import streamlit as st
import matplotlib.pyplot as plt
from clusters import DBSCANCluster, KMeansCluster, CLUSTERING_STRATEGIES
from sklearn.decomposition import PCA
from sklearn.metrics import silhouette_score
import numpy as np

st.header("Clustering")

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

    general_row = st.columns([1, 1, 1])
    clustering = general_row[0].selectbox("Clustering method", CLUSTERING_STRATEGIES)
    data_name = general_row[1].multiselect("Columns", data.select_dtypes(include="number").columns)
    n_components = general_row[2].number_input("Reduce dimensions to (PCA)", min_value=1, max_value=3, value=2)

    with st.form("cluster_form"):
        if isinstance(clustering, KMeansCluster):
            row1 = st.columns([1, 1, 1])
            clustering.n_clusters = row1[0].number_input("Number of clusters", min_value=1, max_value=data.shape[0], value=clustering.n_clusters)
            clustering.n_init = row1[1].number_input("n_init", min_value=1, value=clustering.n_init)
            clustering.max_iter = row1[2].number_input("max_iter", min_value=1, value=clustering.max_iter)
        elif isinstance(clustering, DBSCANCluster):
            row1 = st.columns([1, 1])
            clustering.eps = row1[0].slider("eps", min_value=0.0001, max_value=1.0, step=0.05, value=clustering.eps)
            clustering.min_samples = row1[1].number_input("min_samples", min_value=1, value=clustering.min_samples)

        st.form_submit_button("Launch")

    if len(data_name) > 0:
        x = data[data_name].to_numpy()
        n_components = min(n_components, len(data_name))
        if len(data_name) > n_components:
            pca = PCA(n_components)
            x = pca.fit_transform(x)
            if n_components == 2:
                (fig, ax) = plt.subplots(figsize=(8, 8))
                for i in range(0, pca.components_.shape[1]):
                    ax.arrow(
                        0,
                        0,
                        pca.components_[0, i],
                        pca.components_[1, i],
                        head_width=0.1,
                        head_length=0.1
                    )

                    plt.text(
                        pca.components_[0, i] + 0.05,
                        pca.components_[1, i] + 0.05,
                        data_name[i]
                    )
                circle = plt.Circle((0, 0), radius=1, edgecolor='b', facecolor='None')
                ax.add_patch(circle)
                plt.axis("equal")
                ax.set_title("PCA result - Correlation circle")
                st.pyplot(fig)

        result = clustering.run(x)
        st.write("## Cluster stats")
        st.table(result.statistics)

        st.write("## Graphical representation")
        fig = plt.figure()
        if n_components == 1:
            plt.scatter(x, np.zeros_like(x))
        elif n_components == 2:
            ax = fig.add_subplot(projection='rectilinear')
            plt.scatter(x[:, 0], x[:, 1], c=result.labels, s=50, cmap="viridis")
            if result.centers is not None:
                plt.scatter(result.centers[:, 0], result.centers[:, 1], c="black", s=200, marker="X")
        else:
            ax = fig.add_subplot(projection='3d')

            ax.scatter(x[:, 0], x[:, 1],x[:, 2], c=result.labels, s=50, cmap="viridis")
            if result.centers is not None:
                ax.scatter(result.centers[:, 0], result.centers[:, 1], result.centers[:, 2], c="black", s=200, marker="X")
        st.pyplot(fig)
        if not (result.labels == 0).all():
            st.write("Silhouette score:", silhouette_score(x, result.labels))
    else:
        st.error("Select at least one column")

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