from sklearn.cluster import DBSCAN
from sklearn.preprocessing import LabelEncoder
import pandas as pd

# Sample data
data = [
    ["green", "nature", "thumbnail", "landscape"],
    ["blue", "architecture", "medium", "portrait"],
    ["blue", "people", "medium", "landscape"],
    ["yellow", "nature", "medium", "portrait"],
    ["green", "nature", "thumbnail", "landscape"],
    ["blue", "people", "medium", "landscape"],
    ["blue", "nature", "thumbnail", "portrait"],
    ["yellow", "architecture", "thumbnail", "landscape"],
    ["blue", "people", "medium", "portrait"],
    ["yellow", "nature", "medium", "landscape"],
    ["yellow", "people", "thumbnail", "portrait"],
    ["blue", "people", "medium", "landscape"],
    ["red", "architecture", "thumbnail", "landscape"],
]

# Encode categorical features
label_encoders = [LabelEncoder() for _ in range(len(data[0]))]
encoded_data = []
for i, column in enumerate(zip(*data)):
    encoded_data.append(label_encoders[i].fit_transform(column))

X = list(zip(*encoded_data))  # Features

# Clustering
eps = 1  # Epsilon for DBSCAN
min_samples = 2  # Minimum samples for DBSCAN
clusters = DBSCAN(eps=eps, min_samples=min_samples)
labels = clusters.fit_predict(X)

# Add cluster labels to the original data
data_with_clusters = pd.DataFrame(data, columns=["Color", "Category", "Size", "Type"])
data_with_clusters["Cluster"] = labels

# Recommendation function
def recommend_items(cluster_label, data_with_clusters):
    items_in_cluster = data_with_clusters[data_with_clusters["Cluster"] == cluster_label]
    recommended_items = items_in_cluster.sample(n=min(3, len(items_in_cluster)))  # Sample up to 3 items from the cluster
    return recommended_items

# Example usage
user_interaction = ["yellow", "architecture", "thumbnail", "landscape"]  # Assuming user interacted with this item
interaction_index = data.index(user_interaction)
cluster = clusters.labels_[interaction_index]
recommendations = recommend_items(cluster, data_with_clusters)
print("Recommended items:")
print(recommendations)