Merge pull request #366 from flojoy-ai/fathom-qa

feat: Add example headings and QA docs.

docs/nodes/AI_ML/CLASSIFICATION/ACCURACY/ACCURACY.md

@@ -32,6 +32,8 @@ import App1 from '!!raw-loader!./examples/EX1/app.json';
 import appImg from './examples/EX1/app.jpeg'
 import outputImg from './examples/EX1/output.jpeg'
 
+### Determine whether predictions are accurate
+
 <AppDisplay 
     nodeLabel='ACCURACY'
     appImg={appImg}

docs/nodes/AI_ML/CLASSIFICATION/ONE_HOT_ENCODING/ONE_HOT_ENCODING.md

@@ -5,7 +5,6 @@ keywords: [AI, Classification, Python, DataFrame, Columns Dataframe, Categorical
 image: https://raw.githubusercontent.com/flojoy-ai/docs/main/docs/nodes/AI_ML/CLASSIFICATION/ONE_HOT_ENCODING/examples/EX1/output.jpeg
 ---
 
-
 [//]: # (Custom component imports)
 
 import DocString from '@site/src/components/DocString';
@@ -33,6 +32,8 @@ import App1 from '!!raw-loader!./examples/EX1/app.json';
 import appImg from './examples/EX1/app.jpeg'
 import outputImg from './examples/EX1/output.jpeg'
 
+### Create a one hot encoding from a dataframe
+
 <AppDisplay 
     nodeLabel='ONE_HOT_ENCODING'
     appImg={appImg}

docs/nodes/AI_ML/CLASSIFICATION/SUPPORT_VECTOR_MACHINE/SUPPORT_VECTOR_MACHINE.md

@@ -32,6 +32,8 @@ import App1 from '!!raw-loader!./examples/EX1/app.json';
 import appImg from './examples/EX1/app.jpeg'
 import outputImg from './examples/EX1/output.jpeg'
 
+### Train a support vector machine model
+
 <AppDisplay 
     nodeLabel='SUPPORT_VECTOR_MACHINE'
     appImg={appImg}

docs/nodes/AI_ML/CLASSIFICATION/TRAIN_TEST_SPLIT/TRAIN_TEST_SPLIT.md

@@ -32,6 +32,8 @@ import App1 from '!!raw-loader!./examples/EX1/app.json';
 import appImg from './examples/EX1/app.jpeg'
 import outputImg from './examples/EX1/output.jpeg'
 
+### Split the data for testing and training
+
 <AppDisplay 
     nodeLabel='TRAIN_TEST_SPLIT'
     appImg={appImg}

docs/nodes/AI_ML/IMAGE_CAPTIONING/NLP_CONNECT_VIT_GPT2/NLP_CONNECT_VIT_GPT2.md

@@ -32,6 +32,8 @@ import App1 from '!!raw-loader!./examples/EX1/app.json';
 import appImg from './examples/EX1/app.jpeg'
 import outputImg from './examples/EX1/output.jpeg'
 
+### Caption and image
+
 <AppDisplay 
     nodeLabel='NLP_CONNECT_VIT_GPT2'
     appImg={appImg}

docs/nodes/AI_ML/IMAGE_CLASSIFICATION/HUGGING_FACE_PIPELINE/HUGGING_FACE_PIPELINE.md

@@ -1,3 +1,4 @@
+<!--Add SEO here-->
 
 [//]: # (Custom component imports)
 
@@ -17,16 +18,14 @@ import PythonSource from '!!raw-loader!./a1-[autogen]/python_code.txt';
 
 <SectionBreak />
 
-
-
 [//]: # (Examples)
 
 ## Examples
 
 import Example1 from './examples/EX1/example.md';
 import App1 from '!!raw-loader!./examples/EX1/app.json';
 
-
+### Process and classify an image
 
 <AppDisplay
     nodeLabel='HUGGING_FACE_PIPELINE'
@@ -38,9 +37,7 @@ import App1 from '!!raw-loader!./examples/EX1/app.json';
 
 <Example1 />
 
-<SectionBreak />
-
-
+<SectionBreak /> 
 
 [//]: # (Appendix)
 
