import numpy as np
import pandas as pd
import tensorflow as tf
from tensorflow.keras import layers, models
import matplotlib.pyplot as plt
from sklearn.preprocessing import MinMaxScaler

# Load the dataset
data_url = 'https://raw.githubusercontent.com/selva86/datasets/master/aapl.csv'
df = pd.read_csv(data_url)
df = df[['Date', 'Close']]
df['Date'] = pd.to_datetime(df['Date'])
df.set_index('Date', inplace=True)

# Normalize the data
scaler = MinMaxScaler(feature_range=(0, 1))
scaled_data = scaler.fit_transform(df)

# Create sequences
def create_sequences(data, seq_length):
    xs = []
    ys = []
    for i in range(len(data) - seq_length):
        x = data[i:i + seq_length]
        y = data[i + seq_length]
        xs.append(x)
        ys.append(y)
    return np.array(xs), np.array(ys)

seq_length = 60
X, y = create_sequences(scaled_data, seq_length)

# Split the data into training and testing sets
split = int(0.8 * len(X))
X_train, X_test = X[:split], X[split:]
y_train, y_test = y[:split], y[split:]

# Reshape data for the model
X_train = np.reshape(X_train, (X_train.shape[0], X_train.shape[1], 1))
X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], 1))

# Build the RNN model
model = models.Sequential()
model.add(layers.LSTM(50, return_sequences=True, input_shape=(seq_length, 1)))
model.add(layers.LSTM(50, return_sequences=False))
model.add(layers.Dense(25))
model.add(layers.Dense(1))

model.summary()

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

# Train the model
history = model.fit(X_train, y_train, batch_size=32, epochs=20, validation_split=0.1)

# Make predictions
train_predict = model.predict(X_train)
test_predict = model.predict(X_test)

# Inverse transform the predictions
train_predict = scaler.inverse_transform(train_predict)
y_train = scaler.inverse_transform(y_train.reshape(-1, 1))
test_predict = scaler.inverse_transform(test_predict)
y_test = scaler.inverse_transform(y_test.reshape(-1, 1))

# Plot the results
plt.figure(figsize=(14, 5))
plt.plot(df.index, df['Close'], label='True Price')
plt.plot(df.index[seq_length:seq_length + len(train_predict)], train_predict, label='Train Predict')
plt.plot(df.index[seq_length + len(train_predict):], test_predict, label='Test Predict')
plt.xlabel('Date')
plt.ylabel('Close Price')
plt.legend()
plt.show()