import numpy as np
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score

class MachineLearningAI:
    def __init__(self):
        self.model = RandomForestClassifier(n_estimators=100)
        self.data = None
        self.labels = None

    def load_data(self, data, labels):
        self.data = data
        self.labels = labels

    def train_model(self):
        if self.data is None or self.labels is None:
            raise ValueError("Data and labels must be loaded before training the model.")
        
        X_train, X_test, y_train, y_test = train_test_split(self.data, self.labels, test_size=0.2, random_state=42)
        self.model.fit(X_train, y_train)
        predictions = self.model.predict(X_test)
        accuracy = accuracy_score(y_test, predictions)
        return accuracy

    def predict(self, new_data):
        if self.model is None:
            raise ValueError("Model must be trained before making predictions.")
        
        return self.model.predict(new_data)

    def render(self):
        return "Machine Learning and AI Module: Ready to improve the accuracy and efficiency of the framework."

    def integrate_with_new_components(self, new_component_data):
        # Placeholder for integration logic with new components
        integrated_data = {
            "new_component_data": new_component_data.get("data", {}),
            "new_component_labels": new_component_data.get("labels", {})
        }
        return integrated_data

    def ensure_compatibility(self, existing_data, new_component_data):
        # Placeholder for compatibility logic
        compatible_data = {
            "existing_data": existing_data.get("data", {}),
            "existing_labels": existing_data.get("labels", {}),
            "new_component_data": new_component_data.get("data", {}),
            "new_component_labels": new_component_data.get("labels", {})
        }
        return compatible_data