File size: 3,503 Bytes
af1d8cd | 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 | import requests
import os
# API_URL = "https://api-inference.huggingface.co/models/google/vit-base-patch16-224"
# Trying ResNet-50 as it's very standard
API_URL = "https://api-inference.huggingface.co/models/microsoft/resnet-50"
def classify_garment(image_path: str):
"""
Classifies a garment image using Hugging Face API with Filename Fallback.
"""
# 1. Try Keyword Analysis on Filename (Fast & Robust Fallback)
filename = os.path.basename(image_path).lower()
bottom_keywords = ["jean", "denim", "trouser", "pant", "skirt", "short", "legging", "sweatpant", "trunks"]
shoe_keywords = ["shoe", "sneaker", "boot", "sandal", "heel", "loafer", "clog"]
top_keywords = ["shirt", "jersey", "tee", "top", "blouse", "sweater", "cardigan", "jacket", "coat", "hoodie", "vest"]
fallback_category = "top"
fallback_desc = "Uploaded Item"
if any(k in filename for k in bottom_keywords):
fallback_category = "bottom"
fallback_desc = filename.split('.')[0].replace('_', ' ').title()
print(f"DEBUG: Filename '{filename}' matches BOTTOM keywords.")
elif any(k in filename for k in shoe_keywords):
fallback_category = "shoes"
fallback_desc = filename.split('.')[0].replace('_', ' ').title()
print(f"DEBUG: Filename '{filename}' matches SHOES keywords.")
elif any(k in filename for k in top_keywords):
fallback_category = "top"
fallback_desc = filename.split('.')[0].replace('_', ' ').title()
print(f"DEBUG: Filename '{filename}' matches TOP keywords.")
# 2. Try API
token = os.environ.get("HF_TOKEN")
if not token and os.path.exists("hf_token.txt"):
with open("hf_token.txt", "r") as f:
token = f.read().strip()
if not token:
print("Warning: No HF Token. Using filename fallback.")
return fallback_category, fallback_desc, "untagged"
headers = {"Authorization": f"Bearer {token}"}
try:
with open(image_path, "rb") as f:
data = f.read()
print(f"Calling HF API ({API_URL})...")
response = requests.post(API_URL, headers=headers, data=data, timeout=5)
if response.status_code != 200:
print(f"HF API Error: {response.status_code}. Using fallback.")
return fallback_category, fallback_desc, "api-error"
predictions = response.json()
if not predictions or not isinstance(predictions, list):
return fallback_category, fallback_desc, "api-unknown"
top_prediction = predictions[0]
label = top_prediction.get('label', '').lower()
score = top_prediction.get('score', 0)
print(f"AI Classification: '{label}' ({score:.2f})")
# Map to Category
category = "top"
description = label.split(',')[0].title()
if any(k in label for k in bottom_keywords):
category = "bottom"
elif any(k in label for k in shoe_keywords):
category = "shoes"
# SEO Naming
if "jersey" in label or "tee" in label:
description = "T-Shirt"
elif "jean" in label:
description = "Denim Jeans"
return category, description, label
except Exception as e:
print(f"Classification Failed: {e}. Using fallback.")
return fallback_category, fallback_desc, "error"
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