Merge branch 'main' of https://github.com/cnpem/deepsirius-ui

apps/deepsirius-ui/src/components/help.tsx

@@ -1,9 +1,5 @@
-import {
-  CoffeeIcon,
-  DatabaseIcon,
-  DumbbellIcon,
-  ExternalLinkIcon,
-} from 'lucide-react';
+import { ExternalLinkIcon } from 'lucide-react';
+import NodeIcon from './workboard/node-components/node-icon';
 import Link from 'next/link';
 import React, { useState } from 'react';
 import { useHotkeys } from 'react-hotkeys-hook';
@@ -24,7 +20,7 @@ function ExternalLinks() {
     <div className="flex flex-row items-start justify-between gap-1">
       <Link
         className={cn(buttonVariants({ variant: 'outline' }), '')}
-        href="https://github.com/cnpem/deepsirius-ui/blob/main/README.md"
+        href="https://deepsirius.lnls.br/dive"
         rel="noopener noreferrer"
         target="_blank"
         title="help"
@@ -68,17 +64,25 @@ function MinimapCaption() {
       <div className="flex flex-row items-center justify-between gap-1 rounded-md border border-input p-2">
         <div className="flex flex-col gap-1">
           <div className="flex flex-row items-center gap-1">
-            <DatabaseIcon className="h-4 w-4" />
+            <NodeIcon nodeType="dataset" className="h-4 w-4" />
             <p className="text-xs">Dataset</p>
           </div>
           <div className="flex flex-row items-center gap-1">
-            <DumbbellIcon className="h-4 w-4" />
+            <NodeIcon nodeType="augmentation" className="h-4 w-4" />
+            <p className="text-xs">Augmentation</p>
+          </div>
+          <div className="flex flex-row items-center gap-1">
+            <NodeIcon nodeType="network" className="h-4 w-4" />
             <p className="text-xs">Network</p>
           </div>
           <div className="flex flex-row items-center gap-1">
-            <CoffeeIcon className="h-4 w-4" />
+            <NodeIcon nodeType="inference" className="h-4 w-4" />
             <p className="text-xs">Inference</p>
           </div>
+          <div className="flex flex-row items-center gap-1">
+            <NodeIcon nodeType="finetune" className="h-4 w-4" />
+            <p className="text-xs">Finetune</p>
+          </div>
         </div>
         <div className="flex flex-col gap-1">
           <div className="flex flex-row items-center gap-1">

apps/deepsirius-ui/src/pages/index.tsx

@@ -29,14 +29,14 @@ const SessionOptions: React.FC = () => {
         <StyledLink href={'/'} onClick={() => void signIn(undefined)}>
           Sign in
         </StyledLink>
-        <StyledLink href={'/dive'}>Documentation</StyledLink>
+        <StyledLink href={'https://deepsirius.lnls.br/dive'}>Documentation</StyledLink>
       </>
     );
 
   return (
     <>
       <StyledLink href={user.route}>Workspaces</StyledLink>
-      <StyledLink href={'/dive'}>Documentation</StyledLink>
+      <StyledLink href={'https://deepsirius.lnls.br/dive'}>Documentation</StyledLink>
     </>
   );
 };

apps/docs/content/docs/concepts/augmentation.mdx

@@ -28,7 +28,7 @@ to perform the augmentation transformations. A dataset in a `success` state can
 be connected to the augmentation node by clicking and dragging a connection line
 from the dataset node bottom side
 [handle](/docs/concepts/workspace#node-handles) to the augmentation node top
-handle, of you can create a connected augmentation node by clicking on the
+handle. Or you can create a connected augmentation node by clicking on the
 handle in the bottom side of the dataset node.
 
 <ImageZoom

apps/docs/content/docs/concepts/dataset.mdx

@@ -9,11 +9,11 @@ import Node from "@/app/_components/node";
 import { Callout } from "fumadocs-ui/components/callout";
 import DatasetFormImage from "@/public/assets/node_form_dataset.png";
 import PatchesImage from "@/public/assets/patches.png";
-import RandomSamplitImage from "@/public/assets/random_sampling.png";
+import RandomSamplingImage from "@/public/assets/random_sampling.png";
 
 The dataset component is a [node](/docs/concepts/workspace#nodes) responsible
 for creating a dataset for training the network. It creates a file containing a
-collection of images and labels stored in an apropriate format for deep learning
+collection of images and labels stored in an appropriate format for deep learning
 data processing that will be used to train a network. When clicked, the dataset
 will open a panel with a form to be filled in with the necessary information to
 create a dataset. Let's go through the form fields:
@@ -71,7 +71,7 @@ Number of classes in the label file.
 
 ## Sample Size
 
-The number of random patches extracted from a strategy (uniform avaible only)
+The number of random patches extracted from a strategy (uniform available only)
 used to create the train dataset, example below shows a blue cube patch
 extracted to a pink cube for training. The size of the dataset depends on the
 sample size. The larger the sample size, the heavier the file will become.
@@ -96,7 +96,7 @@ patch, this parameter adjusts its size.
 
