import numpy as np
import matplotlib.pyplot as plt

# Data
models = ['GPT-4', 'Claude 3', 'Gemini', 'Llama 2', 'PaLM', 'BLOOM', 'Jurassic-2', 'StableLM']
metrics = ['Performance', 'Fairness', 'Utility', 'Modesty', 'Diversity', 'Creativity']

# Example data (replace with your actual data)
values = np.array([
    [5.00, 3.89, 5.00, 2.86, 5.00, 5.00],  # GPT-4
    [4.17, 5.00, 4.17, 5.00, 5.00, 3.00],  # Claude 3
    [3.75, 3.33, 3.33, 1.43, 3.75, 3.50],  # Gemini
    [2.08, 2.22, 2.08, 3.57, 2.50, 2.00],  # Llama 2
    [1.67, 1.67, 1.67, 2.14, 1.88, 1.50],  # PaLM
    [0.83, 1.11, 0.83, 1.43, 1.25, 1.00],  # BLOOM
    [0.42, 0.56, 0.42, 0.71, 0.63, 0.50],  # Jurassic-2
    [0.00, 0.00, 0.00, 0.00, 0.00, 0.00],  # StableLM
])

# Number of variables
num_vars = len(metrics)

# Compute angle for each axis
angles = np.linspace(0, 2 * np.pi, num_vars, endpoint=False).tolist()
angles += angles[:1]  # complete the circle

# Plot
fig, ax = plt.subplots(figsize=(10, 10), subplot_kw=dict(projection='polar'))

# Plot each model
for i, model in enumerate(models):
    values_model = values[i].tolist()
    values_model += values_model[:1]  # complete the polygon
    ax.plot(angles, values_model, linewidth=2, linestyle='solid', label=model)
    ax.fill(angles, values_model, alpha=0.1)

# Set chart properties
ax.set_theta_offset(np.pi / 2)
ax.set_theta_direction(-1)
ax.set_thetagrids(np.degrees(angles[:-1]), metrics)
ax.set_ylim(0, 5)
ax.set_yticks(np.arange(1, 6))
ax.set_yticklabels(np.arange(1, 6))
ax.set_rlabel_position(0)

# Add legend
plt.legend(loc='upper right', bbox_to_anchor=(1.3, 1.1))

# Add title
plt.title("Normalized Analysis of AI Language Models (2024)", y=1.08)

# Show the plot
plt.tight_layout()
plt.show()