SmartPredict
  • Documentation
  • OVERVIEW
    • Presentation
      • Key features
      • Who may benefit from its use
      • The SmartPredict Graphical User Interface (GUI)
        • The SmartPredict Modules
        • Focus on the Notebooks
        • Practical shortcuts
    • Prerequisites
  • Getting started
    • Getting started (Part1) : Iris classification project
      • Project description
      • Step 1. Create the project
      • Step 2. Upload the dataset
      • Step 3. Preprocess the dataset
      • Step 4. Build the flowchart
        • Set up the flowchart
        • Configure the modules
      • Step 5. Run the build
      • Step 6. Deploy the project
      • Step 7. Make inferences with our model
      • Conclusion
  • Getting started (Part 2): Predicting the passengers' survival in the Titanic shipwreck
    • Project description
    • Step 1. Create the project
    • Step 2. Upload the dataset
    • Step 3. Preprocess the dataset
    • Step 4. Build the flowchart
    • Step 5. Run the build
    • Step 6. Deploy the pipeline
    • Step 7. Make inferences with our pipeline
  • MODULE REFERENCE
    • CORE MODULES
      • Introduction
      • Basic Operations
        • Item Saver
      • Web Services
        • Web Service IN and OUT
      • Data retrieval
        • Data fetcher
        • Data frame loader/converter
        • Image data loader
      • Data preprocessing
        • Introduction
        • Array Reshaper
        • Generic Data Preprocessor
        • Missing data handler
        • Normalizer
        • One Hot Encoder
        • Ordinal Encoder
      • Data selection
        • Features selector
        • Generic data splitter
        • Labeled data splitter
      • Training and Prediction
        • Predictor DL models
        • Predictor ML models
        • Predictor ML models (Probabilistic models)
        • Trainer ML models
        • Trainer/Evaluator DL models
      • Evaluation and fine-tuning
        • Cross Validator for ML
        • Evaluator for ML models
      • Machine Learning algorithms
        • ML modules in SmartPredict
        • Decision Tree Regressor
        • KNeighbors Classifier
        • KNeighbors Regressors
        • Linear Regressor
        • Logistic Regressor
        • MLP Regressor
        • Naive Bayes Classifier
        • Random Forest Classifier
        • Random Forest Regressor
        • Support Vector Classifier
        • Support Vector Regressor
        • XGBoost Classifier
        • XGBoost Regressor
      • Deep learning algorithms
        • Dense Neural Network
        • Recurrent Neural Networks
      • Computer Vision
        • Convolutional Recurrent Networks
        • Fully Convolutional Neural Networks
        • Face detector
        • Image IO
        • Image matcher
        • Yolo
      • Natural Language Processing
        • Introduction
        • Text cleaner
        • Text vectorizer
      • Times Series processing
        • TS features selector
      • TensorFlow API
        • LSTM Layer
        • Dense Layer
      • Helpers
        • Data/Object Logger
        • Object Selector (5 ports)
      • Conclusion
  • CUSTOM MODULES
    • Function
    • Class
    • Use cases
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  1. OVERVIEW
  2. Presentation

The SmartPredict Graphical User Interface (GUI)

This section describes the visual appearance of SmartPredict, its components as well as their roles.

PreviousWho may benefit from its useNextThe SmartPredict Modules

Last updated 5 years ago

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The SP GUI

​ Once you are logged in, the SmartPredict Graphical User Interface displays full-screen. The first instance you use it, the workspace' s layout is empty . Later on, as we create new projects, it will show a mosaic view of all of them.

The GUI is composed of:

1. a left pane/sidebar containing the following elements (represented with their respective pictogram) :

  • Dashboard

  • Project

  • Datasets

  • Applications

  • Notebooks

  • Settings

2. and next to it, a clear workspace, with a:

  • search bar

  • a project-creation button

3. On the top is the logo of SmartPredict .The User icon is on its extreme right, clicking through which leads to the user space including the:

  • Profile

  • Account settings

  • and Logout.

The user space is where you can manage everything about your personal information.

The left pane contains the following elements:

  1. Dashboard: The dashboard is the main menu of the platform. From there, you may create new projects as well as consult previous ones. Those latter are arranged according to the date of creation, from the most recent to the least recent. If you have been running a project lately, it is also where you see its current status (either it has run successfully or failed to do so).

  2. Project: The project menu opens on the project panel wherein AI workflows will be represented by flowchart following a four-step process: build, deploy,monitor , test.

  3. Datasets: The dataset menu displays the list of all previously uploaded datasets. It is also the place to upload and store new datasets.

  4. Applications: The Applications menu contains SmartApps for Data Processing and Visualization .

  5. Notebook: The Notebook is a console for customizing modules through Python codes. Use it to interact with SmartPredict by embedding your own code snippets and libraries.

  6. Settings : it is a space for setting up the user account and for integration with GitHub version controls.

  1. Build: The first project workspace is the build workspace.

  2. Deploy: The deploy workspace directly follows the build workspace. Executed projects from the build workspace will directly transition to this next tab, once they are prepared for deployment.

  3. Monitor: The monitor workspace is a dashboard tab to monitor the deployed project.

  4. Test: the test tab is intended for testing models.

The right sidebar displays the menu of different toolsets such as modules, datasets, processing pipelines and logs.

Left pane:

Project Workspace

Right sidebar

Modules: modules are drag and droppable elements. Some are native and some can be created and added to custom modules . They are located under accordions that can be expanded or collapsed to reveal their contents.

Datasets: datasets may present themselves in csv table formats , in image format or soon, in text format. They originate from uploads . There are also preset modules . Supported data formats are: .csv, .npy, .xlsx, .xls, .json, .h5, .html, .pkl ,.joblib ,.txt

Processing Pipelines: processing pipelines are collections of processing steps to apply to datasets for feeding or training a model. They result from processed datasets.

Logs: the logs recapitulate the conditions of the running build . They show after the build sessions. They contain information such as the operation flow .

SmartPredict' s sleek GUI.