import json
import numpy as np

def load_palettes(filepath):
    with open(filepath, 'r') as file:
        return json.load(file)

def hex_to_rgb(hex_color):
    hex_color = hex_color.lstrip('#')
    return tuple(int(hex_color[i:i+2], 16) for i in (0, 2, 4))

def rgb_distance(color1, color2):
    return np.sqrt(sum((a - b) ** 2 for a, b in zip(color1, color2)))

def predict_palette(collection_name, hex_color, filepath='output.json'):
    palettes_data = load_palettes(filepath)
    
    target_rgb = hex_to_rgb(hex_color)
    
    min_distance = float('inf')
    best_palette = None
    
    for collection in palettes_data['collections']:
        if collection['collection'] == collection_name:
            for palette in collection['colors']:
                for color in palette:
                    distance = rgb_distance(target_rgb, hex_to_rgb(color))
                    if distance < min_distance:
                        min_distance = distance
                        best_palette = palette
                
    return best_palette