import matplotlib.pyplot as plt
import numpy as np
import umap
from sklearn.manifold import TSNE
import tempfile

def plot_embedding(X, labels, method="UMAP", title="Clustering Visualization") -> str:
    if method.upper() == "NONE":
        # ไม่ลดมิติ กูทำแค่ plot scatter ตามข้อมูลเดิม 2 มิติ
        if X.shape[1] < 2:
            raise ValueError("Data must have at least 2 features for plotting without dimensionality reduction.")
        plt.figure(figsize=(8, 6))
        scatter = plt.scatter(X[:, 0], X[:, 1], c=labels, cmap='tab10', s=30)
        plt.title(f"No Dimensionality Reduction - {title}")
        plt.colorbar(scatter, label="Cluster ID")
        plt.tight_layout()
        with tempfile.NamedTemporaryFile(delete=False, suffix=".png") as tmp_img:
            plt.savefig(tmp_img.name)
            plt.close()
            return tmp_img.name

    elif method.upper() == "UMAP":
        reducer = umap.UMAP(random_state=69)
    elif method.upper() == "TSNE":
        reducer = TSNE(random_state=69, perplexity=30, max_iter=1000)
    else:
        raise ValueError(f"Unknown method: {method}. Use 'UMAP', 'TSNE', or 'None'.")

    X_embedded = reducer.fit_transform(X)

    plt.figure(figsize=(8, 6))
    scatter = plt.scatter(X_embedded[:, 0], X_embedded[:, 1], c=labels, cmap='tab10', s=30)
    plt.title(f"{method.upper()} - {title}")
    plt.colorbar(scatter, label="Cluster ID")
    plt.tight_layout()

    with tempfile.NamedTemporaryFile(delete=False, suffix=".png") as tmp_img:
        plt.savefig(tmp_img.name)
        plt.close()
        return tmp_img.name

def plot_som(som_model, X_scaled, labels):
    """
    Visualize SOM clustering result with U-Matrix + labeled points.
    som_model: trained SOM object (เช่น MiniSom)
    X_scaled: scaled data array
    labels: cluster labels assigned for each point
    """

    plt.figure(figsize=(8, 8))

    # วาด U-Matrix (distance map)
    plt.pcolor(som_model.distance_map().T, cmap='bone_r')
    plt.colorbar(label='Distance')

    # วาดจุดข้อมูลบน SOM grid
    markers = ['o', 's', 'D', '^', 'v', 'p', '*', 'h', 'x', '+']  # marker สำหรับ cluster สูงสุด 10 กลุ่ม
    colors = plt.cm.tab10.colors

    for cnt, x in enumerate(X_scaled):
        w = som_model.winner(x)  # ตำแหน่ง node ที่ชนะ (winner neuron)
        cluster_id = labels[cnt] - 1  # adjust label to zero-based index
        plt.plot(w[0] + 0.5, w[1] + 0.5, markers[cluster_id % len(markers)],
                 markerfacecolor=colors[cluster_id % len(colors)],
                 markeredgecolor='k',
                 markersize=12,
                 markeredgewidth=1.5)

    plt.title("SOM Clustering Visualization (U-Matrix + Clustered Data Points)")
    plt.tight_layout()

    with tempfile.NamedTemporaryFile(delete=False, suffix=".png") as tmp_img:
        plt.savefig(tmp_img.name)
        plt.close()
        return tmp_img.name