import streamlit as st
import matplotlib.pyplot as plt

# Initialize sliders
x = st.slider('X position', min_value=0.0, max_value=10.0, value=5.0, step=0.1)
w = st.slider('W (slope)', min_value=-10.0, max_value=10.0, value=1.0, step=0.1)
b = st.slider('B (intercept)', min_value=-10.0, max_value=10.0, value=0.0, step=0.1)

# Calculate y based on the equation y = w * x + b
y = w * x + b

# Create the plot
fig, ax = plt.subplots(2, 1, figsize=(6, 4), gridspec_kw={'height_ratios': [1, 1]})
fig.subplots_adjust(hspace=0.5)

# Plot for the x-axis
ax[0].hlines(0, 0, 10, color='blue', linestyle='--')  # X-axis
ax[0].plot(x, 0, 'ro')  # Plot the x point
ax[0].set_xlim(0, 10)
ax[0].set_ylim(-1, 1)
ax[0].set_xlabel('X-axis')
ax[0].set_yticks([])  # Hide y-axis ticks
ax[0].set_title('X-axis Position')
ax[0].grid(True)

# Plot for the y-axis
ax[1].hlines(0, -10, 10, color='blue', linestyle='--')  # Y-axis
ax[1].plot(y, 0, 'ro')  # Plot the y point
ax[1].set_xlim(-10, 10)
ax[1].set_ylim(-1, 1)
ax[1].set_xlabel('Y-axis')
ax[1].set_yticks([])  # Hide y-axis ticks
ax[1].set_title(f'y = {w} * x + {b}')
ax[1].grid(True)

# Display the plot in Streamlit
st.pyplot(fig)