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title: Book Recommender 01 # Your Space's title
emoji: 📊 # An emoji for your Space (optional)
colorFrom: red # Color for the Space's theme (optional)
colorTo: gray # Color for the Space's theme (optional)
sdk: gradio # Important: Specifies the Gradio SDK
app_file: app.py # The name of your main Python file (case-sensitive!)
# sdk_version: 5.14.0 # Remove this line unless you have a VERY specific reason to pin an older version
# Book Recommender App
This is a book recommendation app built using Python, scikit-learn, and Gradio. It uses TF-IDF vectorization and cosine similarity to recommend books based on their summaries.
## Functionality
* **File Upload and Processing:** Users can upload CSV or Excel files containing book data (title and summary). This uses the `pandas` library.
* **Data Preprocessing:** Handles missing values and removes duplicates using `pandas`.
* **TF-IDF Vectorization:** Converts book summaries to numerical vectors using `scikit-learn`'s `TfidfVectorizer`.
* **Similarity Calculation:** Calculates cosine similarity between TF-IDF vectors using `scikit-learn`.
* **Book Recommendation:** Recommends similar books based on a user-provided title.
* **User Interface:** Provides an interactive interface using `gradio`.
## Dependencies
* **pandas:** For data loading, manipulation, and preprocessing (CSV and Excel file handling).
* **scikit-learn:** For TF-IDF vectorization and cosine similarity calculation.
* **gradio:** For creating the interactive user interface.
## How to Run Locally
1. **Clone the repository:**
```bash
git clone [https://github.com/](https://github.com/)<your_username>/<your_repo_name>.git # Replace with your repo URL
cd <your_repo_name>
```
2. **Create a virtual environment (recommended):**
```bash
python3 -m venv .venv # Create a virtual environment (Linux/macOS)
source .venv/bin/activate # Activate the environment (Linux/macOS)
.venv\Scripts\activate # Activate the environment (Windows)
```
3. **Install the dependencies:**
```bash
pip install -r requirements.txt
```
4. **Run the app:**
```bash
python app.py
```
5. **Open the app in your browser:** Gradio will provide a URL (usually `http://127.0.0.1:7860/`).
## How to Deploy on Hugging Face Spaces
1. **Create a Hugging Face account:** If you don't have one, sign up at [huggingface.co](https://huggingface.co).
2. **Create a new Space:**
* Click on "New Space."
* Give your Space a name.
* Select the "Gradio" SDK.
* Choose a hardware and storage upgrade if needed.
* Select a license (e.g., MIT).
* Click "Create Space."
3. **Push your code to the Space:**
* Clone the newly created Git repository:
```bash
git clone [https://huggingface.co/spaces/](https://huggingface.co/spaces/)<your_username>/<your_space_name>
cd <your_space_name>
```
* Copy all the necessary files (`app.py`, `requirements.txt`, your data file, `README.md`, `config.yaml`) into the cloned repository.
* Commit and push your changes:
```bash
git add .
git commit -m "Initial commit"
git push origin main
```
4. **Wait for the build:** Hugging Face Spaces will automatically build your app. Monitor the build progress on your Space page.
5. **Access your deployed app:** Once the build is successful, your app will be live at a URL provided by Hugging Face.
## Data File
The app requires a CSV or Excel file containing book data, including at least the `title` and `summary` columns. The file should be named (e.g., `books.csv`) and placed in the same directory as `app.py`. The path to the file is specified in the `load_data` method of the `BookRecommender` class within `app.py`.
## Example Usage
1. Upload your book data file.
2. Enter the title of a book you like.
3. The app will recommend similar books.
## Contributing
Contributions are welcome! Please open an issue or submit a pull request.
## License
[MIT License](LICENSE) (Or your chosen license) |