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| title: Traffice Light | |
| emoji: 🔥 | |
| colorFrom: indigo | |
| colorTo: indigo | |
| sdk: gradio | |
| sdk_version: 5.47.2 | |
| app_file: app.py | |
| pinned: false | |
| Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference | |
| # 🚦 Traffic Sign Identifier | |
| An interactive image classification app for identifying traffic signs using a pretrained **AutoGluon MultiModalPredictor** model hosted on Hugging Face. | |
| The app provides **predictions with confidence scores**, a clean **Gradio interface**, and user-friendly visualizations. | |
| --- | |
| ## ✨ Features | |
| - **Traffic Sign Classification** | |
| Upload or capture an image of a traffic sign and receive predictions. | |
| - **Model Integration** | |
| Uses `cassieli226/sign-identification-automl` trained with AutoGluon, deployed via Hugging Face Hub. | |
| - **Configurable Inference** | |
| - Resize size: adjust input resolution (64–512 px). | |
| - Top-k predictions: see multiple likely classes. | |
| - Probability threshold: filter low-confidence results. | |
| - **Interactive Interface** | |
| Built with [Gradio](https://gradio.app/) for intuitive user experience, including drag-and-drop upload and webcam capture. | |
| - **Styled Output** | |
| Results are presented with clear visuals: top prediction, confidence %, and a ranked list of alternatives. | |
| --- | |
| ## 📂 Project Structure | |
| - **`app.py`** — main application with Gradio Blocks interface. | |
| - **`autogluon_predictor_dir.zip`** — packaged AutoGluon model checkpoint (downloaded from Hugging Face Hub). | |
| - **`requirements.txt`** — dependencies for running locally or in a Hugging Face Space. | |
| - **`README.md`** — this documentation. | |
| --- | |
| ## 🚀 Running the App | |
| ### 1. Local Setup | |
| Clone the repository and install dependencies: | |
| ```bash | |
| pip install -r requirements.txt | |
| ``` | |
| ## 📊 Example Usage | |
| 1. Upload an image of a traffic sign (or use webcam capture). | |
| 2. Adjust resize size, top-k predictions, or probability threshold if desired. | |
| 3. Click **Predict**. | |
| You’ll see: | |
| - Original image preview. | |
| - Top predicted sign + confidence score. | |
| - Ranked list of additional predictions with probabilities. | |
| --- | |
| ## 📚 Citations & References | |
| - **AutoGluon**: Erickson et al., *AutoGluon: AutoML Toolkit for Deep Learning*, [GitHub](https://github.com/autogluon/autogluon). | |
| - **Gradio**: Abid et al., *Gradio: Hassle-Free Sharing and Testing of ML Models*, [gradio.app](https://gradio.app). | |
| - **Hugging Face Hub** for model hosting. | |
| --- | |
| ## 📜 License | |
| This project is distributed under the **MIT License**. See [LICENSE](LICENSE) for details. | |
| --- | |
| ## 🙌 Acknowledgments | |
| - **Model** trained by `cassieli226` and shared via Hugging Face Hub. | |
| - **App** adapted and deployed by `maryzhang`. | |
| - Special thanks to classmates and instructors for feedback. | |
| --- | |
| ## 🤖 AI Usage | |
| This project made use of **ChatGPT (OpenAI)** during development to: | |
| - Refactor and debug the Gradio Blocks interface. | |
| - Improve prediction display styling with HTML/CSS. | |
| - Draft and polish this README for clarity and completeness. | |