[WIP] joey/new docs gen - nodes PR:326

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

@@ -1,13 +1,9 @@
----
-title: ACCURACY
-description: The ACCURACY node takes two dataframes with the true and predicted labels from a classification task, and indicates what proportion of the predictions were correct. These dataframes should both be single columns.
-keywords: [AI, Classification, Python, DataFrame, Accuracy, Prediction]
-image: https://raw.githubusercontent.com/flojoy-ai/docs/main/docs/nodes/AI_ML/CLASSIFICATION/ACCURACY/examples/EX1/output.jpeg
----
 
 [//]: # (Custom component imports)
 
 import DocString from '@site/src/components/DocString';
+import DocStringJSON from '@site/src/components/DocStringJSON';
+
 import PythonCode from '@site/src/components/PythonCode';
 import AppDisplay from '@site/src/components/AppDisplay';
 import SectionBreak from '@site/src/components/SectionBreak';
@@ -16,56 +12,40 @@ import AppendixSection from '@site/src/components/AppendixSection';
 [//]: # (Docstring)
 
 import DocstringSource from '!!raw-loader!./a1-[autogen]/docstring.txt';
+import DocstringJson from '!!raw-loader!./a1-[autogen]/docstring.json';
 import PythonSource from '!!raw-loader!./a1-[autogen]/python_code.txt';
 
 <DocString>{DocstringSource}</DocString>
+<DocStringJSON data={DocstringJson} />
 <PythonCode GLink='AI_ML/CLASSIFICATION/ACCURACY/ACCURACY.py'>{PythonSource}</PythonCode>
 
 <SectionBreak />
 
+
+
 [//]: # (Examples)
 
 ## Examples
 
-import Example1 from './examples/EX1/example.md';
-import App1 from '!!raw-loader!./examples/EX1/app.json';
-import appImg from './examples/EX1/app.jpeg'
-import appImgLight from './examples/EX1/app_light.jpeg'
-import outputImg from './examples/EX1/output.jpeg'
-import outputLight from './examples/EX1/output_light.jpeg'
-
-### Determine whether predictions are accurate
-
 <AppDisplay 
-    nodeLabel='ACCURACY'
-    appImg={appImg}
-    appLight={appImgLight}
-    outputLight={outputLight}
-    outputImg={outputImg}
-    >
-    {App1}
+  GLink='AI_ML/CLASSIFICATION/ACCURACY'
+  nodeLabel='ACCURACY'>
 </AppDisplay>
 
-<Example1 />
-
 <SectionBreak />
 
-[//]: # (Appendix)
+    
 
-## Appendix
+[//]: # (Appendix)
 
 import Notes from './appendix/notes.md';
 import Hardware from './appendix/hardware.md';
 import Media from './appendix/media.md';
 
+## Appendix
+
 <AppendixSection index={0} folderPath='nodes/AI_ML/CLASSIFICATION/ACCURACY/appendix/'><Notes /></AppendixSection>
 <AppendixSection index={1} folderPath='nodes/AI_ML/CLASSIFICATION/ACCURACY/appendix/'><Hardware /></AppendixSection>
 <AppendixSection index={2} folderPath='nodes/AI_ML/CLASSIFICATION/ACCURACY/appendix/'><Media /></AppendixSection>
 
-<SectionBreak />
-
-[//]: # (Edit page on GitHub)
-
-#### Edit this page on GitHub
 
-[Edit page here](https://github.com/flojoy-ai/docs/tree/main/docs/nodes/AI_ML/CLASSIFICATION/ACCURACY)

docs/nodes/AI_ML/CLASSIFICATION/ACCURACY/a1-[autogen]/docstring.json

@@ -0,0 +1,22 @@
+{
+  "description": "These dataframes should both be single columns.",
+  "parameters": [
+    {
+      "name": "true_label",
+      "type": "optional str",
+      "description": "true label users can select from original data"
+    },
+    {
+      "name": "predicted_label",
+      "type": "optional str",
+      "description": "resulting predicted label users can select"
+    }
+  ],
+  "returns": [
+    {
+      "name": null,
+      "type": "DataFrame",
+      "description": "The input predictions dataframe, with an extra boolean column \"prediction_correct\"."
+    }
+  ]
+}

