Code Editor for app.py

import streamlit as st
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

# Creating random data for X_train
np.random.seed(0)  # For reproducibility
X_train = np.random.randn(100, 1)  # 100 random samples, 1 feature
df = pd.DataFrame({'date': pd.date_range(start='2023-01-01', periods=100),
                   'close': np.random.rand(100) * 100})

train_size = X_train.shape[0]
f, axs = plt.subplots(1, 2, figsize=(20, 10))

# Assuming linear_regression_validation_predict is defined somewhere in your code
linear_regression_validation_predict = np.random.rand(100) * 100  # Dummy data for prediction

axs[0].plot(df['date'][:train_size], df['close'][:train_size], color='black')
axs[0].plot(df['date'][train_size:], linear_regression_validation_predict[:100 - train_size], color='red')
axs[0].plot(df['date'][train_size:], df['close'][train_size:], color='green')

# this generates a blank plot because it's not selecting data
# axs[1].plot(df['date'][train_size:], linear_regression_validation_predict[:100 - train_size], color='red')
# axs[1].plot(df['date'][train_size:], df['close'][train_size:], color='green')

axs[1].plot(df['date'][:train_size], df['close'][:train_size], color='black')
axs[1].plot(df['date'][train_size:], linear_regression_validation_predict[:100 - train_size], color='red')
axs[1].plot(df['date'][train_size:], df['close'][train_size:], color='green')


st.pyplot(fig=f)