Spaces:
Sleeping
Sleeping
File size: 4,142 Bytes
e2bff0a |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 |
from typing import List, Dict, Tuple
from dataclasses import dataclass
import json
from enum import Enum
class SizeLabel(Enum):
XS = 1
S = 2
M = 3
L = 4
XL = 5
XXL = 6
@dataclass
class SizeRange:
label: str
waist: List[int]
hip: List[int]
length: List[int]
@dataclass
class Brand:
brand: str
sizes: List[SizeRange]
def load_size_data() -> List[Brand]:
"""Load and parse the size data from JSON."""
with open('./data/size_data_pants.json', 'r') as f:
data = json.load(f)
brands = []
for brand_data in data:
sizes = []
for size in brand_data['sizes']:
sizes.append(SizeRange(
label=size['label'],
waist=size['waist'],
hip=size['hip'],
length=size['length']
))
brands.append(Brand(brand=brand_data['brand'], sizes=sizes))
return brands
def find_size_category(measurement: int, ranges: List[List[int]]) -> List[int]:
"""Find matching size indices for a given measurement."""
return [i for i, (min_val, max_val) in enumerate(ranges) if min_val <= measurement <= max_val]
def get_best_size(brand_name: str, waist: int, hip: int, length: int) -> Tuple[str, Dict[str, str]]:
"""
Determine the best size for given measurements and brand.
Args:
brand_name (str): Name of the brand
waist (int): Waist measurement in cm
hip (int): Hip measurement in cm
length (int): Pants length in cm
Returns:
Tuple[str, Dict[str, str]]: Recommended size label and detailed fit information
"""
brands = load_size_data()
brand = next((b for b in brands if b.brand.lower() == brand_name.lower()), None)
if not brand:
raise ValueError(f"Brand '{brand_name}' not found")
waist_ranges = [size.waist for size in brand.sizes]
hip_ranges = [size.hip for size in brand.sizes]
length_ranges = [size.length for size in brand.sizes]
waist_sizes = find_size_category(waist, waist_ranges)
hip_sizes = find_size_category(hip, hip_ranges)
length_sizes = find_size_category(length, length_ranges)
if not waist_sizes or not hip_sizes or not length_sizes:
measurements_info = {
"waist": "too small" if waist < waist_ranges[0][0] else "too large" if waist > waist_ranges[-1][1] else "ok",
"hip": "too small" if hip < hip_ranges[0][0] else "too large" if hip > hip_ranges[-1][1] else "ok",
"length": "too short" if length < length_ranges[0][0] else "too long" if length > length_ranges[-1][1] else "ok"
}
return "No exact fit", measurements_info
all_indices = waist_sizes + hip_sizes + length_sizes
avg_size_index = round(sum(all_indices) / len(all_indices))
recommended_size = brand.sizes[avg_size_index].label
fit_info = {
"waist": "perfect" if avg_size_index in waist_sizes else "tight" if avg_size_index > max(waist_sizes) else "loose",
"hip": "perfect" if avg_size_index in hip_sizes else "tight" if avg_size_index > max(hip_sizes) else "loose",
"length": "perfect" if avg_size_index in length_sizes else "short" if avg_size_index > max(length_sizes) else "long"
}
return recommended_size, fit_info
def print_size_recommendation(brand: str, waist: int, hip: int, length: int) -> None:
"""
Print a formatted size recommendation.
Args:
brand (str): Brand name
waist (int): Waist measurement in cm
hip (int): Hip measurement in cm
length (int): Pants length in cm
"""
try:
size, fit_info = get_best_size(brand, waist, hip, length)
print(f"\nSize Recommendation for {brand}:")
print(f"Recommended size: {size}")
print("\nFit Details:")
print(f"Waist: {fit_info['waist']}")
print(f"Hip: {fit_info['hip']}")
print(f"Length: {fit_info['length']}")
except ValueError as e:
print(f"Error: {str(e)}")
# Example usage
if __name__ == "__main__":
print_size_recommendation("Zara", 80, 95, 105)
|