app.py

import streamlit as st
from pathlib import Path


# Load custom CSS
def load_css():
    css_file = Path(__file__).parent / ".streamlit" / "style.css"
    if css_file.exists():
        with open(css_file) as f:
            st.markdown(f"<style>{f.read()}</style>", unsafe_allow_html=True)


load_css()


home_page = [
    st.Page("pages/home.py", title="Welcome", icon=":material/home:", default=True),
]

probability_pages = [
    st.Page(
        "pages/probability/00_gaussian_1d.py",
        title="Gaussian 1D",
        icon=":material/science:",
    ),
    st.Page(
        "pages/probability/01_gaussian_2d.py",
        title="Gaussian 2D",
        icon=":material/scatter_plot:",
    ),
    st.Page(
        "pages/probability/02_beta_distribution.py",
        title="Beta Distribution",
        icon=":material/pie_chart:",
    ),
    st.Page(
        "pages/probability/03_binomial_distribution.py",
        title="Binomial Distribution",
        icon=":material/casino:",
    ),
    st.Page(
        "pages/probability/04_gaussian_expectation.py",
        title="Gaussian Expectation",
        icon=":material/integration_instructions:",
    ),
    st.Page(
        "pages/probability/05_bayes_theorem.py",
        title="Bayes' Theorem",
        icon=":material/calculate:",
    ),
    st.Page(
        "pages/probability/06_beta_binomial.py",
        title="Bayesian Coin Toss",
        icon=":material/monetization_on:",
    ),
    st.Page(
        "pages/probability/07_grid_approximation.py",
        title="Grid Approximation",
        icon=":material/grid_on:",
    ),
    st.Page(
        "pages/probability/08_beta_binomial_predictive.py",
        title="Beta-Binomial Predictive",
        icon=":material/query_stats:",
    ),
]

regression_pages = [
    st.Page(
        "pages/regression/00_curve_fitting.py",
        title="Curve Fitting",
        icon=":material/show_chart:",
    ),
    st.Page(
        "pages/regression/01_likelihood.py",
        title="What is Likelihood?",
        icon=":material/analytics:",
    ),
    st.Page(
        "pages/regression/02_mle_unbiased.py",
        title="MLE is Unbiased",
        icon=":material/balance:",
    ),
    st.Page(
        "pages/regression/03_bayesian_linear_regression.py",
        title="Bayesian Linear Regression",
        icon=":material/trending_up:",
    ),
    st.Page(
        "pages/regression/04_predictive_distribution.py",
        title="Predictive Distribution",
        icon=":material/query_stats:",
    ),
    st.Page(
        "pages/regression/05_regression_basis_functions.py",
        title="Regression with Basis Functions",
        icon=":material/stacked_line_chart:",
    ),
    st.Page(
        "pages/regression/06_model_selection.py",
        title="Model Selection",
        icon=":material/tune:",
    ),
    st.Page(
        "pages/regression/07_loss_likelihood.py",
        title="Probabilistic Interpretation of Loss",
        icon=":material/sync_alt:",
    ),
    st.Page(
        "pages/regression/08_occams_razor.py",
        title="Bayesian Occam's Razor",
        icon=":material/balance:",
    ),
    st.Page(
        "pages/regression/09_prior_data_effect.py",
        title="Effect of Prior and Data",
        icon=":material/tune:",
    ),
    st.Page(
        "pages/regression/10_heteroskedastic_noise.py",
        title="Heteroskedastic Noise",
        icon=":material/equalizer:",
    ),
]

monte_carlo_pages = [
    st.Page(
        "pages/monte_carlo/00_estimate_pi.py",
        title="Estimating π",
        icon=":material/casino:",
    ),
    st.Page(
        "pages/monte_carlo/01_rejection_sampling.py",
        title="Rejection Sampling",
        icon=":material/filter_alt:",
    ),
    st.Page(
        "pages/monte_carlo/02_random_walk.py",
        title="2D Random Walk",
        icon=":material/explore:",
    ),
    st.Page(
        "pages/monte_carlo/03_mcmc_metropolis.py",
        title="MCMC Metropolis-Hastings",
        icon=":material/hub:",
    ),
    st.Page(
        "pages/monte_carlo/04_mcmc_diagnostics.py",
        title="MCMC Diagnostics",
        icon=":material/monitoring:",
    ),
    st.Page(
        "pages/monte_carlo/05_hamiltonian_dynamics.py",
        title="Hamiltonian Dynamics",
        icon=":material/motion_photos_on:",
    ),
    st.Page(
        "pages/monte_carlo/06_hmc.py",
        title="HMC Sampling",
        icon=":material/rocket_launch:",
    ),
]