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

@@ -1,13 +1,9 @@
----
-title: ONE_HOT_ENCODING
-description: Discover the power of Python in AI/ML classification with the ONE_HOT_ENCODING node that creates encoding from a dataframe and columns dataframe that contain categorical features.
-keywords: [AI, Classification, Python, DataFrame, Columns Dataframe, Categorical Features, Encoding]
-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';
+import DocStringJSON from '@site/src/components/DocStringJSON';
+
 import PythonCode from '@site/src/components/PythonCode';
 import AppDisplay from '@site/src/components/AppDisplay';
 import SectionBreak from '@site/src/components/SectionBreak';
@@ -16,56 +12,40 @@ import AppendixSection from '@site/src/components/AppendixSection';
 [//]: # (Docstring)
 
 import DocstringSource from '!!raw-loader!./a1-[autogen]/docstring.txt';
+import DocstringJson from '!!raw-loader!./a1-[autogen]/docstring.json';
 import PythonSource from '!!raw-loader!./a1-[autogen]/python_code.txt';
 
 <DocString>{DocstringSource}</DocString>
+<DocStringJSON data={DocstringJson} />
 <PythonCode GLink='AI_ML/CLASSIFICATION/ONE_HOT_ENCODING/ONE_HOT_ENCODING.py'>{PythonSource}</PythonCode>
 
 <SectionBreak />
 
+
+
 [//]: # (Examples)
 
 ## Examples
 
-import Example1 from './examples/EX1/example.md';
-import App1 from '!!raw-loader!./examples/EX1/app.json';
-import appImg from './examples/EX1/app.jpeg'
-import appImgLight from './examples/EX1/app_light.jpeg'
-import outputImg from './examples/EX1/output.jpeg'
-import outputLight from './examples/EX1/output_light.jpeg'
-
-### Create a one hot encoding from a dataframe
-
 <AppDisplay 
-    nodeLabel='ONE_HOT_ENCODING'
-    appImg={appImg}
-    appLight={appImgLight}
-    outputLight={outputLight}
-    outputImg={outputImg}
-    >
-    {App1}
+  GLink='AI_ML/CLASSIFICATION/ONE_HOT_ENCODING'
+  nodeLabel='ONE_HOT_ENCODING'>
 </AppDisplay>
 
-<Example1 />
-
 <SectionBreak />
 
-[//]: # (Appendix)
+    
 
-## Appendix
+[//]: # (Appendix)
 
 import Notes from './appendix/notes.md';
 import Hardware from './appendix/hardware.md';
 import Media from './appendix/media.md';
 
+## Appendix
+
 <AppendixSection index={0} folderPath='nodes/AI_ML/CLASSIFICATION/ONE_HOT_ENCODING/appendix/'><Notes /></AppendixSection>
 <AppendixSection index={1} folderPath='nodes/AI_ML/CLASSIFICATION/ONE_HOT_ENCODING/appendix/'><Hardware /></AppendixSection>
 <AppendixSection index={2} folderPath='nodes/AI_ML/CLASSIFICATION/ONE_HOT_ENCODING/appendix/'><Media /></AppendixSection>
 
-<SectionBreak />
-
-[//]: # (Edit page on GitHub)
-
-#### Edit this page on GitHub
 
