import ee
import geemap

import solara


class Map(geemap.Map):
    def __init__(self, **kwargs):
        super().__init__(**kwargs)
        self.add_ee_data()
        self.add("layer_manager")
        self.add("inspector")

    def add_ee_data(self):
        # Add Earth Engine dataset
        dem = ee.Image('USGS/SRTMGL1_003')
        landsat7 = ee.Image('LANDSAT/LE7_TOA_5YEAR/1999_2003').select(
            ['B1', 'B2', 'B3', 'B4', 'B5', 'B7']
        )
        states = ee.FeatureCollection("TIGER/2018/States")

        # Set visualization parameters.
        vis_params = {
            'min': 0,
            'max': 4000,
            'palette': ['006633', 'E5FFCC', '662A00', 'D8D8D8', 'F5F5F5'],
        }

        # Add Earth Engine layers to Map
        self.addLayer(
            landsat7,
            {'bands': ['B4', 'B3', 'B2'], 'min': 20, 'max': 200, 'gamma': 2.0},
            'Landsat 7',
            True,
        )
        self.addLayer(dem, vis_params, 'SRTM DEM', True, 1)
        self.addLayer(states, {}, "US States")


@solara.component
def Page():
    with solara.Column(align="center"):
            markdown = """
                
                ### Landsat Inspection

                **Here, we are using [NASA SRTM Digital Elevation 30m](https://developers.google.com/earth-engine/datasets/catalog/USGS_SRTMGL1_003) for digital elevation and [LANDSAT_LE7_TOA_5YEAR](https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LE7_TOA_5YEAR) that contains 5 year composites from all Landsat 7 images in the specified composite period with [TIGER: US Census States 2018](https://developers.google.com/earth-engine/datasets/catalog/TIGER_2018_States) for state geometry clipping**
                    """
            solara.Markdown(markdown)

    with solara.Column(style={"min-width": "500px"}):
        Map.element(
            center=[40, -100],
            zoom=4,
            height="600px",
        )