import os
from sklearn.metrics.pairwise import cosine_similarity
import numpy as np

class SimilarityCalculator:
    """
    Class for calculating cosine similarity between embeddings.
    """
    def __init__(self):
        pass

    def compute_similarity(template_embeddings: np.ndarray, contract_embeddings: np.ndarray) -> np.ndarray:
        """
        Compute cosine similarity between template and contract embeddings.

        Args:
            template_embeddings (np.ndarray): A NumPy array of template embeddings.
            contract_embeddings (np.ndarray): A NumPy array of contract embeddings.

        Returns:
            np.ndarray: A NumPy array of similarity scores between contracts and templates.
        """
        return cosine_similarity(contract_embeddings, template_embeddings)

    def clear_folder(path):
        if not os.path.exists(path):
            os.makedirs(path)  # Create the directory if it doesn't exist
        for file in os.listdir(path):
            file_path = os.path.join(path, file)
            try:
                if os.path.isfile(file_path):
                    os.unlink(file_path)
            except Exception as e:
                print(f"Failed to delete {file_path}: {e}")

    def save_uploaded_file(uploaded_file, path):
        try:
            with open(os.path.join(path, uploaded_file.name), "wb") as f:
                f.write(uploaded_file.getbuffer())
            return True
        except:
            return False