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, 10))
        data_name = row1[1].multiselect("Data Name",data.select_dtypes(include="number").columns, max_selections=2)
        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:
        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, ax = plt.subplots(figsize=(12,8))
        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')
        st.pyplot(fig)

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