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It is built using Python, Flask, and scikit-learn, and can be easily customized to suit your needs.\n\n## Features\n\nQandAI has the following features:\n\n- Simple and intuitive interface for asking questions\n- Uses a Multinomial Naive Bayes model to predict the most likely answer to a given question\n- Can be easily trained on new data to improve its accuracy\n- Returns the top 3 most likely answers, ranked by probability\n- API endpoint for programmatic access to the chatbot\n- Includes sample data for testing and training\n\n## Getting Started\n\n### Requirements\n\nTo use QandAI, you will need:\n\n- Python 3.x\n- Flask\n- scikit-learn\n- pandas\n- joblib\n- pipenv\n\n### Installation\n\nTo install QandAI, follow these steps:\n\n1. Clone the repository from GitHub: `git clone https://github.com/BlackIQ/QandAI.git`.\n2. Navigate to the project directory: `cd QandAI`.\n3. Install the required dependencies: `pipenv install`.\n\n### Usage\n\nTo use QandAI, follow these steps:\n\n1. Copy `.env.example` to `.env` and change `API_PORT` to your custome port.\n2. Train the model on your own data or use the included sample data: `python3 app/core/core.py`\n3. Start the Flask server: `python3 wsgi.py`\n4. Send a POST request to the `/api/predict` endpoint with a JSON payload containing a `question` key and the user's question as the value.\n5. The server will return a JSON response containing the top 3 most likely answers, ranked by probability.\n\n### Customization\n\nTo customize QandAI, you can modify the following:\n\n- `data/faq_data.json`: Add or remove questions and answers to train the model on your own data\n- `app/core/core.py`: Modify the code for preprocessing and vectorizing the input data\n- `models/faq_model.joblib`: Train and save a new model with different hyperparameters or a different algorithm\n\n## License\n\nThis project is licensed under the MIT License - see the LICENSE file for details.\n\n## Acknowledgments\n\nQandAI was inspired by the many open-source chatbot projects available online. Thank you to the developers and contributors of scikit-learn, Flask, and joblib for their excellent libraries.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fblackiq%2Fqandai","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fblackiq%2Fqandai","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fblackiq%2Fqandai/lists"}