Drag-and-Drop Machine Learning Demo App Development

Demo App Description: Drag-and-Drop Machine Learning Pipeline Builder The goal is to create a visually elegant and intuitive demo app that allows users to design machine learning pipelines on a canvas. The app will support drag-and-drop functionality, enabling users to connect modules seamlessly by linking dots between them. The interface will feature Apple-style UI aesthetics, prioritizing minimalism, elegance, and user-friendly interactivity. The app will be compatible with both iOS and web [login to view URL], I want to create a demo for a generalized MlOps platform that allows the user to drag modules for Machine learning around a canvas and connect the dots between modules to create a machine learning pipeline. it need to embed a few pretrained ml/dl models for visual, sound clone, and data analysis it doesn't need to actually train the model based on teh data provided but it needs to be able to showcase the predicted results and be able to use the pretrained models and real-time video/sound/digital data to demonstrate the prediction results something like the [login to view URL] except for its not for creating llm pipeline, but a more generalized ml pipeline. you can actually build it based on the [login to view URL] project if its more convenient for you Core Features: Canvas Interface: Users can drag and arrange five key modules on a canvas: [Dataset] [Features] [Labeling] [Algorithms] [Predictions] Modules can be connected by drawing lines between "input" and "output" dots. Modular Options: Each module will offer three selectable options: Dataset: Predefined datasets (e.g., CSV, JSON, SQL-based datasets). Features: Feature extraction techniques (e.g., scaling, PCA, custom). Labeling: Labeling strategies (e.g., manual, automated, pre-labeled). Algorithms: Machine learning models (e.g., Random Forest, SVM, Neural Network). Predictions: Visualization or evaluation methods (e.g., confusion matrix, ROC curve, scatterplot). Interactivity and Feedback: Real-time validation of connections to ensure pipeline logic is valid. Clear visual indicators for active, inactive, and error states of modules. Dragging and resizing capabilities for components to create custom layouts. Apple-like UI: Modern, sleek design with smooth transitions and animations. Rounded edges, gradient accents, and polished typography. Intuitive touch and mouse interactivity, adhering to Apple's Human Interface Guidelines. Cross-Platform Compatibility: Fully functional on both iOS (via SwiftUI or React Native) and web browsers (via React.js with TailwindCSS or similar frameworks). Responsive design for various screen sizes. Additional Considerations: Provide pre-built templates to help users quickly start building pipelines. Include a "preview" mode to simulate pipeline execution.