Spaces:
No application file
No application file
| title: Book Recommender | |
| emoji: ๐ | |
| colorFrom: indigo | |
| colorTo: blue | |
| sdk: docker | |
| sdk_version: "latest" # Or specify a version if needed | |
| app_file: app.py | |
| pinned: false | |
| # Book Recommender (Flask) | |
| This Hugging Face Space hosts a web application for recommending books based on their summaries. It's built using Python, Flask, pandas, and scikit-learn. | |
| ## How to Use | |
| 1. **Upload Data:** Upload a CSV or Excel file containing book titles and summaries. The file *must* have columns named "title" and "summary" (case-sensitive). | |
| 2. **Process Data:** Click the "Upload and Process" button. | |
| 3. **Get Recommendations:** Enter a book title and click the "Get Recommendations" button. The app will display similar books. | |
| ## Data Format | |
| The uploaded CSV or Excel file should have the following columns: | |
| * `title` (string): The title of the book. | |
| * `summary` (string): A brief summary of the book. | |
| ## Dependencies | |
| The following Python libraries are used: | |
| * Flask | |
| * pandas | |
| * scikit-learn | |
| * gunicorn | |
| These dependencies are listed in the `requirements.txt` file. | |
| ## Running Locally (for development) | |
| 1. Clone the repository: | |
| ```bash | |
| git clone <repository_url> | |
| ``` | |
| 2. Create a virtual environment: | |
| ```bash | |
| python3 -m venv venv | |
| source venv/bin/activate # macOS/Linux | |
| venv\Scripts\activate # Windows | |
| ``` | |
| 3. Install dependencies: | |
| ```bash | |
| pip install -r requirements.txt | |
| ``` | |
| 4. Run the app: | |
| ```bash | |
| python app.py | |
| ``` | |
| ## File Structure |