---
title: Book Genre Predictor
colorFrom: indigo
colorTo: red
sdk: gradio
sdk_version: 5.47.1
app_file: app.py
pinned: false
license: mit
---
# Book Genre Predictor

This Hugging Face Space hosts a **Gradio app** that predicts the **genre of a book** based on its **physical dimensions and page count**.  
It uses a **AutoGluon Tabular model** trained during last session.  

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## Dataset & Model Card

- **Dataset:** Book metadata dataset (features: `Height`, `Width`, `Depth`, `Page Count`; label: `Genre`).
- **Dataset Information:** This app uses the [Books-tabular-dataset (its-zion-18)](https://huggingface.co/datasets/its-zion-18/Books-tabular-dataset) The dataset is licensed under **MIT** and consists of **~330 records** in Parquet format (split into `original` and `augmented`).
- **Model Repo:** [FaiyazAzam/24679-tabular-autolguon-predictor](https://huggingface.co/FaiyazAzam/24679-tabular-autolguon-predictor)  
- **Framework:** [AutoGluon Tabular](https://auto.gluon.ai/stable/index.html)  
- **Task:** Multi class classification -> predict `Genre` (numeric code).  

### Input Features
| Feature      | Type    | Unit / Description                 |
|--------------|---------|-------------------------------------|
| Height       | float   | cm – height of the book             |
| Width        | float   | cm – width of the book              |
| Depth        | float   | cm – spine thickness                |
| Page Count   | integer | number of pages                     |

### Label
- `Genre` → encoded as **numeric codes** (e.g. 0, 1, 2, …).  
- Mapping to actual names was not provided in the original dataset. 

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## App Interface

- **Widgets:** Numeric input boxes for each feature.  
- **Output:** Numeric code prediction (e.g. `"Predicted Genre: 1"`).  
- **Examples:** 3 preloaded examples for quick testing.  
- **Validation:** Ensures all inputs are positive.  

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## 🔍 Example Usage

| Height (cm) | Width (cm) | Depth (cm) | Page Count |
|-------------|------------|------------|------------|
| 20.1        | 13.5       | 1.8        | 250        |
| 24.0        | 15.0       | 2.2        | 320        |
| 18.5        | 12.0       | 1.5        | 180        |

Note: The model often defaults to predicting a single genre (e.g. code 0).  
This reflects dataset/model limitations, not the app itself.

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## Technical Details

- **Backend:** AutoGluon `TabularPredictor` loaded from a zipped artifact.  
- **Interface:** [Gradio](https://www.gradio.app/).  
- **Deployment:** Hugging Face Spaces (`sdk: gradio`).  
- **Environment:** Python 3.10, pinned requirements.  

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## Limitations

- **Numeric labels only:** Original training dataset did not include human readable genre names.  
- **Collapsed predictions:** Model tends to overpredict the majority class (`0`).  
- **Generalization:** Accuracy on unseen books is uncertain due to limited feature set.  

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## Future Improvements

- Map numeric codes to the actual genre categories from the dataset.  
- Retrain model with balanced classes.  
- Provide confidence scores along with predictions.  
- Explore richer book features (author, publisher, language).  

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## AI Disclosure

Parts of this project were supported with the help of AI tools (GPT-5), mainly for:  
- Debugging deployment issues on Hugging Face Spaces  
- Improving the stability of the Gradio interface  
- Polishing documentation  

The dataset, model training, and integration choices remain based on classmate provided artifacts and my own implementation work.

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Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference