service.py

from sklearn.metrics.pairwise import cosine_similarity
from model import model

# Calculate cosine similarity between two messages
def calculate_cosine_similarity(message1, message2):
    embeddings = model.encode([message1, message2], convert_to_tensor=True)
    embeddings = embeddings.detach().numpy()
    similarity = cosine_similarity([embeddings[0]], [embeddings[1]])[0][0]
    return similarity