Update README.md

README.md

@@ -11,4 +11,42 @@ license: mit
 short_description: Stop Sign Image Identification
 ---
 
+# Stop Sign Image Classifier  
+
+**Author:** Your Name  
+**Course:** 24679 - Designing and Deploying AI/ML Systems  
+
+This app classifies traffic images into two categories using an AutoGluon-trained model:  
+- **0 = Not a Stop Sign**  
+- **1 = Stop Sign**  
+
+The interface allows you to upload or drag-and-drop an image of a road scene. The model outputs the predicted class along with probability scores.  
+
+---
+
+## How to Use  
+1. Upload an image (JPG/PNG).  
+2. Click **Submit** to run the classifier.  
+3. View the predicted label (`0` or `1`) and the probability distribution.  
+
+---
+
+## Deployment Details  
+- **Frameworks:** [AutoGluon Image](https://auto.gluon.ai/stable/tutorials/image_prediction/index.html), [Gradio](https://gradio.app/)  
+- **Hosting:** Hugging Face Spaces  
+- **Model Loading:** Model is downloaded from the Hugging Face Hub and automatically unpacked on startup.  
+
+---
+
+## Requirements  
+Dependencies are listed in `requirements.txt`.  
+
+---
+
+## Acknowledgments  
+- Model trained by a classmate in Homework 2  
+- Deployment scaffold and documentation supported with AI assistance (ChatGPT, OpenAI)  
+- Reference: Class-provided notebook *image gradio.ipynb*  
+
+
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference