ClassifyAI

ClassifyAI is a Python project that uses the OpenAI API to classify data with personalized classification models. It has a REST API that allows users to send requests for classifying information and receive answers from the GPT model. The API returns responses in the format of the specified model, making it easy for applications to work with the results. The project requires Python 3.7 or higher, Flask, the OpenAI Python library, python-dotenv, and is currently at version 0.6.0. It includes features such as creating, editing, and deleting models over a Python API, new endpoints for listing, retrieving, creating, changing, and deleting models, and a management website to create, edit, and delete models. The project is licensed under the MIT License and uses the OpenAI GPT models for data classification.

⚡Top 5 ClassifyAI Features:

1. Python Project: ClassifyAI is a Python project that uses the OpenAI API to sort data with personalized classification models.
2. REST API: It has a REST API that allows users to send requests for classifying information and receive answers from the GPT model.
3. Model Structure Response: The API sends back responses in the format of the specified model, making it easy for applications to work with the results.
4. Versions: The project has undergone several versions, each with new features and improvements, such as the ability to define and run tests for models, new default models, and new endpoints for managing models.
5. Documentation: The project includes documentation on .

⚡Top 5 ClassifyAI Use Cases:

1. Classification of Data: ClassifyAI can be used to classify various types of data, such as text, images, or audio, using personalized classification models.
2. Integration with Applications: The API’s response format makes it easy for applications to work with the results, allowing for seamless integration.
3. Testing Models: Users can define and run tests for their models within the project, ensuring the accuracy and reliability of the classification results.
4. Management of Models: The project includes new endpoints for creating, editing, and deleting models over the Python API, providing more control over the classification process.
5. Documentation and Support: The project includes documentation on , as well as the ability to open issues for support and contributions.