apps/precalculated/app.py

"""
Precalculated Embeddings Explorer - Standalone Application

A Streamlit application for exploring precomputed embeddings stored in parquet files.
Features dynamic filter generation based on available columns.
"""

import streamlit as st


def main():
    """CLI entry point — launches the Streamlit server."""
    import sys
    import os
    from streamlit.web import cli as stcli

    sys.argv = ["streamlit", "run", os.path.abspath(__file__), "--server.headless", "true"]
    stcli.main()


def app():
    """Streamlit application layout."""
    from apps.precalculated.components.sidebar import (
        render_file_section,
        render_dynamic_filters,
        render_projection_section,
        render_kmeans_section,
    )
    from apps.precalculated.components.data_preview import (
        render_data_preview,
        render_cluster_representatives,
    )
    from shared.components.visualization import render_scatter_plot
    from shared.components.summary import render_clustering_summary
    from shared.components.demo_chrome import (
        is_demo_mode,
        render_demo_header,
        render_demo_footer,
    )

    st.set_page_config(
        layout="wide",
        page_title="Precalculated Embeddings Explorer",
        page_icon="📊"
    )

    # Initialize session state
    if "page_type" not in st.session_state or st.session_state.page_type != "precalculated_app":
        # Clear any stale state from other apps
        keys_to_clear = ["embeddings", "valid_paths", "last_image_dir", "embedding_complete", "kmeans_column"]
        for key in keys_to_clear:
            if key in st.session_state:
                del st.session_state[key]
        st.session_state.page_type = "precalculated_app"

    # Header — demo chrome when hosted, otherwise the standard title.
    if is_demo_mode():
        render_demo_header()
    else:
        st.title("📊 Precalculated Embeddings Explorer")
        st.markdown(
            "Load parquet files with embeddings, apply dynamic filters, and cluster for visualization. "
            "Filters are automatically generated based on your data columns."
        )

    # Row 1: File loading
    render_file_section()

    # Row 2: Dynamic filters
    render_dynamic_filters()

    # Row 3: Main content
    col_settings, col_plot, col_preview = st.columns([2, 7, 3])

    with col_settings:
        render_projection_section()
        render_kmeans_section()

    with col_plot:
        render_scatter_plot()

    with col_preview:
        render_data_preview()

    # Bottom: Taxonomy summary + representative images
    st.markdown("---")
    render_clustering_summary(show_taxonomy=True)
    render_cluster_representatives()

    # Demo-only attribution / funding footer.
    if is_demo_mode():
        render_demo_footer()


if __name__ == "__main__":
    app()