ag grid pagination
add_logo()


def pagination(df):
    gb = GridOptionsBuilder.from_dataframe(df)
    gb.configure_pagination(paginationAutoPageSize=True)
    return gb.build()


# PAGE LAYOUT
# heading
st.title("Make a Model")

# ARIMA PARAMETERS
pValue = 1
dValue = 0
qValue = 0

# show raw data
st.header("Raw Data")
# sidebar
# Sidebar - Specify parameter settings
with st.sidebar.header('Set Data Split'):
  # PARAMETERS min,max,default,skip
    trainData = st.sidebar.slider(
        'Data split ratio (% for Training Set)', 10, 90, 50, 5)
    # ARIMA PARAMETERS
    pValue = st.sidebar.number_input('P-value:', 0, 100, pValue)
    st.sidebar.write('The current p-Value is ', pValue)
    dValue = st.sidebar.number_input('D-value:', 0, 100, dValue)
    st.sidebar.write('The current d-Value is ', dValue)
    qValue = st.sidebar.number_input('Q-value:', 0, 100, qValue)
    st.sidebar.write('The current q-Value is ', qValue)
    details = st.sidebar.checkbox('Show Details')
    runModels = st.sidebar.button('Test Models')


# select time interval
interv = st.select_slider('Select Time Series Data Interval for Prediction', options=[
                          'Daily', 'Weekly', 'Monthly', 'Quarterly'], value='Weekly')


@st.cache
def getInterval(argument):
    switcher = {
        "W": "1wk",
        "M": "1mo",
        "Q": "3mo",
        "D": "1d"
    }
    return switcher.get(argument, "1d")


df = yf.download('BZ=F', interval=getInterval(interv[0]))
st.table(df.head())
# download full data


@st.cache
def convert_df(df):
    # IMPORTANT: Cache the conversion to prevent computation on every rerun
    return df.to_csv().encode('utf-8')


csv = convert_df(df)
# download full data
st.download_button(
    label="Download data as CSV",
    data=csv,
    file_name='Brent Oil Prices.csv',
    mime='text/csv',
)


# graph visualization
st.header("Visualizations")

# LSTM


def df_to_X_y(df, window_size=5):
    df_as_np = df.to_numpy()
    X = []
    y = []
    for i in range(len(df_as_np)-window_size):
        row = [[a] for a in df_as_np[i:i+window_size]]
        X.append(row)
        label = df_as_np[i+window_size]
        y.append(label)
    return np.array(X), np.array(y)


def mse_eval(test, predictions):
    return mean_squared_error(test, predictions)


def mape_eval(test, predictions):
    return mean_absolute_percentage_error(test, predictions)


def evaluate_lstm_model(split):
    global lstmModel
    WINDOW_SIZE = 3
    X1, y1 = df_to_X_y(df['Close'], WINDOW_SIZE)

    # preprocessing
    date_train, date_test = df.index[:int(
        df.shape[0]*split)], df.index[int(df.shape[0]*split)+WINDOW_SIZE:]
    X_train1, y_train1 = X1[:int(df.shape[0]*split)
                            ], y1[:int(df.shape[0]*split)]
    X_test1, y_test1 = X1[int(df.shape[0]*split):], y1[int(df.shape[0]*split):]

    # lstm model
    with st.spinner('LSTM Model...'):
        model = Sequential([layers.Input((3, 1)), layers.LSTM(64), layers.Dense(
            32, activation='relu'), layers.Dense(32, activation='relu'), layers.Dense(1)])
        cp1 = ModelCheckpoint('model1/', save_best_only=True)
        model.compile(loss='mse', optimizer=Adam(learning_rate=0.001),
                      metrics=['mean_absolute_percentage_error'])
        model.fit(X_train1, y_train1, epochs=100)

        lstmModel = model.summary()
        # train predictions
        train_predictions = model.predict(X_train1).flatten()
        train_results = pd.DataFrame(
            data={'Date': date_train, 'Close Prices': y_train1, 'Train Predictions': train_predictions})
        # train_results

        # test predictions
        test_predictions = model.predict(X_test1).flatten()
        test_results = pd.DataFrame(
            data={'Date': date_test, 'Close Prices': y_test1, 'LSTM Predictions': test_predictions})
        # test_results

        # evaluate model
        mse = mse_eval(test_results['Close Prices'],
                       test_results['LSTM Predictions'])
        mape = mape_eval(test_results['Close Prices'],
                         test_results['LSTM Predictions'])
        print(mse)
        print(mape)

    return test_results, mse, mape


global results


# ARIMA MODEL
def evaluate_arima_model(df, trainData):
    global arimamodsum
    try:
        with st.spinner('ARIMA Model...'):
            row = int(len(df)*(trainData*.01))  # 80% testing
            trainingData = list(df[0:row]['Close'])
            testingData = list(df[row:]['Close'])
            predictions = []
            nObservations = len(testingData)

            for i in range(nObservations):
                model = ARIMA(trainingData, order=(
                    pValue, dValue, qValue))  # p,d,q
                model_fit = model.fit()
                output = model_fit.forecast()
                yhat = list(output[0])[0]
                predictions.append(yhat)
                actualTestValue = testingData[i]
                trainingData.append(actualTestValue)

            arimamodsum = model_fit.summary()

            # st.write(predictions)
            testingSet = pd.DataFrame(testingData)
            testingSet['ARIMApredictions'] = predictions
            testingSet.columns = ['Close Prices', 'ARIMA Predictions']
            results["ARIMA Predictions"] = testingSet["ARIMA Predictions"]
            MSE = mean_squared_error(testingData, predictions)
            MAPE = mean_absolute_percentage_error(testingData, predictions)

            return MSE, MAPE
    except:
        st.error('Please select other ARIMA values as this is not possible.')
        st.stop()
        return()


# run models
# plot all results
if runModels:
    results, lstmMse, lstmMape = evaluate_lstm_model(trainData*.01)
    arimaMSE, arimaMAPE = evaluate_arima_model(df, trainData)

    # plot orig price and predicted price
    fig = px.line(results, x=results["Date"], y=["Close Prices", "ARIMA Predictions", "LSTM Predictions"],
                  title="BOTH PREDICTED BRENT CRUDE OIL PRICES", width=1000)
    st.plotly_chart(fig, use_container_width=True)

    # print(arimamodsum)

    # initialize session state
    if 'details_state' not in st.session_state:
        st.session_state.details_state = False
    # st.write(details)
    if details or st.session_state.details_state:
        st.session_state.details_state = True
        page = pagination(results)
        AgGrid(results, key='dailyCombined', fit_columns_on_grid_load=True,
               enable_enterprise_modules=True, theme='streamlit', gridOptions=page)
        st.write(arimamodsum)

    # ACCURACY METRICS
    accTable = pd.DataFrame()
    accTable['ARIMA-MAPE'] = [arimaMAPE]
    accTable['LSTM-MAPE'] = [lstmMape]
    accTable['ARIMA-MSE'] = [arimaMSE]
    accTable['LSTM-MSE'] = [lstmMse]

    # accuracy metrics
    st.header("Accuracy Metrics")

    st.table(accTable)