 <ImageZoom
   alt="Random Sampling"
-  src={RandomSamplitImage}
+  src={RandomSamplingImage}
   className="!my-0 w-full rounded-sm"
   priority
 />

apps/docs/content/docs/concepts/network.mdx

@@ -39,15 +39,15 @@ _Network node form from a network node connected to a dataset node._
 
 In this section, you will set parameters that will directly affect the GPU
 memory usage and the quality and velocity of training and select the deep
-learning architeture. You must imagine that basically the training is trying to
+learning architecture. You must imagine that basically the training is trying to
 determine a function that can predict labels based on a dataset. Ideally, this
 function must obtain the lowest possible error, also known as the optimum
 condition (global minimum). Optimization is carried out by a variation of the
 gradient descent method in order to find a minimum point of the error function.
 
 ## Network Type
 
-### U-net – 2D
+### U-net - 2D
 
 It is the best option for 2D training and works faster for 3D training. The
 training strategy lies in the widespread use of augmentation data to use
@@ -61,11 +61,11 @@ segmented samples more efficiently.
 />
 _U-net architecture (example for 32x32 pixels in the lowest resolution). Each blue
 box corresponds to a multi-channel feature map. The number of channels is denoted
-on top of the box. The x-y-size is provided at the lower left edge of the box. White
+on top of the box. The xy-size is provided at the lower left edge of the box. White
 boxes represent copied feature maps. The arrows denote the different operations.
 Reference: [U-net 2D](https://link.springer.com/chapter/10.1007/978-3-319-24574-4_28)._
 
-### V-net – 3D
+### V-net - 3D
 
 It usually gives better results, but it demands more computationally. It is
 trained to predict the segmentation of the entire volume at once, with an
@@ -74,7 +74,7 @@ optimized function to deal with the imbalance between background and foreground.
 <ImageZoom alt="V-net" src={ParamVnet} className="!my-0 rounded-sm" priority />
 _Schematic representation of Vnet network architecture. Reference: [V-net 3D](https://ieeexplore.ieee.org/abstract/document/7785132/)._
 
-### U-net – 3D
+### U-net - 3D
 
 Similar to V-net 3D but with less convolutions and layers, therefore, faster for
 training and inference.

apps/docs/content/docs/concepts/workspace.mdx

@@ -136,11 +136,11 @@ explorer window where the user can navigate through the remote storage file
 system and select the desired base path for the workspace files. The user can
 only view and explore the files in the remote storage server that are allowed by
 the user's credential permissions. By selecting a base path, the user will be
-propted back to the workspace creation form with the selected path filled in the
+prompted back to the workspace creation form with the selected path filled in the
 Workspace path input field and needs to fill in the Workspace name input field
 which will be the name of the new directory that will be created in the selected
 base path. Before submitting the form, the user must choose the Slurm partition
-where the workspace creation job will be sumitted. By now the user must know
+where the workspace creation job will be submitted. By now the user must know
 which partitions are available and scheduled for the user's group.
 
 <ImageZoom
@@ -156,7 +156,7 @@ _Remote file navigation for selecting the the workspace base path._
 If the submitted data is correct, the job will be submitted and when the job is
 completed, the user will be redirected to the workspace page. There are many
 reasons why the job could fail, even after the basic checks are done before
-sumitting the job. From network errors, to the selection of an unauthorized
+submitting the job. From network errors, to the selection of an unauthorized
 queue and many types of server errors. After the job is submitted, the form will
 be disabled and the interface will keep checking the job status until it is
 completed or show an error if the job fails.
@@ -227,21 +227,21 @@ three main independent concepts that can be subdivided in 5 independent steps:
    by the `run_inference` function in the ssc-deepsirius package cli.
 
 Any combination of these steps that starts from 1 and ends in 3 mean a complete
-processing workflow of the ssc-deepsirius package, with specific input and ouput
+processing workflow of the ssc-deepsirius package, with specific input and output
 parameters that results in the creation or transformation of data between them
 on the remote storage server. So the results of a step can be used as input for
-another processing step as an independent job without the user ocuppying the
+another processing step as an independent job without the user occupying the
 slurm queue and the cluster resources when preparing the data for the next step.
 
 As the results of the steps are saved in the remote storage server, the user can
-check the results of the steps or use it as it see fit, even if the interface is
+check the results of the steps or use it as it sees fit, even if the interface is
 not running - or start another variation of the analysis at any time during the
 life cycle of the files.
 
-These caracteristics of the ssc-deepsirius package are explored by the interface
+These characteristics of the ssc-deepsirius package are explored by the interface
 by describing the steps as nodes in a graph and the relations between them as
 edges. By saving the state of the nodes and the edges in the database, the user
-can load the workspace with the apropiate metadata telling a story of the
+can load the workspace with the appropriate metadata telling a story of the
 analysis and the state of the files in the remote storage server, thus being
 able to continue the analysis and compare with other possible steps or
 variations of the analysis at any time.
@@ -363,6 +363,6 @@ following possible visualizations:
   the expected results.
 - **Tensorboard**: _(Only available for the network and finetune nodes)_. Calls
   a secondary process that starts a tensorboard view of the logs of the training
-  process. The tensorboard view is a tool that helps the user to monitor the
+  process. The tensorboard view is a tool that helps the user monitor the
   training process of the deep learning model. The tensorboard view is available
   for the training and finetuning nodes.