README.md

metadata

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) 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
• Framework: AutoGluon Tabular
• 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.
• 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