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README.md

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 ---
-{}
----
+dataset_info:
+  features:
+  - name: src_html_path
+    dtype: string
+  - name: src_css_path
+    dtype: string
+  - name: web_type
+    dtype: string
+  - name: css_framework
+    dtype: string
+  - name: image_instruct
+    dtype: image
+  - name: modification_category
+    dtype: string
+  - name: style
+    dtype: string
+  - name: image_has_arrow
+    dtype: bool
+  - name: image_has_enclosure
+    dtype: bool
+  - name: image_has_ui_sketch
+    dtype: bool
+  - name: ref_html_path
+    dtype: string
+  - name: ref_css_path
+    dtype: string
+  splits:
+  - name: test
+    num_bytes: 888818482
+    num_examples: 350
+  download_size: 887208210
+  dataset_size: 888818482
+configs:
+- config_name: default
+  data_files:
+  - split: test
+    path: data/test-*
+---
+
+# UI-Redline-bench
+
+This dataset contains the benchmark data for the paper **"UI-Redline-bench: 赤入れ指示によるWebUIコード修正ベンチマーク"**.
+
+The benchmark evaluates the capability of Vision-Language Models (VLMs) to modify Web UI code (HTML/CSS) based on visual "redline" instructions (handwritten or digital) drawn on screenshots.
+
+**📄 [Paper](https://www.anlp.jp/proceedings/annual_meeting/2026/pdf_dir/B8-1.pdf)** | **💻 [GitHub Repository (Evaluation Code & Runnable Environment)](https://github.com/future-architect/UI-Redline-bench)** 
+
+## Dataset Description
+
+* **Repository:** [future-architect/UI-Redline-bench](https://github.com/future-architect/UI-Redline-bench)
+* **Total Instances:** 350
+* **Web Types:** News, Online Store, Portfolio
+* **CSS Frameworks:** Vanilla, Bootstrap, Tailwind CSS
+* **Modification Categories:** Layout, Color Contrast, Text Readability, Button Usability, Learnability
+
+### Usage
+
+This Hugging Face dataset contains only the images and the corresponding metadata.
+To run the experiments, please follow the steps below to clone the GitHub repository for evaluation scripts and place the dataset accordingly.
+You can optionally clone this Hugging Face repository if you want to inspect the images manually.
+
+```bash
+mkdir ui-redline-workspace
+cd ui-redline-workspace
+
+# 1. Clone the GitHub repository (REQUIRED for running code)
+git clone https://github.com/future-architect/UI-Redline-bench.git
+
+# 2. (Optional) Clone this Hugging Face dataset
+# Only needed if you want to browse instruction images manually on your local machine.
+# The python script will download the dataset automatically via API.
+
+# Initialize Git LFS (Required to download large image/parquet files)
+git lfs install
+
+# Clone into a specific directory to avoid name conflict
+git clone https://huggingface.co/datasets/future-architect/UI-Redline-bench UI-Redline-bench-dataset
+
+```
+
+The resulting directory structure should look like this:
+
+```text
+ui-redline-workspace/
+├── UI-Redline-bench/           # GitHub Repo: Scripts, HTML, CSS (The execution environment)
+│   ├── data/
+│   ├── script/
+│   └── ...
+└── UI-Redline-bench-dataset/   # HF Repo: (Optional) For manual image inspection
+    └── ...
+
+```
+
+## Dataset Structure
+
+Each record represents a modification task. The file paths provided (`src_html_path`, etc.) are relative to the root of the **GitHub repository** (`UI-Redline-bench/`).
+
+| Field | Type | Description |
+| --- | --- | --- |
+| `src_html_path` | string | Relative path to the **original** HTML code (e.g., `data/news/bootstrap/src/index.html`). |
+| `src_css_path` | string | Relative path to the **original** CSS code. |
+| `web_type` | string | Type of the website (`news`, `onlinestore`, `portfolio`). |
+| `css_framework` | string | CSS framework used (`vanilla`, `bootstrap`, `tailwind`). |
+| `image_instruct` | image | The visual instruction (redline) image input for the VLM. |
+| `modification_category` | string | Category of the modification (e.g., `layout`, `color_contrast`). |
+| `style` | string | Style of the visual instruction (`digital` or `handwritten`). |
+| `image_has_arrow` | bool | Whether the instruction image contains arrows. |
+| `image_has_enclosure` | bool | Whether the instruction image contains enclosures/bounding boxes. |
+| `image_has_ui_sketch` | bool | Whether the instruction image contains sketches of new UI elements. |
+| `ref_html_path` | string | Relative path to the **ground truth** HTML code. |
+| `ref_css_path` | string | Relative path to the **ground truth** CSS code. |
+
+## Usage Example (Running Inference)
+
+This example demonstrates how to load the dataset and run inference by importing the scripts directly from the cloned GitHub repository.
