Update README.md

README.md

@@ -11,33 +11,91 @@ license: mit
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 # Stop Sign Classifier
 
-This is a simple Gradio app that wraps an **AutoGluon MultiModalPredictor** trained on images of stop signs.  
-Upload a traffic sign and the model will predict whether it contains a **Stop Sign**.
-
-## Features
-- Upload your own image (PNG/JPG) or use your webcam.
-- Get predictions with **probabilities** (Stop Sign vs No Stop Sign).
-- Preview **example images** for quick testing.
-- Built with [AutoGluon](https://auto.gluon.ai/stable/index.html) + [Gradio](https://gradio.app/).
-
-## How It Works
-- The trained model is downloaded directly from Hugging Face Hub (`scottymcgee/image-classifier`).
-- Images are preprocessed (resized to 256x256) and passed into the model.
-- Probabilities are returned and displayed in a simple UI.
-
-## Example Inputs
-- Clear stop sign picture
-- Street scene without stop sign
-- Mixed images
-
-## Files in this Space
-- `app.py`: Gradio interface.
-- `requirements.txt`: Dependencies.
-- `README.md`: This documentation.
-
-## Notes
-- This app is for **educational/demo** purposes.
-- Model was trained by a classmate as part of Homework 2 (CMU 24679).
-- Deployment exercise for **Driving an Image Network Model with Gradio** assignment.
+This Hugging Face Space hosts a **Gradio app** that predicts whether an uploaded traffic image contains a **Stop Sign**.  
+It uses a **classmate’s AutoGluon MultiModal model** trained during Homework 2.  
+
+---
+
+## Dataset & Model Card
+
+- **Dataset:** Traffic sign images (binary classification: Stop Sign vs. No Stop Sign).  
+- **Model Repo:** [scottymcgee/image-classifier](https://huggingface.co/scottymcgee/image-classifier)  
+- **Framework:** [AutoGluon MultiModal](https://auto.gluon.ai/stable/index.html)  
+- **Task:** Binary classification → predict `Stop Sign` or `No Stop Sign`.  
+
+### Input Features
+| Feature | Type           | Description               |
+|---------|----------------|---------------------------|
+| Image   | JPG/PNG/Webcam | Traffic scene input image |
+
+### Label
+- `0 → 🚫 No Stop Sign`  
+- `1 → 🛑 Stop Sign`  
+
+---
+
+## App Interface
+
+- **Widgets:**  
+  - `Image Upload` (supports drag and drop or webcam).  
+  - `Confidence Threshold` slider (filter low confidence results).  
+
+- **Output:**  
+  - Original uploaded image.  
+  - Preprocessed 256×256 version (what the model actually sees).  
+  - Human readable prediction with probabilities.  
+
+- **Examples:** 3 preloaded example images for quick testing.  
+- **Validation:** Ensures uploaded files are valid images and not oversized.  
+
+---
+
+## Example Usage
+
+| Example Image     | Predicted Class |
+|-------------------|-----------------|
+| `stop1.jpg`       | 🛑 Stop Sign    | 
+| `no_stop1.jpg`    | 🚫 No Stop Sign |
+| `stop2.jpg`       | 🛑 Stop Sign    |
+
+---
+
+## Technical Details
+
+- **Backend:** AutoGluon `MultiModalPredictor` loaded from classmate’s Hugging Face repo.  
+- **Interface:** [Gradio](https://www.gradio.app/).  
+- **Deployment:** Hugging Face Spaces (`sdk: gradio`).  
+- **Environment:** Python 3.10, pinned requirements.  
+
+---
+
+## Limitations
+
+- **Binary labels only:** App only distinguishes `Stop Sign` vs. `No Stop Sign`.  
+- **Dataset limitations:** Accuracy depends heavily on dataset quality (lighting, occlusion, unusual traffic scenes).  
+- **Threshold behavior:** High thresholds may filter out useful predictions.  
+
+---
+
+## Future Improvements
+
+- Expand dataset to include more traffic sign types (yield, speed limit, etc.).  
+- Add bounding box detection to highlight the Stop Sign in the image.  
+- Display top-k predictions dynamically instead of threshold filtering.  
+- Improve UI with tooltips and confidence visualization (bar chart).  
+
+---
+
+## AI Disclosure
+
+This app was developed with support from AI assistance in:
+
+- Adding input validation and a configurable threshold slider
+- Streamlining the Gradio interface for better usability
+- Drafting the initial version of this README
+
+All core modeling and training artifacts come from a **classmate’s AutoGluon model**.  
+
+---
 
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference