Update README.md

README.md

@@ -1,26 +1,15 @@
----
-title: Book Recommender 01
-emoji: 📊
-colorFrom: red
-colorTo: gray
-sdk: gradio
-sdk_version: 5.14.0
-app_file: app.py
-pinned: false
----
-
 # 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 functionality depends on the `pandas` library for data loading and manipulation.
-*   **Data Preprocessing:** The app handles missing values and removes duplicate entries. This also relies on `pandas`.
-*   **TF-IDF Vectorization:** Book summaries are converted into numerical vectors using TF-IDF. This is powered by `scikit-learn`'s `TfidfVectorizer`.
-*   **Similarity Calculation:** Cosine similarity is calculated between the TF-IDF vectors to determine how similar books are. This also uses `scikit-learn`.
-*   **Book Recommendation:** Users can enter a book title, and the app recommends similar books based on the calculated similarities.
-*   **User Interface:** The app provides a user-friendly interface using `gradio`.
+*   **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
 
@@ -32,6 +21,80 @@ This is a book recommendation app built using Python, scikit-learn, and Gradio.
 
 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>
+    ```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)