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