Update README.md

README.md

@@ -1,6 +1,5 @@
 ---
 title: Book Genre Predictor
-emoji: 😻
 colorFrom: indigo
 colorTo: red
 sdk: gradio
@@ -8,6 +7,66 @@ sdk_version: 5.47.1
 app_file: app.py
 pinned: false
 license: mit
+---
+# Book Genre Predictor
+
+This Space hosts a **Gradio app** that predicts the **numeric genre code of a book** based on its **physical dimensions and page count**.  
+It was built using a **tabular AutoGluon model** and deployed on Hugging Face Spaces.
+
+---
+
+## Dataset & Model
+
+- **Source Model Repo:** [FaiyazAzam/24679-tabular-autolguon-predictor](https://huggingface.co/FaiyazAzam/24679-tabular-autolguon-predictor)  
+- **Task:** Predict `Genre` of a book given its physical features.  
+- **Features Used:**  
+  - `Height` (cm)  
+  - `Width` (cm)  
+  - `Depth` (cm, spine thickness)  
+  - `Page Count` (integer)  
+
+The model was trained using [AutoGluon Tabular](https://auto.gluon.ai/stable/index.html).  
+Prediction outputs are **numeric labels** (e.g., 0, 1, 2) that correspond to genres in the training data.
+
+---
+
+## App Instructions
+
+1. Enter values for **Height, Width, Depth, Page Count**.  
+2. Click **Predict** to see the model’s prediction.  
+3. Use one of the **example inputs** to quickly test the app.  
+
+✔️ Input validation ensures all values must be **positive numbers**.  
+
+---
+
+## Example Inputs
+
+| Height (cm) | Width (cm) | Depth (cm) | Page Count | Predicted Genre (code) |
+|-------------|------------|------------|------------|-------------------------|
+| 20.1        | 13.5       | 1.8        | 250        | e.g. 1                 |
+| 24.0        | 15.0       | 2.2        | 320        | e.g. 0                 |
+| 18.5        | 12.0       | 1.5        | 180        | e.g. 2                 |
+
+---
+
+## Technical Notes
+
+- **Framework:** [Gradio](https://www.gradio.app/) interface.  
+- **Backend:** AutoGluon `TabularPredictor`.  
+- **Deployment:** Hugging Face Spaces (`sdk: gradio`).  
+- **Known Limitation:** Output is a **numeric genre code**, since the training dataset only contained encoded labels.  
+
+---
+
+## How This Fits the Assignment
+
+- ✅ Uses a **classmate’s tabular model** (not my own).  
+- ✅ Researched and linked the **dataset/model docs**.  
+- ✅ Built a Gradio app with **widgets + examples**.  
+- ✅ Exposed inputs with validation and presented predictions clearly.  
+- ✅ Deployed publicly on Hugging Face Spaces with proper documentation.  
+
 ---
 
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference