# How Reeler Works: Inside the Engine

At the core of Reeler AI is the **Django** framework, providing a solid foundation for backend development. With its robust features for handling user requests, managing databases, and ensuring security, Django seamlessly connects the frontend and backend components through RESTful API services.

**Python** takes the lead in driving Reeler AI's backend operations. Known for its simplicity and extensive library support, Python efficiently processes textual inputs, manages media tasks, and integrates with various APIs, ensuring a smooth and reliable system.

On the frontend, JavaScript, powered by the **React library**, enhances user interaction with Reeler AI. **React's** component-based architecture and intuitive syntax create dynamic, responsive user interfaces, making the user experience enjoyable and engaging.

**Structured Query Language (SQL)** manages the database within **Django**, providing reliability and scalability for storing user inputs, generated scripts, and video metadata, ensuring data integrity throughout the system.

Supporting Reeler AI's functionality are key technologies like the **Django REST Framework**, **OpenAI's GPT** which was trained to be fine tuned for script generation, and the Pexels API for sourcing visual content. Additionally, APIs like ElevenLabs and Amazon Polly add audio narration to generated scripts, enriching the video creation process with human-like voices and expressions.

For video processing tasks, Reeler AI utilizes **C++** and **FFmpeg**, alongside libraries like **OpenCV** and Python Imaging Library (PIL) for text overlay and image processing. These technologies work together to maintain visual consistency and quality, ensuring that every video created with Reeler AI is polished and professional.

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# Agent Instructions: Querying This Documentation

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Perform an HTTP GET request on the current page URL with the `ask` query parameter:

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```

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