README.md

---
title: Book Analytics Space
emoji: 📚
colorFrom: blue
colorTo: gray
sdk: gradio
sdk_version: 5.22.0
app_file: app.py
pinned: false
---

# Book Analytics Space

This Hugging Face Space runs a Jupyter notebook on two CSV datasets and then exposes the outputs in a Gradio interface.

## Files included

- `app.py` — the Gradio application
- `style.css` — a clean style file with no background images
- `requirements.txt` — Python dependencies
- `2a_Python_Analysis_Constance_Gonnelle.ipynb` — the analysis notebook
- `books_data.csv` — sales dataset
- `synthetic_book_reviews.csv` — reviews dataset

## How it works

1. The app copies `books_data.csv` to `synthetic_sales_data.csv` because the notebook expects that filename.
2. The app executes the notebook with `papermill`.
3. The notebook writes figures and tables to `artifacts/py/figures` and `artifacts/py/tables`.
4. The Dashboard tab reads those outputs and displays KPIs, charts, images, and tables.

## Expected notebook exports

The current app is ready for these notebook outputs:

- `artifacts/py/figures/sales_trends_sampled_titles.png`
- `artifacts/py/figures/sentiment_distribution_sampled_titles.png`
- `artifacts/py/figures/arima_forecasts_sampled_titles.png`
- `artifacts/py/figures/kpis.json`
- `artifacts/py/tables/df_dashboard.csv`
- `artifacts/py/tables/sentiment_counts_sampled.csv`
- `artifacts/py/tables/top_titles_by_units_sold.csv`

## Deploy on Hugging Face Spaces

1. Create a new **Gradio** Space.
2. Upload all files from this package.
3. Wait for the build to finish.
4. Open the Space and click **Run notebook**.
5. Open **Dashboard** to inspect the generated outputs.

## Notes

- The notebook contains a `!pip install ...` cell. It is better to keep dependencies in `requirements.txt`, but the app can still run with the current notebook.
- If you change the notebook filename, update the `NB_ANALYSIS` environment variable or rename the file in the repository.