README

AI Model Studio

AI Model Studio is a cross-platform FireMonkey (FMX) application (Windows, Linux, macOS) that provides a seamless interface to run AI models locally—via Docker images from Replicate.com—or remotely using Replicate’s cloud API. Whether you’re generating text, images, video, or audio, AI Model Studio gives you a single, straightforward app to experiment with a wide variety of models.

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Cross-Platform FireMonkey

• Written in Delphi FMX for a native look-and-feel on Windows, Linux, and macOS.
• Unified code base that compiles to each platform with minimal changes.
• Leverages Embarcadero’s FireMonkey framework for rich GUI controls, media handling, and cross-platform support.

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Architecture Overview

1. Model Orchestration

1. Model Selection
   • AI Model Studio categorizes AI models (text, image, video, audio, etc.) published on Replicate.com.
   • Users can pick a specific model or category from the UI.
2. Toggle: Local vs. Remote
   • Local:
     • The app pulls a Docker image (if not already present) corresponding to the selected AI model from Replicate.
     • It then runs the container, which exposes a REST API on the local machine.
     • AI Model Studio sends inference requests to http://localhost:<port> (or similar) where the container listens.
   • Remote:
     • The app uses Replicate’s standard API endpoints, requiring a valid API key.
     • This allows quick testing or usage without needing Docker installed locally (beyond the optional local usage).

2. Docker Integration

• Docker Container Pull & Run
  • On Windows, Docker for Windows needs to be installed and running in the background.
  • On Linux and macOS, standard Docker Engine (via CLI) suffices.
  • AI Model Studio programmatically checks for the needed Docker image, pulls it if missing, and starts it.
• Container Lifecycle Management
  • The application can stop and remove containers when switching models or when the user explicitly ends the session.
  • Logging and status notifications keep the user informed of Docker commands, pull progress, and runtime events.

3. REST Communication

1. Local Inference (Docker)
   • Once the container is active, it provides an API that mirrors Replicate’s inference endpoints.
   • Delphi’s built-in REST client libraries handle the HTTP POST requests, sending the prompt/parameters and retrieving outputs.
2. Remote Inference (Replicate.com)
   • For remote usage, the application sets the base endpoint to Replicate’s servers and includes the user’s API key.
   • The same request/response structure is used, but no local Docker container is involved.

4. Core Application Components

• Docker Command Execution:
  • Often done via simple command-line calls (e.g., docker pull, docker run), which the app executes in the background.
  • On Windows, these are typically ShellExecute or command prompt calls wrapped in Delphi code.
  • On macOS and Linux, the commands are similar, but the path or environment might differ slightly.
• REST Client & Response Handling:
  • A Delphi REST client component (or direct HTTP calls) communicates with either localhost or Replicate’s URL.
  • Handles JSON payloads for prompts, as well as media files if the model requires images/video.

5. Output Visualization

• Text Generation
  • Results appear in a memo or large edit control inside the FMX form.
• Image / Video Frames
  • Images are displayed in TImage components, with the app downloading and converting them to a compatible FMX bitmap.
• Audio
  • If the model returns audio data, it is stored locally, and the UI can provide playback options or a file link.

6. Extensibility

• New Models
  • By defining the Docker image name, model parameters, and input prompts, additional Replicate-based models can be integrated with minimal code changes.
• Additional Features
  • Since the application’s structure is modular, developers can add more advanced configurations, logging, or specialized controls for custom models.

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License

This project is released under the MIT License (or whichever license is defined in this repository). Please check the LICENSE file for details.

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Contributing

We welcome contributions of all kinds—whether it’s adding support for new models, improving Docker orchestration, or enhancing the user interface.

1. Fork the repository
2. Create a feature branch (git checkout -b feature/my-new-feature)
3. Commit your changes (git commit -am 'Add a new feature')
4. Push to the branch (git push origin feature/my-new-feature)
5. Create a new Pull Request

For major changes, please open an issue first to discuss what you would like to change.

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Acknowledgments

• Delphi & FireMonkey: For a unified cross-platform GUI framework.
• Replicate: For hosting and distributing AI models via Docker images and providing a robust cloud inference API.

Enjoy exploring local and remote AI models with AI Model Studio!