README.md

metadata

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.

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✨ 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 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.

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📂 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.

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🚀 Running the App

1. Local Setup

Clone the repository and install dependencies:

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.

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📚 Citations & References

• AutoGluon: Erickson et al., AutoGluon: AutoML Toolkit for Deep Learning, GitHub.
• Gradio: Abid et al., Gradio: Hassle-Free Sharing and Testing of ML Models, gradio.app.
• Hugging Face Hub for model hosting.

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📜 License

This project is distributed under the MIT License. See LICENSE for details.

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🙌 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.

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🤖 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.