import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.pylab import rcParams
from keras.models import Sequential
from keras.layers import LSTM, Dropout, Dense
from sklearn.preprocessing import MinMaxScaler

df = pd.read_csv('static/HistoricalData_APL.csv')
df = df[['Date', 'Close/Last']]
df = df.replace({'\$': ''}, regex=True)
print(df.head())
df = df.astype({"Close/Last": float})
df["Date"] = pd.to_datetime(df.Date, format="%m/%d/%Y")
print(df.dtypes)
df.index = df['Date']
plt.plot(df["Close/Last"], label='AAPL Close Price history')
plt.show()
df = df.sort_index(ascending=True,axis=0)
data = pd.DataFrame(index=range(0,len(df)),columns=['Date','Close/Last'])
for i in range(0,len(data)):
    data["Date"][i]=df['Date'][i]
    data["Close/Last"][i]=df["Close/Last"][i]
scaler=MinMaxScaler(feature_range=(0,1))
data.index=data.Date
data.drop("Date",axis=1,inplace=True)
final_data = data.values
train_data=final_data[0:200,:]
valid_data=final_data[200:,:]
scaler=MinMaxScaler(feature_range=(0,1))
scaled_data=scaler.fit_transform(final_data)
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])
lstm_model=Sequential()
lstm_model.add(LSTM(units=50,return_sequences=True,input_shape=(np.shape(x_train_data)[1],1)))
lstm_model.add(LSTM(units=50))
lstm_model.add(Dense(1))
model_data=data[len(data)-len(valid_data)-60:].values
model_data=model_data.reshape(-1,1)
model_data=scaler.transform(model_data)
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)
X_test=[]
for i in range(60,model_data.shape[0]):
    X_test.append(model_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))
predicted_stock_price=lstm_model.predict(X_test)
predicted_stock_price=scaler.inverse_transform(predicted_stock_price)
train_data=data[:200]
valid_data=data[200:]
valid_data['Predictions']=predicted_stock_price
plt.plot(train_data["Close/Last"])
plt.plot(valid_data[['Close/Last',"Predictions"]])
plt.show()