Update README.md

README.md

@@ -10,3 +10,90 @@ 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.  
+