@@ -54,4 +51,4 @@ import Media from './appendix/media.md';
 <AppendixSection index={1} folderPath='nodes/AI_ML/IMAGE_CLASSIFICATION/HUGGING_FACE_PIPELINE/appendix/'><Hardware /></AppendixSection>
 <AppendixSection index={2} folderPath='nodes/AI_ML/IMAGE_CLASSIFICATION/HUGGING_FACE_PIPELINE/appendix/'><Media /></AppendixSection>
 
-
+<!--Add Button here-->

docs/nodes/AI_ML/IMAGE_CLASSIFICATION/HUGGING_FACE_PIPELINE/examples/EX1/example.md

@@ -1,3 +1,5 @@
-In this example, a `LOCAL_FILE` node reads in a png image of Ada Lovelace.
+<!--Add SEO here-->
 
-The Hugging Face Image classification pipeline processes the image and tries to classify this portrait.
+In this example, a `LOCAL_FILE` node reads in a .png image with a protrait of Ada Lovelace.
+
+The `HUGGING_FACE_PIPELINE` node processes the image and tries to classify this portrait.

docs/nodes/AI_ML/NLP/COUNT_VECTORIZER/COUNT_VECTORIZER.md

@@ -32,6 +32,8 @@ import App1 from '!!raw-loader!./examples/EX1/app.json';
 import appImg from './examples/EX1/app.jpeg'
 import outputImg from './examples/EX1/output.jpeg'
 
+### Transform text to words and a matrix
+
 <AppDisplay 
     nodeLabel='COUNT_VECTORIZER'
     appImg={appImg}

docs/nodes/AI_ML/OBJECT_DETECTION/OBJECT_DETECTION/OBJECT_DETECTION.md

@@ -32,6 +32,8 @@ import App1 from '!!raw-loader!./examples/EX1/app.json';
 import appImg from './examples/EX1/app.jpeg'
 import outputImg from './examples/EX1/output.jpeg'
 
+### Detect and display objects in an image
+
 <AppDisplay 
     nodeLabel='OBJECT_DETECTION'
     appImg={appImg}

docs/nodes/AI_ML/PREDICT_TIME_SERIES/PROPHET_PREDICT/PROPHET_PREDICT.md

@@ -32,6 +32,8 @@ import App1 from '!!raw-loader!./examples/EX1/app.json';
 import appImg from './examples/EX1/app.jpeg'
 import outputImg from './examples/EX1/output.jpeg'
 
+### Train a Prophet model and run a prediction forecast
+
 <AppDisplay 
     nodeLabel='PROPHET_PREDICT'
     appImg={appImg}

docs/nodes/AI_ML/REGRESSION/LEAST_SQUARES/LEAST_SQUARES.md

@@ -32,6 +32,8 @@ import App1 from '!!raw-loader!./examples/EX1/app.json';
 import appImg from './examples/EX1/app.jpeg'
 import outputImg from './examples/EX1/output.jpeg'
 
+### Calculate the coefficients that minimize errors from data points
+
 <AppDisplay 
     nodeLabel='LEAST_SQUARES'
     appImg={appImg}

docs/nodes/AI_ML/SEGMENTATION/DEEPLAB_V3/DEEPLAB_V3.md

@@ -32,6 +32,8 @@ import App1 from '!!raw-loader!./examples/EX1/app.json';
 import appImg from './examples/EX1/app.jpeg'
 import outputImg from './examples/EX1/output.jpeg'
 
+### Produce a segmentation mask from an image
+
 <AppDisplay 
     nodeLabel='DEEPLAB_V3'
     appImg={appImg}

docs/nodes/AI_ML/TEXT_SUMMARIZATION/BART_LARGE_CNN/BART_LARGE_CNN.md

@@ -32,6 +32,8 @@ import App1 from '!!raw-loader!./examples/EX1/app.json';
 import appImg from './examples/EX1/app.jpeg'
 import outputImg from './examples/EX1/output.jpeg'
 
+### Produce a dataframe with a single "summary_text" column
+
 <AppDisplay 
     nodeLabel='BART_LARGE_CNN'
     appImg={appImg}

docs/nodes/EXTRACTORS/DATAFRAME/EXTRACT_COLUMNS/EXTRACT_COLUMNS.md

@@ -32,6 +32,8 @@ import App1 from '!!raw-loader!./examples/EX1/app.json';
 import appImg from './examples/EX1/app.jpeg'
 import outputImg from './examples/EX1/output.jpeg'
 
+### Extract specific columns from a dataframe
+
 <AppDisplay 
     nodeLabel='EXTRACT_COLUMNS'
     appImg={appImg}

docs/nodes/EXTRACTORS/FILE/OPEN_IMAGE/OPEN_IMAGE.md

@@ -32,6 +32,8 @@ import App1 from '!!raw-loader!./examples/EX1/app.json';
 import appImg from './examples/EX1/app.jpeg'
 import outputImg from './examples/EX1/output.jpeg'
 
+### Load an image file from disk
+
 <AppDisplay 
     nodeLabel='OPEN_IMAGE'
     appImg={appImg}

docs/nodes/EXTRACTORS/FILE/OPEN_PARQUET/OPEN_PARQUET.md

@@ -32,6 +32,8 @@ import App1 from '!!raw-loader!./examples/EX1/app.json';
 import appImg from './examples/EX1/app.jpeg'
 import outputImg from './examples/EX1/output.jpeg'
 
+### Load a `.parquet` file from disk
+
 <AppDisplay 
     nodeLabel='OPEN_PARQUET'
     appImg={appImg}

docs/nodes/EXTRACTORS/FILE/READ_CSV/READ_CSV.md

@@ -32,6 +32,8 @@ import App1 from '!!raw-loader!./examples/EX1/app.json';
 import appImg from './examples/EX1/app.jpeg'
 import outputImg from './examples/EX1/output.jpeg'
 
+### Load a `.csv` file from disk or a URL
+
 <AppDisplay 
     nodeLabel='READ_CSV'
     appImg={appImg}

docs/nodes/EXTRACTORS/FILE/READ_S3/READ_S3.md

@@ -32,6 +32,8 @@ import App1 from '!!raw-loader!./examples/EX1/app.json';
 import appImg from './examples/EX1/app.jpeg'
 import outputImg from './examples/EX1/output.jpeg'
 
+### Extract a file from an S3 bucket
+
 <AppDisplay 
     nodeLabel='READ_S3'
     appImg={appImg}

docs/nodes/EXTRACTORS/FILE/READ_S3/examples/EX1/example.md

@@ -1,8 +1,8 @@
 ---
 title: READ_S3
-description: In this example, the READ_S3 node extracts a file from an AWS S3 bucket, and displays it with the TABLE visualizer node.
+description: In this example, the READ_S3 node extracts a file from an Amazon S3 bucket, and displays it with the TABLE visualizer node.
 keyword: [Python, Extractor, Extract, AWS, S3 file reading, Read data from S3, File extraction from S3, Flojoy Read S3 node, Python S3 data retrieval, S3 file handling, Data extraction from cloud, Python data import from S3, Cloud data processing, Efficient data access from S3]
 image: https://raw.githubusercontent.com/flojoy-ai/docs/main/docs/nodes/EXTRACTORS/FILE/READ_S3/examples/EX1/output.jpeg
 ---
 
-In this example, the `READ_S3` node extracts a file from an AWS S3 bucket, and displays it with the `TABLE` visualizer node.
+In this example, the `READ_S3` node extracts a file from an Amazon S3 bucket, and displays it with the `TABLE` visualizer node.

docs/nodes/GENERATORS/GENERATORS.md

@@ -1,6 +1,6 @@
 ---
 hide_table_of_contents: true
-sidebar_label: Generate
+sidebar_label: Generators
 sidebar_position: 1
 slug: /nodes/GENERATORS/
 ---

docs/nodes/GENERATORS/SAMPLE_DATASETS/PLOTLY_DATASET/PLOTLY_DATASET.md

@@ -32,6 +32,8 @@ import App1 from '!!raw-loader!./examples/EX1/app.json';
 import appImg from './examples/EX1/app.jpeg'
 import outputImg from './examples/EX1/output.jpeg'
 
+### Retrieve a dataframe from a Plotly dataset
+
 <AppDisplay 
     nodeLabel='PLOTLY_DATASET'
     appImg={appImg}

docs/nodes/GENERATORS/SAMPLE_DATASETS/R_DATASET/R_DATASET.md

@@ -32,6 +32,8 @@ import App1 from '!!raw-loader!./examples/EX1/app.json';
 import appImg from './examples/EX1/app.jpeg'
 import outputImg from './examples/EX1/output.jpeg'
 
+### Retrieve a dataframe from an R dataset
+
 <AppDisplay 
     nodeLabel='R_DATASET'
     appImg={appImg}

docs/nodes/GENERATORS/SAMPLE_DATASETS/TEXT_DATASET/TEXT_DATASET.md

@@ -32,6 +32,8 @@ import App1 from '!!raw-loader!./examples/EX1/app.json';
 import appImg from './examples/EX1/app.jpeg'
 import outputImg from './examples/EX1/output.jpeg'
 
+### Retrieve categorized text from a text dataset
+
 <AppDisplay 
     nodeLabel='TEXT_DATASET'
     appImg={appImg}

docs/nodes/GENERATORS/SAMPLE_IMAGES/SKIMAGE/SKIMAGE.md

@@ -23,8 +23,6 @@ import PythonSource from '!!raw-loader!./a1-[autogen]/python_code.txt';
 
 <SectionBreak />
 
-
-
 [//]: # (Examples)
 
 ## Examples
@@ -34,6 +32,8 @@ import App1 from '!!raw-loader!./examples/EX1/app.json';
 import appImg from './examples/EX1/app.jpeg'
 import outputImg from './examples/EX1/output.jpeg'
 
+### Load `scikit` images
+
 <AppDisplay 
     nodeLabel='SKIMAGE'
     appImg={appImg}

docs/nodes/GENERATORS/SAMPLE_IMAGES/SKIMAGE/examples/EX1/example.md

@@ -5,4 +5,8 @@ keywords: [Python, Generate, SklearnImage generator, Sample image data generatio
 image: https://raw.githubusercontent.com/flojoy-ai/docs/main/docs/nodes/GENERATORS/SAMPLE_DATASETS/SKIMAGE/examples/EX1/output.jpeg
 ---
 
-In this example, the `SKIMAGE` node takes one parameter, `img_key`, which is the name of the sample image, and loads it from the `scikit-image` package. In this case, `astronaut` is the default value of this parameter. It then passes an `image` type object (r,g,b,a), from the `DataContainer` class, to the `OBJECT_DETECTION` node using the `opencv` Python library. Finally, the current script is terminated with the `END` node, which is the same as its parent node.
+In this example, the `SKIMAGE` node takes one parameter, `img_key`, which is the name of the sample image, and loads it from the `scikit-image` package. In this case, `astronaut` is the default value of this parameter. 
+
+It then passes an `image` type object (r,g,b,a), from the `DataContainer` class, to the `OBJECT_DETECTION` node using the `opencv` Python library.
+
+Finally, the script is terminated with the `END` node, which is the same as its parent node.

docs/nodes/GENERATORS/SIMULATIONS/BASIC_OSCILLATOR/BASIC_OSCILLATOR.md

@@ -32,6 +32,8 @@ import App1 from '!!raw-loader!./examples/EX1/app.json';
 import appImg from './examples/EX1/app.jpeg'
 import outputImg from './examples/EX1/output.jpeg'
 
+### Generate a sine wave
+
 <AppDisplay 
     nodeLabel='BASIC_OSCILLATOR'
     appImg={appImg}

docs/nodes/GENERATORS/SIMULATIONS/CONSTANT/CONSTANT.md

@@ -32,6 +32,8 @@ import App1 from '!!raw-loader!./examples/EX1/app.json';
 import appImg from './examples/EX1/app.jpeg'
 import outputImg from './examples/EX1/output.jpeg'
 
+### Generate an OrderedPair, Vector or Scalar of numeric constants
+
 <AppDisplay 
     nodeLabel='CONSTANT'
     appImg={appImg}

docs/nodes/GENERATORS/SIMULATIONS/FEEDBACK/FEEDBACK.md

@@ -33,6 +33,8 @@ import App1 from '!!raw-loader!./examples/EX1/app.json';
 import appImg from './examples/EX1/app.jpeg'
 import outputImg from './examples/EX1/output.jpeg'
 
