import pandas as pd
import numpy as np
import os

import matplotlib.pyplot as plt
# %matplotlib inline

from matplotlib.pylab import rcParams
rcParams['figure.figsize']=20,10

from sklearn.preprocessing import MinMaxScaler
scaler=MinMaxScaler(feature_range=(0,1))

from keras.models import Sequential
from keras.layers import LSTM,Dropout,Dense

ROOTDIR = os.path.abspath(os.curdir)

def render_prediction(ticker):
    data_points = 987
    df=pd.read_csv(os.path.join(ROOTDIR, 'market_data', ticker + '.csv'))
    df.head()

    df["Date"]=pd.to_datetime(df.Date,format="%Y-%m-%d")
    df.index=df['Date']

    plt.switch_backend('agg')
    plt.figure(figsize=(16,8))
    plt.plot(df["Close"],label='Close Price history')

    data=df.sort_index(ascending=True,axis=0)
    new_dataset=pd.DataFrame(index=range(0,len(df)),columns=['Date','Close'])

    pd.set_option('mode.chained_assignment', None)

    for i in range(0,len(data)):
        new_dataset["Date"][i]=data['Date'][i]
        new_dataset["Close"][i]=data["Close"][i]
        

    new_dataset.index=new_dataset.Date
    new_dataset.drop("Date",axis=1,inplace=True)

    final_dataset=new_dataset.values

    if (len(final_dataset < 1000)):
        data_points = int(len(final_dataset) * 0.85)    

    train_data=final_dataset[0:data_points,:]
    valid_data=final_dataset[data_points:,:]

    scaler=MinMaxScaler(feature_range=(0,1))
    scaled_data=scaler.fit_transform(final_dataset)

    x_train_data,y_train_data=[],[]

    for i in range(60,len(train_data)):
        x_train_data.append(scaled_data[i-60:i,0])
        y_train_data.append(scaled_data[i,0])
        
    x_train_data,y_train_data=np.array(x_train_data),np.array(y_train_data)

    x_train_data=np.reshape(x_train_data,(x_train_data.shape[0],x_train_data.shape[1],1))

    lstm_model=Sequential()
    lstm_model.add(LSTM(units=50,return_sequences=True,input_shape=(x_train_data.shape[1],1)))
    lstm_model.add(LSTM(units=50))
    lstm_model.add(Dense(1))




    lstm_model.compile(loss='mean_squared_error',optimizer='adam')
    lstm_model.fit(x_train_data,y_train_data,epochs=1,batch_size=1,verbose=2)

    inputs_data=new_dataset[len(new_dataset)-len(valid_data)-60:].values
    inputs_data=inputs_data.reshape(-1,1)
    inputs_data=scaler.transform(inputs_data)


    X_test=[]
    for i in range(60,inputs_data.shape[0]):
        X_test.append(inputs_data[i-60:i,0])
    X_test=np.array(X_test)

    X_test=np.reshape(X_test,(X_test.shape[0],X_test.shape[1],1))
    closing_price=lstm_model.predict(X_test)
    closing_price=scaler.inverse_transform(closing_price)

    lstm_model.save("saved_lstm_model.h5")

    train_data=new_dataset[:data_points]
    valid_data=new_dataset[data_points:]
    valid_data.loc[:, 'Predictions'] = closing_price
    plt.plot(train_data["Close"])
    plt.plot(valid_data[['Close',"Predictions"]])
    plt.tight_layout()
    plt.savefig(os.path.join(ROOTDIR, 'static', ticker + '_prediction.png'))
    plt.close()

if __name__ == "__main__":
    pass