How to add values future prediction on LSTM Timeseries Forecast?

Question

I have code here for LSTM Time Series for a CSV file, just want to add future data from that CSV and plot them.

note:

1. Waktu = Date
2. column one = Date (Waktu), column two = Temperature
3. it has 502 data

import math
import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from keras.models import Sequential
from keras.layers import Dense, LSTM
import matplotlib.pyplot as plt
plt.style.use('fivethirtyeight')

lokasi='C:/Users/HP Elitebook X360/Downloads/Nino 34 (ncep).csv'
df = pd.read_csv(lokasi)
df.index = pd.to_datetime(df['Waktu'], format='%m/%d/%Y')

'''
plt.figure (figsize = (16, 8))
plt.title('Close Price History')
plt.plot(df['Temperature'])
plt.xlabel('Waktu', fontsize=18)
plt.ylabel('Temperature', fontsize=18)
#plt.show()
'''

#Create a new dataframe with only the 'Close column
data= df.filter(['Temperature'])
#Convert the dataframe to a numpy array
dataset = data.values
#Get the number of rows to train the model on
training_data_len = math.ceil( len(dataset)*.8)
future_data_len = math.ceil(len(dataset)*.8)
#Scale the data
scaler = MinMaxScaler(feature_range=(0,1))
scaled_data = scaler.fit_transform(dataset)

#Create the training data set
#Create the scaled training data set
train_data= scaled_data[0: training_data_len, :]
#Split the data into x_train and y_train data sets
x_train = []
y_train = []

interval=30
for i in range(interval, len(train_data)):
    x_train.append(train_data[i-interval:i, 0])
    y_train.append(train_data[i,0])
if i<=interval:
    print(x_train)
    print(y_train)
    print()

#convert the x_train and y_train to numpy array
x_train, y_train = np.array(x_train), np.array(y_train)
#x_train.shape

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

#Build LSTM
model = Sequential()
model.add(LSTM(50, return_sequences=True, input_shape=(x_train.shape[1],1)))
model.add(LSTM(50, return_sequences=False))
model.add(Dense(25))
model.add(Dense(1))

#compile model
model.compile(optimizer='adam', loss='mean_squared_error')
#train model
model.fit(x_train, y_train, batch_size=1, epochs=1)
#create test data set
#create new array containing scaled values from index 341 to 400
test_data = scaled_data[training_data_len - interval:,:]
#create the data sets x_test and y_test
x_test = []
y_test =dataset[training_data_len:,:]
for i in range(interval, len(test_data)):
    x_test.append(test_data[i-interval:i, 0])

#convert the data to a numpy array
x_test = np.array(x_test)
#reshape data
x_test = np.reshape(x_test, (x_test.shape[0], x_test.shape[1], 1))
#get model predicted price values
predictions=model.predict(x_test)
predictions=scaler.inverse_transform(predictions)
#get the RMSE
rmse = np.sqrt(np.mean(((predictions- y_test)**2)))
#plot the data
train = data[:training_data_len]
valid = data[training_data_len:]
valid['Predictions']=predictions

#visualize data
plt.figure(figsize=(16,8))
plt.title('Model LSTM Nino 3.4')
plt.xlabel('Waktu', fontsize=18)
plt.ylabel('Temperature', fontsize=18)
plt.plot(train['Temperature'], linewidth=2)
plt.plot(valid[['Temperature','Predictions']], linewidth=2)
plt.legend(['Train', 'Val', 'Predictions'], loc='lower right')
plt.show()

and there is the result Result Code

How to get future forecast for LSTM Prediction like picture below?

Hope Like This