-[Edit page here](https://github.com/flojoy-ai/docs/tree/main/docs/nodes/AI_ML/CLASSIFICATION/ONE_HOT_ENCODING)

docs/nodes/AI_ML/CLASSIFICATION/ONE_HOT_ENCODING/a1-[autogen]/docstring.json

@@ -0,0 +1,17 @@
+{
+  "description": "Inputs\n------\ndata : DataFrame\n    The input dataframe containing the categorical features.",
+  "parameters": [
+    {
+      "name": "feature_col",
+      "type": "DataFrame",
+      "description": "A dataframe whose columns are used to create the one hot encoding.\nFor example, if 'data' has columns ['a', 'b', 'c'] and 'feature_col' has columns ['a', 'b'],\nthen the one hot encoding will be created only for columns ['a', 'b'] against 'data'.\nDefaults to None, meaning that all columns of categorizable objects are encoded."
+    }
+  ],
+  "returns": [
+    {
+      "name": null,
+      "type": "DataFrame",
+      "description": "The one hot encoding of the input features."
+    }
+  ]
+}

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

@@ -1,13 +1,9 @@
----
-title: SUPPORT_VECTOR_MACHINE
-description: Train a support vector machine model for classification tasks by taking two dataframes of label and feature from labelled training data and a dataframe of unlabelled input data.
-keywords: [AI, Classification, Python, DataFrame, Train, Vector, Machine Model]
-image: https://raw.githubusercontent.com/flojoy-ai/docs/main/docs/nodes/AI_ML/CLASSIFICATION/SUPPORT_VECTOR_MACHINE/examples/EX1/output.jpeg
----
 
 [//]: # (Custom component imports)
 
 import DocString from '@site/src/components/DocString';
+import DocStringJSON from '@site/src/components/DocStringJSON';
+
 import PythonCode from '@site/src/components/PythonCode';
 import AppDisplay from '@site/src/components/AppDisplay';
 import SectionBreak from '@site/src/components/SectionBreak';
@@ -16,56 +12,40 @@ import AppendixSection from '@site/src/components/AppendixSection';
 [//]: # (Docstring)
 
 import DocstringSource from '!!raw-loader!./a1-[autogen]/docstring.txt';
+import DocstringJson from '!!raw-loader!./a1-[autogen]/docstring.json';
 import PythonSource from '!!raw-loader!./a1-[autogen]/python_code.txt';
 
 <DocString>{DocstringSource}</DocString>
+<DocStringJSON data={DocstringJson} />
 <PythonCode GLink='AI_ML/CLASSIFICATION/SUPPORT_VECTOR_MACHINE/SUPPORT_VECTOR_MACHINE.py'>{PythonSource}</PythonCode>
 
 <SectionBreak />
 
+
+
 [//]: # (Examples)
 
 ## Examples
 
-import Example1 from './examples/EX1/example.md';
-import App1 from '!!raw-loader!./examples/EX1/app.json';
-import appImg from './examples/EX1/app.jpeg'
-import appImgLight from './examples/EX1/app_light.jpeg'
-import outputImg from './examples/EX1/output.jpeg'
-import outputLight from './examples/EX1/output_light.jpeg'
-
-### Train a support vector machine model
-
 <AppDisplay 
-    nodeLabel='SUPPORT_VECTOR_MACHINE'
-    appImg={appImg}
-    appLight={appImgLight}
-    outputLight={outputLight}
-    outputImg={outputImg}
-    >
-    {App1}
+  GLink='AI_ML/CLASSIFICATION/SUPPORT_VECTOR_MACHINE'
+  nodeLabel='SUPPORT_VECTOR_MACHINE'>
 </AppDisplay>
 
-<Example1 />
-
 <SectionBreak />
 
-[//]: # (Appendix)
+    
 
-## Appendix
+[//]: # (Appendix)
 
 import Notes from './appendix/notes.md';
 import Hardware from './appendix/hardware.md';
 import Media from './appendix/media.md';
 
+## Appendix
+
 <AppendixSection index={0} folderPath='nodes/AI_ML/CLASSIFICATION/SUPPORT_VECTOR_MACHINE/appendix/'><Notes /></AppendixSection>
 <AppendixSection index={1} folderPath='nodes/AI_ML/CLASSIFICATION/SUPPORT_VECTOR_MACHINE/appendix/'><Hardware /></AppendixSection>
 <AppendixSection index={2} folderPath='nodes/AI_ML/CLASSIFICATION/SUPPORT_VECTOR_MACHINE/appendix/'><Media /></AppendixSection>
 
