Confusion Matrix Heatmap

import matplotlib.pyplot as plt
import numpy as np
from matplotlib.colors import LinearSegmentedColormap
from matplotlib.patches import FancyBboxPatch

fig, ax = plt.subplots(figsize=(10, 9), facecolor='#ffffff')
ax.set_facecolor('#ffffff')

np.random.seed(42)
classes = ['Cat', 'Dog', 'Bird', 'Fish', 'Horse']
n = len(classes)
data = np.random.randint(0, 20, (n, n))
np.fill_diagonal(data, np.random.randint(80, 150, n))

colors = ['#1e293b', '#0c4a6e', '#0284c7', '#38bdf8', '#bae6fd']
cmap = LinearSegmentedColormap.from_list('cm', colors, N=256)

cell_w, cell_h = 0.88, 0.88
for i in range(n):
    for j in range(n):
        val = data[i, j]
        rect = FancyBboxPatch((j - cell_w/2, i - cell_h/2), cell_w, cell_h,
                               boxstyle="round,pad=0.02,rounding_size=0.12",
                               facecolor=cmap(val/data.max()), edgecolor='#e2e8f0', linewidth=1.5)
        ax.add_patch(rect)
        ax.text(j, i, str(val), ha='center', va='center', color='#1e293b', fontsize=12, fontweight='bold')

ax.set_xlim(-0.5, n-0.5)
ax.set_ylim(-0.5, n-0.5)
ax.set_aspect('equal')
ax.invert_yaxis()
ax.set_xticks(range(n))
ax.set_yticks(range(n))
ax.set_xticklabels(classes, color='#64748b', fontsize=10, fontweight='500')
ax.set_yticklabels(classes, color='#1e293b', fontsize=10, fontweight='500')

sm = plt.cm.ScalarMappable(cmap=cmap, norm=plt.Normalize(0, data.max()))
cbar = plt.colorbar(sm, ax=ax, shrink=0.8, pad=0.02)
cbar.set_label('Count', color='#1e293b', fontsize=11)
cbar.outline.set_edgecolor('#e2e8f0')
plt.setp(plt.getp(cbar.ax.axes, 'yticklabels'), color='#64748b')

for spine in ax.spines.values(): spine.set_visible(False)
ax.tick_params(length=0)
ax.set_xlabel('Predicted', fontsize=12, color='#64748b', labelpad=10)
ax.set_ylabel('Actual', fontsize=12, color='#64748b', labelpad=10)
ax.set_title('Classification Confusion Matrix', fontsize=18, color='#1e293b', fontweight='bold', pad=20)
plt.tight_layout()
plt.show()