Code Editor for app.py

import streamlit as st
from st_aggrid import AgGrid, GridOptionsBuilder, JsCode
import pandas as pd
import requests

st.set_page_config(layout='wide')

# Load sample data
url = "https://corsproxy.io/?https://www.ag-grid.com/example-assets/olympic-winners.json"
# NOTE: Using corsproxy.io because we're in a WASM environment. If running locally,
# you can remove the corsproxy.io prefix.

@st.cache_data
def get_data():
    return pd.read_json(url)

df = get_data()

# Custom cell styling using JsCode
cellStyle = JsCode(
    """
    function(params) {
        if (params.value > 3) {
            return {
                'backgroundColor': 'gold',
                'color': 'black'
            }
        }
        return null;
    }
    """
)

# Configure grid options
grid_builder = GridOptionsBuilder.from_dataframe(df)
grid_builder.configure_default_column(
    groupable=True, 
    value=True, 
    enableRowGroup=True,
    editable=True,
    filterable=True
)

# Configure specific columns
grid_builder.configure_column("gold", cellStyle=cellStyle)
grid_builder.configure_column("silver", cellStyle=cellStyle)
grid_builder.configure_column("bronze", cellStyle=cellStyle)

# Enable selection
grid_builder.configure_selection(selection_mode="multiple", use_checkbox=True)

# Build final grid options
grid_options = grid_builder.build()

st.title("Olympic Winners Data Grid")
st.markdown("""
This interactive grid shows Olympic winners data with the following features:
- Selectable rows with checkboxes
- Sortable columns
- Filtering capabilities
- Gold highlighting for medal counts > 3
- Grouping functionality
""")

# Create tabs for different views
grid_tab, data_tab = st.tabs(["Grid View", "Raw Data"])

with grid_tab:
    grid_response = AgGrid(
        df,
        grid_options,
        height=500,
        allow_unsafe_jscode=True,
        enable_enterprise_modules=True,
        update_mode="MODEL_CHANGED"
    )

    if grid_response["selected_rows"]:
        st.write("Selected rows:")
        st.json(grid_response["selected_rows"])

with data_tab:
    st.dataframe(df)