gaussian_process_pages = [
    st.Page(
        "pages/gaussian_process/00_multivariate_gaussian.py",
        title="Multivariate Gaussian",
        icon=":material/blur_on:",
    ),
    st.Page(
        "pages/gaussian_process/01_kernels.py",
        title="SE Kernel",
        icon=":material/waves:",
    ),
    st.Page(
        "pages/gaussian_process/02_gp_regression.py",
        title="GP Regression",
        icon=":material/ssid_chart:",
    ),
    st.Page(
        "pages/gaussian_process/03_marginal_likelihood.py",
        title="Marginal Likelihood",
        icon=":material/balance:",
    ),
    st.Page(
        "pages/gaussian_process/04_nngp_convergence.py",
        title="Neural Network → GP",
        icon=":material/device_hub:",
    ),
    st.Page(
        "pages/gaussian_process/05_conditioning_marginalization.py",
        title="Conditioning & Marginalization",
        icon=":material/call_split:",
    ),
    st.Page(
        "pages/gaussian_process/06_gp_joint_distribution.py",
        title="GP Joint Distribution",
        icon=":material/scatter_plot:",
    ),
    st.Page(
        "pages/gaussian_process/07_ml_non_identifiability.py",
        title="Non-Identifiability of ML",
        icon=":material/warning:",
    ),
    st.Page(
        "pages/gaussian_process/08_inducing_points.py",
        title="Inducing Points",
        icon=":material/track_changes:",
    ),
    st.Page(
        "pages/gaussian_process/08_sparse_gp.py",
        title="Sparse GP",
        icon=":material/compare_arrows:",
    ),
    st.Page(
        "pages/gaussian_process/09_gp_classification_laplace.py",
        title="GP Classification (Laplace)",
        icon=":material/bubble_chart:",
    ),
]

linear_algebra_pages = [
    st.Page(
        "pages/linear_algebra/00_determinant.py",
        title="Determinant",
        icon=":material/grid_on:",
    ),
    st.Page(
        "pages/linear_algebra/01_eigenvalues.py",
        title="Eigenvalues & Eigenvectors",
        icon=":material/open_with:",
    ),
    st.Page(
        "pages/linear_algebra/02_cholesky.py",
        title="Cholesky Decomposition",
        icon=":material/layers:",
    ),
    st.Page(
        "pages/linear_algebra/03_cholesky_solve.py",
        title="Solving with Cholesky",
        icon=":material/function:",
    ),
]

optimization_pages = [
    st.Page(
        "pages/optimization/00_gradient_descent.py",
        title="Gradient Descent",
        icon=":material/south_east:",
    ),
    st.Page(
        "pages/optimization/01_momentum.py",
        title="Momentum",
        icon=":material/speed:",
    ),
    st.Page(
        "pages/optimization/02_hessian.py",
        title="Hessian Matrix",
        icon=":material/grid_4x4:",
    ),
    st.Page(
        "pages/optimization/03_adam.py",
        title="Adam Optimizer",
        icon=":material/auto_awesome:",
    ),
    st.Page(
        "pages/optimization/04_sgd.py",
        title="Stochastic Gradient Descent",
        icon=":material/shuffle:",
    ),
]

laplace_approximation_pages = [
    st.Page(
        "pages/laplace_approximation/00_gamma_posterior.py",
        title="Gamma Posterior",
        icon=":material/blur_linear:",
    ),
]

variational_inference_pages = [
    st.Page(
        "pages/variational_inference/00_kl_divergence_asymmetry.py",
        title="KL Divergence Asymmetry",
        icon=":material/compare_arrows:",
    ),
    st.Page(
        "pages/variational_inference/01_jensens_inequality.py",
        title="Jensen's Inequality",
        icon=":material/functions:",
    ),
    st.Page(
        "pages/variational_inference/02_reparameterization_vs_reinforce.py",
        title="Reparameterization vs REINFORCE",
        icon=":material/insights:",
    ),
    st.Page(
        "pages/variational_inference/03_vi_optimization.py",
        title="VI Optimization",
        icon=":material/moving:",
    ),
    st.Page(
        "pages/variational_inference/04_normalizing_flow.py",
        title="Normalizing Flows",
        icon=":material/transform:",
    ),
]