-<SectionBreak />
-
-[//]: # (Edit page on GitHub)
-
-#### Edit this page on GitHub
 
-[Edit page here](https://github.com/flojoy-ai/docs/tree/main/docs/nodes/AI_ML/CLASSIFICATION/SUPPORT_VECTOR_MACHINE)

docs/nodes/AI_ML/CLASSIFICATION/SUPPORT_VECTOR_MACHINE/a1-[autogen]/docstring.json

@@ -0,0 +1,17 @@
+{
+  "description": "It takes two dataframes of label and feature from labelled training data and a dataframe of unlabelled input data.",
+  "parameters": [
+    {
+      "name": "kernel",
+      "type": "'linear' | 'poly' | 'rbf' | 'sigmoid' | 'precomputed'",
+      "description": "Specifies the kernel type to be used in the algorithm.\nFor detailed information about kernel types:\nhttps://scikit-learn.org/stable/modules/svm.html#kernel-functions"
+    }
+  ],
+  "returns": [
+    {
+      "name": null,
+      "type": "DataFrame",
+      "description": "The predictions for the input data."
+    }
+  ]
+}

docs/nodes/AI_ML/CLASSIFICATION/TORCHSCRIPT_CLASSIFIER/TORCHSCRIPT_CLASSIFIER.md

@@ -1,8 +1,9 @@
-<!--Add SEO here-->
 
 [//]: # (Custom component imports)
 
 import DocString from '@site/src/components/DocString';
+import DocStringJSON from '@site/src/components/DocStringJSON';
+
 import PythonCode from '@site/src/components/PythonCode';
 import AppDisplay from '@site/src/components/AppDisplay';
 import SectionBreak from '@site/src/components/SectionBreak';
@@ -11,36 +12,30 @@ import AppendixSection from '@site/src/components/AppendixSection';
 [//]: # (Docstring)
 
 import DocstringSource from '!!raw-loader!./a1-[autogen]/docstring.txt';
+import DocstringJson from '!!raw-loader!./a1-[autogen]/docstring.json';
 import PythonSource from '!!raw-loader!./a1-[autogen]/python_code.txt';
 
 <DocString>{DocstringSource}</DocString>
+<DocStringJSON data={DocstringJson} />
 <PythonCode GLink='AI_ML/CLASSIFICATION/TORCHSCRIPT_CLASSIFIER/TORCHSCRIPT_CLASSIFIER.py'>{PythonSource}</PythonCode>
 
 <SectionBreak />
 
+
+
 [//]: # (Examples)
 
 ## Examples
 
-import Example1 from './examples/EX1/example.md';
-import App1 from '!!raw-loader!./examples/EX1/app.json';
-import appImg from './examples/EX1/app.jpeg'
-import outputImg from './examples/EX1/output.jpeg'
-
-### Execute a torchscript classifier against an image
-
 <AppDisplay 
-    nodeLabel='TORCHSCRIPT_CLASSIFIER'
-    appImg={appImg}
-    outputImg={outputImg}
-    >
-    {App1}
+  GLink='AI_ML/CLASSIFICATION/TORCHSCRIPT_CLASSIFIER'
+  nodeLabel='TORCHSCRIPT_CLASSIFIER'>
 </AppDisplay>
 
-<Example1 />
-
 <SectionBreak />
 
+
+
 [//]: # (Appendix)
 
 import Notes from './appendix/notes.md';
@@ -53,4 +48,4 @@ import Media from './appendix/media.md';
 <AppendixSection index={1} folderPath='nodes/AI_ML/CLASSIFICATION/TORCHSCRIPT_CLASSIFIER/appendix/'><Hardware /></AppendixSection>
 <AppendixSection index={2} folderPath='nodes/AI_ML/CLASSIFICATION/TORCHSCRIPT_CLASSIFIER/appendix/'><Media /></AppendixSection>
 
-<!--Add Button here-->
+