import ee

# Initialize the Earth Engine API
ee.Initialize(project='farmbase-b2f7e')

def classify_crop_in_polygon_sentinel(polygon_coords, start_date, end_date):
    """
    Classify crops within a single polygon using Sentinel-2 data.

    Args:
        polygon_coords (list): A list of coordinates representing the polygon
                               in the format [[lon1, lat1], [lon2, lat2], ...].
        start_date (str): Start date for image collection (e.g., '2021-01-01').
        end_date (str): End date for image collection (e.g., '2021-12-31').

    Returns:
        ee.Image: A classified image representing the predicted crops within the polygon.
    """
    # Create an ee.Geometry.Polygon from the input polygon coordinates
    polygon = ee.Geometry.Polygon(polygon_coords)

    # Define a function to mask clouds using the Sentinel-2 QA60 band
    def mask_clouds(image):
        cloudBitMask = 1 << 10
        cirrusBitMask = 1 << 11
        qa60 = image.select(['QA60'])
        mask = qa60.bitwiseAnd(cloudBitMask).eq(0).And(qa60.bitwiseAnd(cirrusBitMask).eq(0))
        return image.updateMask(mask).divide(10000)

    # Load Sentinel-2 data and apply the cloud mask
    sentinel2 = ee.ImageCollection('COPERNICUS/S2') \
        .filterBounds(polygon) \
        .filterDate(start_date, end_date) \
        .map(mask_clouds) \
        .median()

    # Select the relevant bands for classification (e.g., 'B4', 'B3', 'B2')
    bands = ['B4', 'B3', 'B2']

    # Apply a classifier of your choice (e.g., Random Forest)
    classifier = ee.Classifier.randomForest().train(training, 'class', bands)
    classified = sentinel2.select(bands).classify(classifier)

    # Clip the dataset to the polygon
    clipped_data = classified.clip(polygon)
    print(clipped_data)
    return clipped_data

# Example usage:
polygon_coords = [
    [75.3589059, 24.4474494],
					[75.3588523, 24.4469977],
					[75.3594504, 24.4469415],
					[75.3594465, 24.4468412],
					[75.3598378, 24.4468241],
					[75.3598374, 24.4467459],
					[75.3600919, 24.446758],
					[75.3602018, 24.4473412],
					[75.3589059, 24.4474494]
]  # Replace with your polygon coordinates
start_date = '2021-01-01'
end_date = '2021-12-31'

predicted_crop = classify_crop_in_polygon_sentinel(polygon_coords, start_date, end_date)

# import ee

# # Initialize Earth Engine
# ee.Initialize(project='farmbase-b2f7e')

# # Define the farm boundary as a GeoJSON geometry
# farm_boundary = ee.Geometry.Polygon(
#     [
# 				[
# 					[75.3589059, 24.4474494],
# 					[75.3588523, 24.4469977],
# 					[75.3594504, 24.4469415],
# 					[75.3594465, 24.4468412],
# 					[75.3598378, 24.4468241],
# 					[75.3598374, 24.4467459],
# 					[75.3600919, 24.446758],
# 					[75.3602018, 24.4473412],
# 					[75.3589059, 24.4474494]
# 				]
# 			]
# )

# # Define the time range for satellite imagery
# start_date = '2022-01-01'
# end_date = '2022-12-31'

# # Load satellite imagery (e.g., Sentinel-2)
# image_collection = (
#     ee.ImageCollection('COPERNICUS/S2')
#     .filterBounds(farm_boundary)
#     .filterDate(ee.Date(start_date), ee.Date(end_date))
#     .sort('CLOUDY_PIXEL_PERCENTAGE')
#     .first()
# )

# # Define a region of interest for classification
# roi = farm_boundary

# # Load the USDA NASS Cropland Data Layer (CDL)
# cdl = ee.Image('USDA/NASS/CDL/2021')  # You can choose the year you need

# # Select relevant bands from the CDL
# cdl_bands = ['cropland']

# # Sample the CDL at the farm boundary
# cdl_sample = cdl.select(cdl_bands).sample(
#     region=roi,
#     scale=30,
#     geometries=True,
# )

# # Define the CDL class property as 'crop_type'
# cdl_sample = cdl_sample.rename(['crop_type'])

# # Merge the CDL sample with your own training data (if available)
# # Replace 'training_data' with your own FeatureCollection
# training_data = training_data.merge(cdl_sample)

# # Select relevant bands from the imagery
# bands = ['B2', 'B3', 'B4', 'B5', 'B6', 'B7', 'B8', 'B8A', 'B11', 'B12']

# # Sample the imagery at training data points
# training = image_collection.select(bands).sampleRegions(
#     collection=training_data,
#     properties=['crop_type'],
#     scale=10,
# )

# # Define a classifier (e.g., Random Forest)
# classifier = ee.Classifier.randomForest(10).train(
#     features=training,
#     classProperty='crop_type',
#     inputProperties=bands,
# )

# # Classify the image
# classified = image_collection.select(bands).classify(classifier)

# # Get the crop type prediction for the farm boundary
# prediction = classified.reduceRegion(
#     reducer=ee.Reducer.mode(),
#     geometry=roi,
#     scale=10,
# )

# # Print the predicted crop type
# print('Predicted Crop Type:', prediction.get('classification'))