classification_pages = [
    st.Page(
        "pages/classification/00_logistic_regression_2d.py",
        title="Logistic Regression 2D",
        icon=":material/category:",
    ),
    st.Page(
        "pages/classification/01_map_logistic_regression.py",
        title="MAP Estimation",
        icon=":material/my_location:",
    ),
    st.Page(
        "pages/classification/02_laplace_approximation.py",
        title="Laplace Approximation",
        icon=":material/blur_on:",
    ),
    st.Page(
        "pages/classification/03_variational_inference.py",
        title="Variational Inference",
        icon=":material/settings_suggest:",
    ),
    st.Page(
        "pages/classification/04_mcmc_inference.py",
        title="MCMC Inference",
        icon=":material/hub:",
    ),
    st.Page(
        "pages/classification/05_naive_bayes.py",
        title="Naive Bayes",
        icon=":material/domain:",
    ),
]

bayesian_inference_pages = [
    st.Page(
        "pages/bayesian_inference/00_uncertainty_decomposition.py",
        title="Uncertainty Decomposition",
        icon=":material/device_thermostat:",
    ),
    st.Page(
        "pages/bayesian_inference/01_online_learning_bnn.py",
        title="Online Learning (BNN)",
        icon=":material/update:",
    ),
    st.Page(
        "pages/bayesian_inference/02_doobs_consistency.py",
        title="Doob's Consistency",
        icon=":material/autorenew:",
    ),
]

generative_models_pages = [
    st.Page(
        "pages/generative_models/00_pca_2d.py",
        title="PCA in 2D",
        icon=":material/compress:",
    ),
    st.Page(
        "pages/generative_models/01_ppca.py",
        title="Probabilistic PCA",
        icon=":material/blur_circular:",
    ),
    st.Page(
        "pages/generative_models/02_gmm_em.py",
        title="GMM with EM",
        icon=":material/scatter_plot:",
    ),
    st.Page(
        "pages/generative_models/03_vae_pure_vi.py",
        title="VAE: Pure VI",
        icon=":material/auto_awesome:",
    ),
    st.Page(
        "pages/generative_models/04_vae_amortized.py",
        title="VAE: Amortized",
        icon=":material/model_training:",
    ),
    st.Page(
        "pages/generative_models/05_cvae.py",
        title="VAE: Conditional",
        icon=":material/tune:",
    ),
    st.Page(
        "pages/generative_models/06_elbo_visualization.py",
        title="ELBO Visualization",
        icon=":material/sync_alt:",
    ),
]

deep_learning_pages = [
    st.Page(
        "pages/deep_learning/00_mnist_classification.py",
        title="MNIST Classification (BNN)",
        icon=":material/neurology:",
    ),
]

neural_network_pages = [
    st.Page(
        "pages/neural_networks/00_weight_to_function_space.py",
        title="Weight → Function Space",
        icon=":material/neurology:",
    ),
    st.Page(
        "pages/neural_networks/01_active_learning_bnn.py",
        title="Active Learning (BALD)",
        icon=":material/science:",
    ),
]


pg = st.navigation(
    {
        "Home": home_page,
        "Probability and Statistics": probability_pages,
        "Linear Algebra": linear_algebra_pages,
        "Regression": regression_pages,
        "Classification": classification_pages,
        "Neural Networks": neural_network_pages,
        "Bayesian Inference": bayesian_inference_pages,
        "Generative Models": generative_models_pages,
        # "Deep Learning": deep_learning_pages,
        "Monte Carlo": monte_carlo_pages,
        "Gaussian Processes": gaussian_process_pages,
        "Optimization": optimization_pages,
        "Laplace Approximation": laplace_approximation_pages,
        "Variational Inference": variational_inference_pages,
    },
    position="sidebar",
)
st.set_page_config(
    layout="wide",
    initial_sidebar_state="collapsed",
    page_icon=":material/menu_book:",
    page_title="ASI Demo",
)
pg.run()