import ee

ee.Initialize('farmbase-b2f7e')
# Load the farm boundary coordinates.
farm_boundary = ee.Geometry.Polygon([[
    [-122.147868, 37.783687],
    [-122.147868, 37.785533],
    [-122.145517, 37.785533],
    [-122.145517, 37.783687],
    [-122.147868, 37.783687]
]])

# Load the Sentinel-2 satellite imagery collection.
s2_collection = ee.ImageCollection('COPERNICUS/S2')

# Filter the imagery collection to the farm boundary and the current year.
s2_collection = s2_collection.filterBounds(farm_boundary).filterDate('2023-01-01', '2023-12-31')

# Calculate the median of the overlapping pixels per band.
s2_median = s2_collection.median()

# Load the crop classification model.
crop_model = ee.Model('MODIS/MOD15A2H')

# Classify the farm using the crop classification model.
crop_classification = crop_model.predict(s2_median)

# Get the crop type at the center of the farm.
crop_type = crop_classification.getRegion(farm_boundary, scale=10).getInfo()['features'][0]['properties']['cropland']

# Print the predicted crop type.
print('The predicted crop type is:', crop_type)