Update README.md

README.md

@@ -113,49 +113,6 @@ Images are stored in byte format, you can decode with `Image.open(BytesIO(img["b
 * **is_preset** - if the image is from the "random image button"
 * **dataset_preset** - which dataset the preset image is from. This can be either [NewYorker](https://huggingface.co/datasets/jmhessel/newyorker_caption_contest), [WikiArt](https://huggingface.co/datasets/huggan/wikiart), [TextVQA](https://huggingface.co/datasets/facebook/textvqa), [ChartQA](https://huggingface.co/datasets/lmms-lab/ChartQA), [DocQA](https://huggingface.co/datasets/lmms-lab/DocVQA), or [realworldqa](https://x.ai/blog/grok-1.5v)
 
-
-## Download Locally
-
-To download the dataset into a local directory, the code below will download the images into the VisionArena-Chat folder
-```
-from datasets import load_dataset
-from PIL import Image
-from io import BytesIO
-import json
-import os
-from tqdm import tqdm
-from multiprocessing import Pool, cpu_count
-
-def download_dataset(num_workers=None):
-    base_dir, images_dir = "VisionArena-Chat", os.path.join("VisionArena-Chat", "images")
-    os.makedirs(images_dir, exist_ok=True)
-    ds = load_dataset("lmarena-ai/VisionArena-Chat", split="train")
-    samples = list(ds)
-    num_workers = num_workers or min(cpu_count(), 8)
-    print(f"Processing samples using {num_workers} workers...")
-
-    def process_sample(idx_sample):
-        idx, sample = idx_sample
-        processed_images = []
-        for img_idx, img in enumerate(sample.get("images", [])):
-            img_filename = f"image_{idx}_{img_idx}.png"
-            img_path = os.path.join(images_dir, img_filename)
-            if not os.path.exists(img_path):
-                Image.open(BytesIO(img["bytes"])).save(img_path)
-            processed_images.append(os.path.join("images", img_filename))
-        sample["images"] = processed_images
-        return sample
-
-    with Pool(num_workers) as pool:
-        processed_data = list(tqdm(pool.imap(process_sample, enumerate(samples)), total=len(samples), desc="Processing samples"))
-    with open(os.path.join(base_dir, "data.json"), 'w', encoding='utf-8') as f:
-        json.dump(processed_data, f, ensure_ascii=False, indent=2)
-    print(f"Dataset downloaded and processed successfully!\nImages saved in: {images_dir}\nData saved in: {os.path.join(base_dir, 'data.json')}")
-
-if __name__ == "__main__":
-    download_dataset()
-```
-
 ## Bias, Risks, and Limitations
 
 This dataset contains a large amount of STEM related questions, OCR tasks, and general problems like captioning. This dataset contains less questions which relate to specialized domains outside of stem.