+Save the following code as `run_benchmark.py` in your `ui-redline-workspace` directory.
+
+```python
+import os
+import sys
+from datasets import load_dataset
+
+# 1. Setup Paths
+# Assuming you are in the 'ui-redline-workspace' directory.
+GITHUB_REPO_ROOT = os.path.abspath("./UI-Redline-bench") 
+HF_DATASET_PATH = os.path.abspath("./UI-Redline-bench-dataset")
+OUTPUT_DIR = os.path.abspath("./output_results")
+
+# 2. Add GitHub script directory to sys.path to allow imports
+sys.path.append(os.path.join(GITHUB_REPO_ROOT, "script"))
+
+try:
+    # -------------------------------------------------------------------------
+    # IMPORT THE TARGET MODEL SCRIPT HERE
+    # Change this line depending on the model you want to evaluate:
+    #   from prediction_based_on_image_gpt5 import process_sample
+    #   from prediction_based_on_image_claude import process_sample
+    #   from prediction_based_on_image_gemini import process_sample
+    #   from prediction_based_on_image_qwen import process_sample
+    # -------------------------------------------------------------------------
+    from prediction_based_on_image_gemini import process_sample
+except ImportError as e:
+    print("Error importing scripts. Make sure you are running this script with the correct environment (e.g., via 'uv run').")
+    raise e
+
+# 3. Load Dataset
+if os.path.exists(HF_DATASET_PATH):
+    print(f"Loading dataset locally from: {HF_DATASET_PATH}")
+    ds = load_dataset(HF_DATASET_PATH, split="test")
+else:
+    print("Local dataset not found. Downloading from Hugging Face Hub...")
+    ds = load_dataset("future-architect/UI-Redline-bench", split="test")
+
+# 4. Iterate and Run Inference
+for example in ds:
+    # The dataset returns a PIL.Image object, which can be passed directly to the scripts.
+    img_input = example['image_instruct']
+
+    # Construct absolute paths for HTML/CSS
+    html_path = os.path.join(GITHUB_REPO_ROOT, example['src_html_path'])
+    css_path = os.path.join(GITHUB_REPO_ROOT, example['src_css_path'])
+    
+    # Construct output directory for this case
+    case_output_dir = os.path.join(OUTPUT_DIR, os.path.dirname(example['ref_html_path']))
+    
+    print(f"Processing: {html_path}")
+    
+    # Call the imported function directly
+    process_sample(
+        html_path=html_path,
+        css_path=css_path,
+        image_path=img_input,
+        output_dir=case_output_dir
+    )
+
+print("Inference completed.")
+
+```
+
+### How to execute the script
+
+Since we use `uv` for dependency management, you must run the script using the correct environment defined in the GitHub repository.
+
+**For GPT, Claude, and Gemini (API-based models):**
+Use the `cpu-env`.
+
+```bash
+uv run --project UI-Redline-bench/cpu-env python run_benchmark.py
+
+```
+
+**For Qwen (Local vLLM model):**
+Use the `gpu-env`. Make sure you have started the vLLM server beforehand.
+
+```bash
+# 1. Start the server (in a separate terminal)
+uv run --project UI-Redline-bench/gpu-env bash UI-Redline-bench/script/launch_vllm_server.sh
+
+# 2. Run the benchmark
+uv run --project UI-Redline-bench/gpu-env python run_benchmark.py
+
+```
+
+## Citation
+
+If you use this dataset, please cite our paper:
+
+```bibtex
+@inproceedings{hiai2026uiredline,
+  title={UI-Redline-bench: 赤入れ指示によるWebUIコード修正ベンチマーク},
+  author={肥合智史 and 藤井諒 and 岸波洋介 and 森下睦},
+  booktitle={Proceedings of the 32nd Annual Meeting of the Association for Natural Language Processing (NLP2026)},
+  year={2026}
+}
+
+```

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