Create similarityCalculator.py

similarityCalculator.py

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+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