AI and Machine Learning

class Genie:
    """AI assistant widget for Earth Engine and geospatial analysis."""
    
    def __init__(
        self,
        project: Optional[str] = None,
        google_api_key: Optional[str] = None,
        gemini_model: str = "gemini-1.5-flash",
        target_score: float = 0.8,
        widget_height: str = "600px",
        initialize_ee: bool = True,
        **kwargs
    ) -> None:
        """
        Initialize Genie AI assistant.
        
        Args:
            project: Google Cloud project ID
            google_api_key: Google API key for authentication
            gemini_model: Gemini model to use for AI assistance
            target_score: Target confidence score for responses  
            widget_height: Height of the widget UI
            initialize_ee: Whether to initialize Earth Engine
            **kwargs: Additional configuration parameters
        """

def fc_to_classifier(
    features: ee.FeatureCollection,
    classifier: str = "RandomForest",
    mode: str = "classification",
    **kwargs
) -> ee.Classifier:
    """
    Convert feature collection to Earth Engine classifier.
    
    Args:
        features: Training feature collection
        classifier: Classifier type ('RandomForest', 'SVM', 'NaiveBayes', 'DecisionTree')
        mode: Classification mode ('classification' or 'regression')
        **kwargs: Additional classifier parameters
        
    Returns:
        Trained Earth Engine classifier
    """

def strings_to_classifier(
    strings: List[str],
    classifier_type: str = "RandomForest",
    **kwargs
) -> ee.Classifier:
    """
    Convert decision tree strings to Earth Engine classifier.
    
    Args:
        strings: List of decision tree string representations
        classifier_type: Type of classifier to create
        **kwargs: Additional parameters
        
    Returns:
        Earth Engine classifier object
    """

def export_trees_to_fc(
    classifier: ee.Classifier,
    features: ee.FeatureCollection,
    **kwargs
) -> ee.FeatureCollection:
    """
    Export decision trees to feature collection.
    
    Args:
        classifier: Trained classifier
        features: Feature collection to export trees for
        **kwargs: Additional export parameters
        
    Returns:
        Feature collection containing tree representations
    """

def tree_to_string(tree, **kwargs) -> str:
    """
    Convert decision tree to string representation.
    
    Args:
        tree: Decision tree object
        **kwargs: Conversion parameters
        
    Returns:
        String representation of decision tree
    """

def rf_to_strings(
    random_forest,
    **kwargs
) -> List[str]:
    """
    Convert random forest to list of tree strings.
    
    Args:
        random_forest: Random forest model
        **kwargs: Conversion parameters
        
    Returns:
        List of decision tree strings
    """

def trees_to_csv(
    trees: List[str],
    output_path: str,
    **kwargs
) -> None:
    """
    Export decision trees to CSV file.
    
    Args:
        trees: List of tree string representations
        output_path: Output CSV file path
        **kwargs: Export parameters
    """

def csv_to_classifier(
    csv_path: str,
    classifier_type: str = "RandomForest",
    **kwargs
) -> ee.Classifier:
    """
    Import classifier from CSV file.
    
    Args:
        csv_path: Path to CSV file containing tree data
        classifier_type: Type of classifier to create
        **kwargs: Additional parameters
        
    Returns:
        Earth Engine classifier
    """

class Catalog:
    """Earth Engine data catalog with AI-powered search."""
    
    def __init__(self, **kwargs) -> None:
        """Initialize catalog interface."""
    
    def search(
        self,
        keywords: str = None,
        spatial_filter: ee.Geometry = None,
        temporal_filter: List[str] = None,
        **kwargs
    ) -> List[Dict]:
        """
        Search Earth Engine catalog with AI assistance.
        
        Args:
            keywords: Search keywords or natural language query
            spatial_filter: Geographic area of interest
            temporal_filter: Date range [start_date, end_date]
            **kwargs: Additional search parameters
            
        Returns:
            List of matching dataset information
        """

class Collection:
    """Earth Engine collection wrapper with AI capabilities."""
    
    def __init__(self, collection_id: str, **kwargs) -> None:
        """
        Initialize collection wrapper.
        
        Args:
            collection_id: Earth Engine collection ID
            **kwargs: Additional parameters
        """

class CollectionList:
    """List of Earth Engine collections with search capabilities."""
    
    def __init__(self, collections: List[Collection] = None, **kwargs) -> None:
        """
        Initialize collection list.
        
        Args:
            collections: List of collection objects
            **kwargs: Additional parameters
        """

class PrecomputedEmbeddings:
    """Precomputed embeddings for semantic search."""
    
    def __init__(
        self,
        embeddings_path: str = None,
        **kwargs
    ) -> None:
        """
        Initialize precomputed embeddings.
        
        Args:
            embeddings_path: Path to embeddings file
            **kwargs: Additional parameters
        """
    
    def search(
        self,
        query: str,
        top_k: int = 10,
        **kwargs
    ) -> List[Dict]:
        """
        Search using semantic embeddings.
        
        Args:
            query: Search query text
            top_k: Number of top results to return
            **kwargs: Additional search parameters
            
        Returns:
            List of semantically similar results
        """

class BBox:
    """Bounding box representation with AI utilities."""
    
    def __init__(
        self,
        bounds: List[float] = None,
        crs: str = "EPSG:4326",
        **kwargs
    ) -> None:
        """
        Initialize bounding box.
        
