I need to make a stock market prediction model for predicting the closing price using LSTM and supertrend as indicator please correct the below code for predicting the closing prices and label the plot for closing prices vs date:

import yfinance as yf
import pandas as pd
import numpy as np
from sklearn.preprocessing import MinMaxScaler
from keras.models import Sequential
from keras.layers import Dense, LSTM
from keras.callbacks import LambdaCallback

nifty = yf.download('^NSEI', start='2009-01-01', end='2021-01-01')

def supertrend(df, period=10, multiplier=3):
    hl = (df['High'] + df['Low']) / 2
    atr = df['High'].rolling(period).max() - df['Low'].rolling(period).min()
    
    up = hl - multiplier * atr
    dn = hl + multiplier * atr

    df['ST'] = 0
    df['ST'][0] = (df['High'][0] + df['Low'][0]) / 2
    
    position = 'none'
    
    for i in range(1, len(df)):
        if df['Close'][i] > up[i]:
            if position != 'buy':
                position = 'buy'
                df['ST'][i] = dn[i]
            else:
                df['ST'][i] = max(df['ST'][i - 1], dn[i])
        elif df['Close'][i] < dn[i]:
            if position != 'sell':
                position = 'sell'
                df['ST'][i] = up[i]
            else:
                df['ST'][i] = min(df['ST'][i - 1], up[i])
        else:
            df['ST'][i] = df['ST'][i - 1]

    return df

nifty = supertrend(nifty)
nifty.head()

dataset = nifty['Close'].values
dataset = dataset.astype('float32')
dataset = dataset.reshape(-1, 1)
scaler = MinMaxScaler(feature_range=(0, 1))
dataset = scaler.fit_transform(dataset)

# convert an array of values into a dataset matrix
def create_dataset(dataset, look_back=1):
	dataX, dataY = [], []
	for i in range(len(dataset)-look_back-1):
		a = dataset[i:(i+look_back), 0]
		dataX.append(a)
		dataY.append(dataset[i + look_back, 0])
	return np.array(dataX), np.array(dataY)

train_size = int(len(dataset) * 0.8)
test_size = len(dataset) - train_size
train, test = dataset[:train_size], dataset[train_size:]

look_back = 1
trainX, trainY = create_dataset(train, look_back)
testX, testY = create_dataset(test, look_back)

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

model = Sequential()
model.add(LSTM(100, input_shape=(1, look_back)))
model.add(Dense(1))

model.compile(loss='mean_squared_error', optimizer='adam')
model.fit(trainX, trainY, epochs=200, batch_size=1, verbose=0)

trainPredict = model.predict(trainX)
testPredict = model.predict(testX)

trainPredict = scaler.inverse_transform(trainPredict)
trainY = scaler.inverse_transform([trainY])
testPredict = scaler.inverse_transform(testPredict)
testY = scaler.inverse_transform([testY])

import matplotlib.pyplot as plt

trainPredictPlot = np.empty_like(dataset)
trainPredictPlot[:, :] = np.nan
trainPredictPlot[look_back:len(trainPredict)+look_back, :] = trainPredict

testPredictPlot = np.empty_like(dataset)
testPredictPlot[:, :] = np.nan
testPredictPlot[len(trainPredict)+(look_back*2)+1:len(dataset)-1, :] = testPredict

plt.plot(scaler.inverse_transform(dataset), label='Actual')
plt.plot(trainPredictPlot, label='Train Prediction')
plt.plot(testPredictPlot, label='Test Prediction')
plt.legend()
plt.show()

model.save('my_model.h5')