import streamlit as st
from sklearn.cluster import KMeans
import matplotlib.pyplot as plt

st.header("Clustering: kmeans")


if "data" in st.session_state:
    data = st.session_state.data

    with st.form("my_form"):
        row1 = st.columns([1,1,1])
        n_clusters = row1[0].selectbox("Number of clusters", range(1,data.shape[0]))
        data_name = row1[1].multiselect("Data Name",data.select_dtypes(include="number").columns, max_selections=3)
        n_init = row1[2].number_input("n_init",step=1,min_value=1)

        row2 = st.columns([1,1])
        max_iter = row1[0].number_input("max_iter",step=1,min_value=1)


        st.form_submit_button("launch")

    if len(data_name) >= 2 and len(data_name) <=3:
        x = data[data_name].to_numpy()

        kmeans = KMeans(n_clusters=n_clusters, init="random", n_init=n_init, max_iter=max_iter, random_state=111)
        y_kmeans = kmeans.fit_predict(x)

        fig = plt.figure()
        if len(data_name) == 2:
            ax = fig.add_subplot(projection='rectilinear')
            plt.scatter(x[:, 0], x[:, 1], c=y_kmeans, s=50, cmap="viridis")
            centers = kmeans.cluster_centers_
            plt.scatter(centers[:, 0], centers[:, 1], c="black", s=200, marker="X")
        else:
            ax = fig.add_subplot(projection='3d')

            ax.scatter(x[:, 0], x[:, 1],x[:, 2], c=y_kmeans, s=50, cmap="viridis")
            centers = kmeans.cluster_centers_
            ax.scatter(centers[:, 0], centers[:, 1],centers[:, 2], c="black", s=200, marker="X")
        st.pyplot(fig)

else:
    st.error("file not loaded")