        Args:
            bounds: Bounding box coordinates [minx, miny, maxx, maxy]
            crs: Coordinate reference system
            **kwargs: Additional parameters
        """
    
    def to_ee_geometry(self) -> ee.Geometry:
        """
        Convert bounding box to Earth Engine geometry.
        
        Returns:
            Earth Engine geometry object
        """

def run_ee_code(
    code: str,
    show_map: bool = True,
    **kwargs
) -> Any:
    """
    Execute Earth Engine code with AI assistance.
    
    Args:
        code: Earth Engine Python code to execute
        show_map: Whether to display map if created
        **kwargs: Additional execution parameters
        
    Returns:
        Execution result
    """

def make_langchain_index(
    docs: List[str],
    index_name: str = "geemap_index",
    **kwargs
) -> Any:
    """
    Create LangChain index for document search.
    
    Args:
        docs: List of documents to index
        index_name: Name for the index
        **kwargs: Index configuration parameters
        
    Returns:
        LangChain index object
    """

def matches_interval(
    value: float,
    interval: List[float],
    **kwargs
) -> bool:
    """
    Check if value matches specified interval.
    
    Args:
        value: Value to check
        interval: Interval bounds [min, max]
        **kwargs: Additional parameters
        
    Returns:
        True if value is within interval
    """

def matches_datetime(
    date_str: str,
    pattern: str,
    **kwargs
) -> bool:
    """
    Check if datetime string matches pattern.
    
    Args:
        date_str: Date string to check
        pattern: Date pattern to match
        **kwargs: Additional parameters
        
    Returns:
        True if date matches pattern
    """

import geemap

# Create Genie AI assistant
genie = geemap.Genie()

# Display the assistant widget
genie

# The Genie widget provides:
# - Natural language to Earth Engine code conversion
# - Interactive guidance for geospatial analysis
# - Code suggestions and optimization
# - Help with Earth Engine API usage

import ee

# Initialize Earth Engine
ee.Initialize()

# Prepare training data
training_points = ee.FeatureCollection('projects/google/ml_training_data')

# Create and train classifier
classifier = geemap.fc_to_classifier(
    training_points,
    classifier='RandomForest',
    mode='classification'
)

# Apply classifier to image
image = ee.Image('LANDSAT/LC08/C02/T1_L2/LC08_044034_20140318')
classified = image.classify(classifier)

# Display results
m = geemap.Map()
m.addLayer(classified, {'min': 0, 'max': 3, 'palette': ['red', 'green', 'blue', 'yellow']}, 'Classified')
m

# Export trained classifier trees
trees_fc = geemap.export_trees_to_fc(classifier, training_points)

# Convert trees to strings
tree_strings = geemap.rf_to_strings(classifier)

# Export to CSV
geemap.trees_to_csv(tree_strings, 'classifier_trees.csv')

# Import classifier from CSV
imported_classifier = geemap.csv_to_classifier('classifier_trees.csv')

# Create catalog interface
catalog = geemap.Catalog()

# Search with natural language
results = catalog.search(
    keywords="high resolution satellite imagery of forests",
    spatial_filter=ee.Geometry.Rectangle([-120, 35, -115, 40]),
    temporal_filter=['2020-01-01', '2020-12-31']
)

# Display results
for result in results[:5]:
    print(f"Dataset: {result['title']}")
    print(f"Description: {result['description']}")
    print(f"ID: {result['id']}")
    print("---")

# Initialize precomputed embeddings
embeddings = geemap.PrecomputedEmbeddings('path/to/embeddings.pkl')

# Perform semantic search
results = embeddings.search(
    query="forest loss detection using machine learning",
    top_k=5
)

# Process results
for result in results:
    print(f"Similarity: {result['score']:.3f}")
    print(f"Content: {result['text']}")
    print("---")

# Execute Earth Engine code with assistance
ee_code = """
var image = ee.Image('MODIS/006/MOD13A2/2020_01_01');
var ndvi = image.select('NDVI');
Map.addLayer(ndvi, {min: 0, max: 9000, palette: ['red', 'yellow', 'green']}, 'NDVI');
"""

result = geemap.run_ee_code(ee_code, show_map=True)

# Create document index for search
documents = [
    "Earth Engine provides access to satellite imagery",
    "MODIS data is useful for vegetation monitoring",
    "Landsat offers medium resolution multispectral data"
]

index = geemap.make_langchain_index(
    documents,
    index_name="earth_engine_docs"
)

# Use index for question answering
# (Additional LangChain code would go here)

# Create bounding box
bbox = geemap.BBox(bounds=[-122, 37, -121, 38])

# Convert to Earth Engine geometry
ee_geom = bbox.to_ee_geometry()

# Use in Earth Engine operations
image = ee.Image('LANDSAT/LC08/C02/T1_L2/LC08_044034_20140318')
clipped = image.clip(ee_geom)

# Classifier types
ClassifierType = Literal["RandomForest", "SVM", "NaiveBayes", "DecisionTree"]

# Classification mode
ClassificationMode = Literal["classification", "regression"]

# Search results
SearchResult = Dict[str, Union[str, float, List]]

# Embedding vector
Embedding = List[float]

# Date interval
DateInterval = List[str]  # [start_date, end_date]

# Bounding box coordinates
BoundsCoords = List[float]  # [minx, miny, maxx, maxy]

# Tree representation
TreeString = str

# AI query response
AIResponse = Dict[str, Any]