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The chatbot responds to customer queries regarding various aspects of the restaurant, such as the name of the chatbot, the name of the restaurant, timings, menu, accessibility, parking, items, payment modes, and more.\n\n## ScreenShot\n![Screenshot 2024-07-28 232148](https://github.com/user-attachments/assets/be55f936-1fa4-4bc3-a691-5d612f3753cc)\n\n\n\n## Project Directory Structure\n\n```\n.\n├── dataset\n│   ├── chatbot-data.json\n│   └── words.py\n├── Model\n│   └── model.pth\n├── app.py\n├── predictions.py\n├── static\n│   └── css\n├── templates\n│   └── chat.html\n```\n\n### Description of Files and Directories\n\n- **dataset/**\n  - `chatbot-data.json`: Contains the dataset used for training the chatbot.\n  - `words.py`: Contains utility functions for processing text data.\n\n- **Model/**\n  - `model.pth`: The trained model for the chatbot.\n\n- **app.py**: The main application file for running the Flask web server.\n\n- **predictions.py**: Contains the code for making predictions using the trained model.\n\n- **static/css**: Directory for static CSS files.\n\n- **templates/chat.html**: HTML template for the chatbot user interface.\n\n## Setup and Installation\n\n### Prerequisites\n\n- Python 3.x\n- Flask\n- PyTorch\n- NLTK\n\n### Installation Steps\n\n1. **Clone the Repository**\n\n   ```bash\n   git clone https://github.com/harshit433/NLP-Chatbot-Cantilever.git\n   cd NLP-Chatbot-Cantilever\n   ```\n\n2. **Install Dependencies**\n\n   Create a virtual environment and install the required packages:\n\n   ```bash\n   python -m venv venv\n   source venv/bin/activate  # On Windows, use `venv\\Scripts\\activate`\n   pip install -r requirements.txt\n   ```\n\n3. **Download NLTK Data**\n\n   If not already downloaded, install necessary NLTK data:\n\n   ```python\n   import nltk\n   nltk.download('punkt')\n   nltk.download('wordnet')\n   ```\n\n4. **Run the Application**\n\n   Start the Flask application:\n\n   ```bash\n   python app.py\n   ```\n\n5. **Access the Chatbot**\n\n   Open your web browser and navigate to `http://127.0.0.1:5000` to interact with ChefAssist.\n\n## How It Works\n\n1. **Data Processing**: The `words.py` script processes the text data from `chatbot-data.json`.\n2. **Model Training**: The chatbot model is trained using the data and stored in `model.pth`.\n3. **Prediction**: The `predictions.py` script uses the trained model to make predictions based on user queries.\n4. **Flask Application**: The `app.py` file runs a Flask web server that serves the chatbot interface and handles user interactions.\n\n## Future Enhancements\n\n- Improve the chatbot's response accuracy.\n- Add more features to handle a wider range of customer queries.\n- Integrate with a live restaurant database for real-time updates.\n\n## Contributing\n\nIf you would like to contribute to this project, please fork the repository and submit a pull request. 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