# Masked Word Prediction with Flask This project is a web application built with Flask that predicts masked words in sentences using a pre-trained BERT model (`bert-base-multilingual-cased`). The app allows users to input sentences with masked tokens (`[MASK]`) and receive top predictions for those masked tokens. ## Demo  <!-- Add a path to an image that shows your app in action --> ## Features - Predict masked words in sentences using BERT. - Simple and intuitive web interface. - Supports multilingual text input. ## Technologies Used - **Flask**: A lightweight WSGI web application framework. - **Transformers**: Library from Hugging Face used for NLP models. - **PyTorch**: Deep learning framework used to run the BERT model. - **HTML/CSS**: For the frontend of the application. ## Getting Started Follow the instructions below to set up and run the project locally. ### Prerequisites - Python 3.7 or above - Git ### Installation 1. Clone the repository: ``` git clone https://github.com/your-username/story-generation-app.git cd story-generation-app ``` 2. Create and activate a virtual environment: ``` python -m venv venv source venv/bin/activate # On Windows use `venv\Scripts\activate` ``` 3. Install the required packages: ``` pip install -r requirements.txt ``` **Note:** If `requirements.txt` is not available, manually install the dependencies: ``` pip install flask transformers torch ``` ### Running the Application 1. Start the Flask application: ``` python app.py ``` 2. Open your browser and go to: ``` http://127.0.0.1:5000 ``` ### Usage 1. Enter a sentence with a `[MASK]` token where you want predictions. 2. Click the "Predict" button. 3. The app will display the top predicted tokens for the masked position. ## Project Structure ``` story-generation-app/ │ ├── app.py # Main Flask application file ├── requirements.txt # Dependencies for the project ├── templates/ │ └── index.html # HTML file for the app's UI └── static/ └── styles.css # CSS file for styling ``` ## Screenshots  <!-- Update this with actual paths to your app's screenshots --> ## Contributing Contributions are welcome! Please follow these steps: 1. Fork the repository. 2. Create a new branch (`git checkout -b feature-branch`). 3. Commit your changes (`git commit -m 'Add some feature'`). 4. Push to the branch (`git push origin feature-branch`). 5. Open a pull request. ## License This project is licensed under the MIT License - see the LICENSE file for details. ## Acknowledgements - Hugging Face Transformers for providing pre-trained models. - Flask documentation for guidance on building web applications.