import pandas as pd
import joblib

def predict_sequence_label(sequence):
    """
    Predict the label for a given amino acid sequence using the saved Random Forest model.
    
    Parameters:
    sequence (str): A string representing the amino acid sequence.
    
    Returns:
    int: The predicted label (0 or 1).
    """
    
    def compute_aac_features(sequence):
        """
        Compute the Amino Acid Composition (AAC) features for a given sequence.

        Parameters:
        sequence (str): A string representing the amino acid sequence.

        Returns:
        pd.DataFrame: DataFrame containing the AAC features for the sequence.
        """
        # Define the 20 standard amino acids
        amino_acids = 'ACDEFGHIKLMNPQRSTVWY'
    
        # Initialize a dictionary to hold the counts of each amino acid
        aac_counts = {f"AAC_{aa}": 0 for aa in amino_acids}
    
        # Calculate the length of the sequence
        seq_length = len(sequence)
    
        # Count the occurrences of each amino acid in the sequence
        for aa in sequence:
            if f"AAC_{aa}" in aac_counts:
                aac_counts[f"AAC_{aa}"] += 1
    
        # Convert counts to frequencies
        aac_features = {aa: count / seq_length for aa, count in aac_counts.items()}
    
        # Convert the AAC features to a DataFrame
        aac_features_df = pd.DataFrame([aac_features])
    
        return aac_features_df
    
    # Compute AAC features from the sequence
    aac_features_df = compute_aac_features(sequence)
    
    # Load the saved Random Forest model
    saved_model = joblib.load('model.joblib')
    
    # Predict using the loaded model
    prediction = saved_model.predict(aac_features_df)
    
    return prediction[0]

# Example usage:
# sequence = "YOUR_AMINO_ACID_SEQUENCE_HERE"
# print(predict_sequence_label(sequence))