import matplotlib.pyplot as plt
from matplotlib.patches import Patch
from matplotlib.lines import Line2D
import numpy as np
from scipy.interpolate import make_interp_spline

# 1. DILI (Probability) Data
bin_centers = [0.10, 0.15, 0.20, 0.25, 0.30, 0.35, 0.40, 0.45, 0.50, 
               0.55, 0.60, 0.65, 0.70, 0.75, 0.80, 0.85, 0.90, 0.95]

frequencies = [0.12, 0.36, 0.81, 3.20, 4.98, 6.55, 7.61, 8.66, 8.33, 
               8.66, 7.36, 8.01, 8.70, 10.40, 7.52, 5.22, 3.11, 0.40]

# 2. Smoothing (Spline to see the shape of the distribution)
x_smooth = np.linspace(min(bin_centers), max(bin_centers), 300)
spl = make_interp_spline(bin_centers, frequencies, k=3)
y_smooth = spl(x_smooth)
y_smooth = [val if val > 0 else 0 for val in y_smooth]

# 3. Colors (Risk Traffic Light)
colors = []
for val in bin_centers:
    if val < 0.3:
        colors.append('mediumseagreen')
    elif val > 0.7:
        colors.append('firebrick')
    else:
        colors.append('gold')

# 4. Create the chart
plt.figure(figsize=(7, 6))

# A. Bars
plt.bar(bin_centers, frequencies, width=0.04, color=colors, edgecolor='black', alpha=0.7, label='Data Frequency')

# 5. Tags and Titles
plt.xlabel('DILI Probability Score (0=Safe, 1=Toxic)', fontsize=12)
plt.ylabel('% Frequency', fontsize=12)
plt.title('Drug-Induced Liver Injury (DILI) Risk Profile', fontsize=14)

# Axle settings
plt.xticks(np.arange(0.1, 1.0, 0.1))
plt.xlim(0.05, 1.0)
plt.ylim(0, 12)

# 6. Legend
legend_elements = [
    Patch(facecolor='mediumseagreen', edgecolor='black', alpha=0.7, label='Predicted Safe (< 0.3)'),
    Patch(facecolor='gold', edgecolor='black', alpha=0.7, label='Ambiguous / Monitor Req. (0.3 - 0.7)'),
    Patch(facecolor='firebrick', edgecolor='black', alpha=0.7, label='Predicted DILI Positive (> 0.7)'),
]

plt.legend(handles=legend_elements, loc='upper left', framealpha=0.95)

plt.grid(axis='y', linestyle='--', alpha=0.5)
plt.tight_layout()

plt.show()