Introduction
The UC Intel Final dashboard is a professional multi-page ML application built with Streamlit for malware image classification using PyTorch. It provides an end-to-end workflow from dataset configuration to model training and evaluation.Dashboard Architecture
The dashboard follows a self-contained page architecture with the following principles:- Self-Contained Pages: Each feature is isolated in its own folder under
app/content/ - Tab-Based Organization: Complex pages split content into multiple tab files
- State Management: Centralized state access through
state/module functions - Flat Components: Shared UI components in
components/directory
Navigation Structure
The dashboard is organized into two main sections:Workflow
The core ML pipeline follows this sequence:
- Dataset Configuration: Configure data splits and augmentation
- Model Builder: Design CNN, Transformer, or Transfer Learning architectures
- Training Configuration: Set hyperparameters and training options
- Monitor Training: Start training and watch real-time progress
- Results & Evaluation: View metrics, confusion matrix, and training curves
- Interpretability: Visualize model attention with Grad-CAM and embeddings
Key Features
Core Infrastructure
- Self-contained page architecture with absolute imports
- Session management with persistence across page reloads
- Theme customization with color presets
- GPU detection and memory monitoring
- Configuration status indicators in sidebar
ML Workflow
- Dataset Module: Automated scanning, train/val/test splits, class selection, augmentation
- Model Module: Custom CNN builder, Transformer architectures, Transfer Learning
- Training Module: Optimizer configuration, learning rate schedules, callbacks
- Monitoring: Live training updates with real-time metrics
- Evaluation: Comprehensive metrics, confusion matrices, per-class analysis
- Interpretability: Grad-CAM, t-SNE embeddings, misclassification analysis
Workflow Diagram
The dashboard follows this logical flow:Getting Started
Create a New Session
Click New Session in the header to start fresh, or use Past Sessions to load saved work
Page URLs
Each page has a dedicated URL path:/home- Home & Session Setup/dataset- Dataset Configuration (4 tabs)/model- Model Builder/training- Training Configuration/monitor- Training Monitor/results- Results & Evaluation/interpretability- Model Interpretability
Sidebar Status Indicators
The sidebar shows real-time configuration status:- ✅ Dataset configured: Train/val/test split and augmentation saved
- ✅ Model configured: At least one model saved to library
- ✅ Training configured: Training hyperparameters saved
Status indicators update automatically as you complete each configuration step.
Theme Customization
Access theme settings in the sidebar:- Color Pickers: Primary, Secondary, Background colors
- Presets: Soft Green (default), Soft Blue, Soft Pink, Soft Orange
- Theme colors persist across sessions via CSS injection
Session Management
All configuration is automatically saved to session state:- Auto-save: Configuration persists during navigation
- Session History: Load previous sessions from header dropdown
- Export: Download training history and model checkpoints
Use “New Session” to start fresh with default settings. Previous sessions remain available in the dropdown.
Next Steps
Explore the detailed guides for each dashboard page:Dataset Configuration
Learn how to configure your malware dataset
Model Builder
Design custom CNN, Transformer, or Transfer Learning models
Training Configuration
Set up optimizers, learning rates, and callbacks
Training Monitor
Watch real-time training progress