+### Capture and pass results from specific nodes
+
 <AppDisplay 
     nodeLabel='FEEDBACK'
     appImg={appImg}

docs/nodes/GENERATORS/SIMULATIONS/LINSPACE/LINSPACE.md

@@ -32,6 +32,8 @@ import App1 from '!!raw-loader!./examples/EX1/app.json';
 import appImg from './examples/EX1/app.jpeg'
 import outputImg from './examples/EX1/output.jpeg'
 
+### Generate data spaced evenly between two points
+
 <AppDisplay 
     nodeLabel='LINSPACE'
     appImg={appImg}

docs/nodes/GENERATORS/SIMULATIONS/MATRIX/MATRIX.md

@@ -32,6 +32,8 @@ import App1 from '!!raw-loader!./examples/EX1/app.json';
 import appImg from './examples/EX1/app.jpeg'
 import outputImg from './examples/EX1/output.jpeg'
 
+### Generate a random matrix
+
 <AppDisplay 
     nodeLabel='MATRIX'
     appImg={appImg}

docs/nodes/GENERATORS/SIMULATIONS/RAND/RAND.md

@@ -32,6 +32,8 @@ import App1 from '!!raw-loader!./examples/EX1/app.json';
 import appImg from './examples/EX1/app.jpeg'
 import outputImg from './examples/EX1/output.jpeg'
 
+### Generate a single or list of random numbers
+
 <AppDisplay 
     nodeLabel='RAND'
     appImg={appImg}

docs/nodes/GENERATORS/SIMULATIONS/SCALAR/SCALAR.md

@@ -18,16 +18,14 @@ import PythonSource from '!!raw-loader!./a1-[autogen]/python_code.txt';
 
 <SectionBreak />
 
-
-
 [//]: # (Examples)
 
 ## Examples
 
 import Example1 from './examples/EX1/example.md';
 import App1 from '!!raw-loader!./examples/EX1/app.json';
 
-
+### Generate a single Scalar value
 
 <AppDisplay 
     nodeLabel='SCALAR'
@@ -40,8 +38,6 @@ import App1 from '!!raw-loader!./examples/EX1/app.json';
 <Example1 />
 
 <SectionBreak />
-
-
 
 [//]: # (Appendix)
 
@@ -55,4 +51,4 @@ import Media from './appendix/media.md';
 <AppendixSection index={1} folderPath='nodes/GENERATORS/SIMULATIONS/SCALAR/appendix/'><Hardware /></AppendixSection>
 <AppendixSection index={2} folderPath='nodes/GENERATORS/SIMULATIONS/SCALAR/appendix/'><Media /></AppendixSection>
 
-
+<!--- Add Button here --->

docs/nodes/GENERATORS/SIMULATIONS/SCALAR/examples/EX1/example.md

@@ -1,3 +1,5 @@
+<!--- Add SEO here --->
+
 In this example, the `LINSPACE` node is used to generate a list of 200 samples and is added with a `SCALAR` node that holds a value of 10.
 
 Therefore, all of the points that were generated have increased by 10. 

docs/nodes/GENERATORS/SIMULATIONS/SECOND_ORDER_SYSTEM/SECOND_ORDER_SYSTEM.md

@@ -32,6 +32,8 @@ import App1 from '!!raw-loader!./examples/EX1/app.json';
 import appImg from './examples/EX1/app.jpeg'
 import outputImg from './examples/EX1/output.jpeg'
 
+### Apply a second order exponential function
+
 <AppDisplay 
     nodeLabel='SECOND_ORDER_SYSTEM'
     appImg={appImg}

docs/nodes/GENERATORS/SIMULATIONS/SINE/SINE.md

@@ -32,6 +32,8 @@ import App1 from '!!raw-loader!./examples/EX1/app.json';
 import appImg from './examples/EX1/app.jpeg'
 import outputImg from './examples/EX1/output.jpeg'
 
+### Generate a waveform function
+
 <AppDisplay 
     nodeLabel='SINE'
     appImg={appImg}