import matplotlib.pyplot as plt
import numpy as np
from scipy.interpolate import make_interp_spline

# --- Your Data ---
# Number of prompts
x_data = np.array([10, 50, 100, 300, 500, 1000, 2000])
# Time in seconds
y_data = np.array([2, 8.4, 13.1, 34.5, 57, 101, 194])

# --- Create a smooth curve ---
# Generate a spline for smooth interpolation.
spline = make_interp_spline(x_data, y_data, k=3)  # k=3 for a cubic spline

# Create a new set of x-values for a smoother line (e.g., 400 points).
x_smooth = np.linspace(x_data.min(), x_data.max(), 400)
y_smooth = spline(x_smooth)

# --- Styling and Plotting ---

# Apply a dark, tech-style theme.
plt.style.use('dark_background')

# Create the figure and axes
fig, ax = plt.subplots(figsize=(10, 6))

# Plot the main, solid line on top
ax.plot(x_smooth, y_smooth, linewidth=2, color=glow_color)

# Plot the original data points
ax.scatter(x_data, y_data, color='#FE53BB', marker='o', s=100, zorder=10, 
           edgecolors=glow_color, linewidth=1.5, label='Original Data Points')


# --- Aesthetics and Labels ---

# Set title and labels with a futuristic font style
title_font = {'family': 'sans-serif', 'color':  'white', 'weight': 'bold', 'size': 18}
label_font = {'family': 'sans-serif', 'color':  '#c7c7c7', 'weight': 'normal', 'size': 12}

ax.set_title('Performance Benchmark', fontdict=title_font, pad=20)
ax.set_xlabel('Number of Prompts', fontdict=label_font, labelpad=10)
ax.set_ylabel('Time (seconds)', fontdict=label_font, labelpad=10)

# Customize the grid for a tech look
ax.grid(True, which='both', linestyle='--', linewidth=0.5, color='#343434')

# Customize spines (the plot border)
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.spines['left'].set_color('#565656')
ax.spines['bottom'].set_color('#565656')

# Customize tick parameters
ax.tick_params(axis='x', colors='#c7c7c7')
ax.tick_params(axis='y', colors='#c7c7c7')

# Add a legend
legend = ax.legend()
for text in legend.get_texts():
    text.set_color("white")

# Ensure a tight layout to prevent labels from being cut off
plt.tight_layout()

# --- Display the Plot ---
plt.show()