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dcc.Interval(id="interval", interval=1_000),

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@callback(Output("time", "children"), Input("interval", "n_intervals"))
def refresh_data_at_interval(interval_trigger):
   """
   This simple callback demonstrates how to use the Interval component to update data at a regular interval.
   This particular example updates time every second, however, you can subsitute this data query with any acquisition method your product requires.
   """
   return dt.datetime.now().strftime("%M:%S")

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@app.callback(
  Output ("demographics", "figure"),
  Input("scatter-x", "value"),
  Input ("comparison", "value"),
)
def make_scatter(xaxis, comp):
  dfscatter = dbx_utils.get_scatter_data(xaxis, comp)
  scatterfig = chart_utils.generate_scatter(dfscatter, xaxis, comp)
  return scatterfig

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def update_graph(input_value):
  
## ORM-based SQL Query with dynamic filters in the callback
   stmt = select([
           sensor_table.columns.MeasurementDateTime,
           sensor_table.columns.LongMovingAverage,
           sensor_table.columns.SensorLocation
           ]).where(and_(
               sensor_table.columns.SensorMeasurement == input_value
           ))

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## Get a full table with SQL Alchemy
sensor_table = Table("gold_all_sensors", MetaData(bind=engine), autoload=True)

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## Set up SQL Alchemy engine
engine = create_engine(
   f"databricks+connector://token:{token}@{host_name}:443/{database}",
   connect_args={
       "http_path": http_path,
   }, 
   )

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from sqlalchemy.engine import create_engine

## Set up SQL Alchemy engine
engine = create_engine(
   f"databricks+connector://token:{token}@{host_name}:443/{database}",
   connect_args={
       "http_path": http_path,
   }, 
   )

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def generate_scatter(df, xaxis, comp):
    axis_labels = {
        "age": "Age (years)",
        "height": "Height (inches)",
        "weight": "Weight (lbs)",
    }
    fig = px.scatter(
        df,
        x=xaxis,
        y="risk",

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def get_scatter_data(xaxis, comp):
    """
    Fetches specified columns and an aggregated column from the silver_users table, returns it as a pandas dataframe
    Returns
    -------
    df : pandas dataframe
        basic query of data from Databricks as a pandas dataframe
    """
    connection0 = sql.connect(
        server_hostname=SERVER_HOSTNAME,

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def get_listofusers():
    connection3 = sql.connect(
        server_hostname=SERVER_HOSTNAME,
        http_path=HTTP_PATH,
        access_token=ACCESS_TOKEN,
    )
    cursor3 = connection3.cursor()
    cursor3.execute(
        f"SELECT DISTINCT userid FROM {DB_NAME}.{USER_TABLE} ORDER BY userid ASC"
    )