import numpy as np

import matplotlib.pyplot as plt

import pandas as pd

import pandas_datareader as web

import datetime as dt

from sklearn.preprocessing import MinMaxScaler

from tensorflow.keras.layers import Dense, Dropout, LSTM

from tensorflow.keras.models import Sequential

def predd2(crp, cur):

crypto_curr = crp

curr = cur

start = dt.datetime(2016,1,1)

end = dt.datetime.now()

data = web.DataReader(f'{crypto_curr}-{curr}', 'yahoo', start, end)

#Prepare data squeeze in -1 -- 0 -- 1

print(data.head())

scaler = MinMaxScaler(feature_range=(0,1))

scaled_data = scaler.fit_transform(data['Close'].values.reshape(-1,1))

pred_days = 90

x_train, y_train = [], []

for x in range(pred_days, len(scaled_data)):

x_train.append(scaled_data[x-pred_days:x, 0])

y_train.append(scaled_data[x, 0])

x_train, y_train = np.array(x_train), np.array(y_train)

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

#NN

#numpy == 1.19.5

#model =

model = Sequential()

model.add(LSTM(units = 50, return_sequences = True, input_shape = (x_train.shape[1], 1)))

model.add(Dropout(0.2))

model.add(LSTM(units = 50, return_sequences = True))

model.add(Dropout(0.2))

model.add(LSTM(units = 50))

model.add(Dropout(0.2))

model.add(Dense(units = 1))

model.compile(optimizer = 'adam', loss = 'mean_squared_error')

#model.fit(x_train, y_train, epochs = 50, batch_size = 32)

#model.fit(x_train, y_train, epochs = 25, batch_size = 32)

model.fit(x_train, y_train, epochs = 2, batch_size = 32)

#Testing model

test_s = dt.datetime(2020,1,1)

test_e = dt.datetime.now()

test_data = web.DataReader(f'{crypto_curr}-{curr}', 'yahoo', test_s, test_e)

act_prices = test_data['Close'].values

total_dataset = pd.concat((data['Close'], test_data['Close']), axis = 0)

model_inputs = total_dataset[len(total_dataset) - len(test_data) - pred_days:].values

model_inputs = model_inputs.reshape(-1, 1)

model_inputs = scaler.fit_transform(model_inputs)

x_test = []

for x in range(pred_days, len(model_inputs)):

x_test.append(model_inputs[x-pred_days:x, 0])

x_test = np.array(x_test)

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

pred_prices = model.predict(x_test)

pred_prices = scaler.inverse_transform(pred_prices)

plt.plot(act_prices, color = 'black', label = 'Actual prices')

plt.plot(pred_prices, color = 'green', label = 'Pred prices')

plt.title(crp)

plt.xlabel('Time')

plt.ylabel('Price')

plt.legend(loc = 'upper left')

return str(pred_prices[-1])

#plt.show()

#input1 = input()

#if input1 == "y":

#model.save('dbot/moddel')

#predd2("RVN", "INR")