Update README.md

README.md

@@ -10,17 +10,15 @@ configs:
   data_files:
   - split: test
     path: products.parquet
+  features:
+  - name: image_path
+    dtype: image
 
 - config_name: reviews
   data_files:
   - split: test
     path: reviews.parquet
-
-dataset_info:
-  features:
-  - name: image_path
-    dtype: image
-
+    
 tags:
   - tabular
 ---
@@ -28,28 +26,14 @@ tags:
 Script to download and use the dataset:
 
 ```python
-import os
 from datasets import load_dataset
-from PIL import Image
-
-# 1. Load metadata from Hugging Face
-# This downloads the Parquet file and creates a local cache
-dataset = load_dataset("allietran/CAMEO")
-products = load_dataset("allietran/CAMEO", data_files="products.parquet")
-reviews = load_dataset("allietran/CAMEO", data_files="reviews.parquet")
 
-# 2. Define local image root
-# Participants would have cloned the 'images' folder via git-lfs or downloaded shards
-IMAGE_ROOT = "./images"
+products = load_dataset("allietran/CAMEO", "products")
+reviews = load_dataset("allietran/CAMEO", "reviews")
 
-def get_image(example):
-    """Helper to load a PIL image using the relative_path stored in metadata"""
-    img_path = os.path.join(IMAGE_ROOT, example['relative_path'])
-    if os.path.exists(img_path):
-        return Image.open(img_path)
-    return None
+print(products["test"][0])
+print(reviews["test"][0])
 
-# Example: Load the first product and its image
-print("First product metadata:", products['train'][0])
-print("First review metadata:", reviews['train'][0])
+img = products["test"][0]["image_path"]
+print(type(img))
 ```