import streamlit as st
import numpy as np

# App title     
st.title('NumPy Concepts: From Scratch to Advanced')

# Sidebar for navigation
st.sidebar.title("Navigation")
options = st.sidebar.radio("Go to", ["Home", "Array Basics", "Advanced Operations", "Linear Algebra"])

# Home page
if options == "Home":
    st.header("Welcome to NumPy Learning Hub!")
    st.write("""
    This app will guide you through various concepts of NumPy, starting from the basics to advanced levels.
    Use the sidebar to navigate through different sections.
    """)

# Array Basics page
elif options == "Array Basics":
    st.header("Array Basics")
    st.write("Learn about creating arrays and basic operations.")
    
    # Creating arrays
    st.subheader("Creating Arrays")
    array1 = np.array([1, 2, 3, 4, 5])
    st.write("1D Array:", array1)

    array2 = np.array([[1, 2], [3, 4]])
    st.write("2D Array:", array2)

    # Basic operations
    st.subheader("Basic Operations")
    array_sum = np.sum(array1)
    st.write("Sum of array:", array_sum)

    array_mean = np.mean(array1)
    st.write("Mean of array:", array_mean)

# Advanced Operations page
elif options == "Advanced Operations":
    st.header("Advanced Operations")
    st.write("Explore advanced NumPy operations like broadcasting and slicing.")
    
    array3 = np.arange(10)
    st.write("Original array:", array3)

    st.subheader("Slicing")
    st.write("Sliced array (2:5):", array3[2:5])

    st.subheader("Broadcasting")
    array4 = np.array([1, 2, 3])
    array_broadcasted = array4 + 5
    st.write("Broadcasted array:", array_broadcasted)

# Linear Algebra page
elif options == "Linear Algebra":
    st.header("Linear Algebra")
    st.write("Dive into linear algebra concepts with NumPy.")
    
    # Matrix multiplication
    st.subheader("Matrix Multiplication")
    matrix1 = np.array([[1, 2], [3, 4]])
    matrix2 = np.array([[5, 6], [7, 8]])
    matrix_product = np.dot(matrix1, matrix2)
    st.write("Matrix 1:", matrix1)
    st.write("Matrix 2:", matrix2)
    st.write("Matrix product:", matrix_product)

    # Eigenvalues and eigenvectors
    st.subheader("Eigenvalues and Eigenvectors")
    matrix3 = np.array([[4, -2], [1, 1]])
    eigenvalues, eigenvectors = np.linalg.eig(matrix3)
    st.write("Matrix:", matrix3)
    st.write("Eigenvalues:", eigenvalues)
    st.write("Eigenvectors:", eigenvectors)