import numpy as np
from PIL import Image
from sklearn.decomposition import PCA
from sklearn.preprocessing import StandardScaler

class VisualProcessingAGI:
    def __init__(self):
        self.preprocessor = None
        self.feature_extractor = None
        self.decision_model = None
    
    def load_image(self, image_path: str) -> np.ndarray:
        """Load an image from a given path and preprocess it."""
        img = Image.open(image_path)
        img = img.convert("L")  # Convert to grayscale for simplicity
        img_array = np.array(img).astype(np.float32) / 255.0
        return img_array
    
    def preprocess(self, image: np.ndarray) -> np.ndarray:
        """Preprocess the input image before feature extraction."""
        if self.preprocessor is None:
            # Example preprocessor: standardize the data
            self.preprocessor = StandardScaler()
            flattened_image = image.flatten().reshape(1, -1)
            standardized_image = self.preprocessor.fit_transform(flattened_image).reshape(image.shape)
        else:
            standardized_image = self.preprocessor.transform(image.reshape(1, -1)).reshape(image.shape)
        
        return standardized_image
    
    def extract_features(self, preprocessed_image: np.ndarray) -> np.ndarray:
        """Extract features from the preprocessed image."""
        if self.feature_extractor is None:
            # Example feature extraction method using PCA
            pca = PCA(n_components=50)  # Adjust based on your requirements
            flattened_image = preprocessed_image.flatten().reshape(1, -1)
            extracted_features = pca.fit_transform(flattened_image).flatten()
            self.feature_extractor = pca
        else:
            extracted_features = self.feature_extractor.transform(preprocessed_image.reshape(1, -1)).flatten()
        
        return extracted_features
    
    def make_decision(self, features: np.ndarray) -> str:
        """Make a decision based on the features."""
        if self.decision_model is None:
            # Example decision model
            from sklearn.ensemble import RandomForestClassifier
            X_train = np.random.rand(100, 50)  # Dummy training data for illustration purposes
            y_train = np.random.randint(2, size=100)
            self.decision_model = RandomForestClassifier()
            self.decision_model.fit(X_train, y_train)
        
        prediction = self.decision_model.predict(features.reshape(1, -1))
        if prediction[0] == 0:
            return "The image is not relevant."
        else:
            return "The image is relevant."

    def process_image(self, image_path: str) -> str:
        """Process the image and make a decision based on its content."""
        img = self.load_image(image_path)
        preprocessed_img = self.preprocess(img)
        features = self.extract_features(preprocessed_img)
        decision = self.make_decision(features)
        return decision

# Example usage
if __name__ == "__main__":
    agi = VisualProcessingAGI()
    decision = agi.process_image("path/to/your/image.jpg")
    print(decision)