46        features_db.append(product_features)
47    features_db = np.array(features_db)
48
49    # Calculate the similarity between the uploaded image and the products in the database50    nbrs = NearestNeighbors(n_neighbors=5,
51                            algorithm="ball_tree").fit(features_db)
52    distances, indices = nbrs.kneighbors([uploaded_features])

39
40
41def predict_inventory(model, transaction):
42    """Predicts the inventory level for the given transaction using the trained model."""43    # Preprocess the transaction data
44    transaction = preprocess_data(transaction)
45

  3
  4
  5def recommend_products(user_id, num_recommendations):
  6    """Recommends the top N products for the given user based on their past purchases and browsing history."""  7    # Connect to the database
  8    connection = connect_to_db()
  9

 91
 92def evaluate_model(user_purchase_matrix, recommendation_model):
 93    """Evaluates the given recommendation model based on the given user purchase matrix."""
 94    # Evaluate the recommendation model using metrics such as precision, recall, and F1 score 95    evaluations = {}
 96    evaluations["precision"] = metrics.precision_score(
 97        user_purchase_matrix.todense(),

 90
 91
 92def evaluate_model(user_purchase_matrix, recommendation_model):
 93    """Evaluates the given recommendation model based on the given user purchase matrix.""" 94    # Evaluate the recommendation model using metrics such as precision, recall, and F1 score
 95    evaluations = {}
 96    evaluations["precision"] = metrics.precision_score(