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

# Set page title and layout
st.set_page_config(page_title='Statistics Basics', layout='wide')

# Set up sidebar
st.sidebar.title('Parameters')
mean = st.sidebar.slider('Mean', min_value=-10.0, max_value=10.0, value=0.0, step=0.1)
std_dev = st.sidebar.slider('Standard Deviation', min_value=0.1, max_value=10.0, value=1.0, step=0.1)
variance = std_dev ** 2
variance_slider = st.sidebar.slider('Variance', min_value=0.1, max_value=10.0, value=variance, step=0.1)

# Generate data
x = np.linspace(-10, 10, 1000)
y = 1 / (np.sqrt(2 * np.pi * variance_slider)) * np.exp(-0.5 * ((x - mean) ** 2) / variance_slider)

# Calculate statistics
median = mean
mode = mean

# Generate random values within the curve boundaries based on kernel density estimate
num_points = 1000
samples = np.random.choice(x, size=num_points, p=y / np.sum(y))

# Create DataFrame for plotting
df = pd.DataFrame({'Values': samples})

# Set seaborn style
sns.set(style='darkgrid')

# Plot the bell curve and histogram of generated values
fig, ax = plt.subplots(figsize=(10, 6))
sns.histplot(df['Values'], kde=True, color='blue', ax=ax)
sns.kdeplot(df['Values'], color='red', ax=ax)
ax.plot(x, y, linewidth=2, color='red')
ax.set_title('Bell Curve with Histogram and KDE')
ax.set_xlabel('X')
ax.set_ylabel('Density')


# Display statistics
st.header('Statistics Concepts')
col1, col2, col3, col4, col5 = st.columns(5)
col1.subheader('Mean')
col1.markdown(f"<span style='font-size:24px'>{mean}</span>", unsafe_allow_html=True)
col2.subheader('Median')
col2.markdown(f"<span style='font-size:24px'>{median}</span>", unsafe_allow_html=True)
col3.subheader('Mode')
col3.markdown(f"<span style='font-size:24px'>{mode}</span>", unsafe_allow_html=True)
col4.subheader('St.D')
col4.markdown(f"<span style='font-size:24px'>{std_dev}</span>", unsafe_allow_html=True)
col5.subheader('Variance')
col5.markdown(f"<span style='font-size:24px'>{variance_slider}</span>", unsafe_allow_html=True)



# Display the bell curve, histogram, and KDE plot
st.header('Bell Curve with Histogram and KDE')
st.pyplot(fig)