Merge pull request #363 from flojoy-ai/update-node-docs-234

[WIP] TORCHSCRIPT_CLASSIFIER node for any user-provided .torchscript AI model - nodes PR:234

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

@@ -1,7 +1,19 @@
+The ONE_HOT_ENCODING node creates a one hot encoding from a dataframe containing categorical features.
 
-The ONE_HOT_ENCODING node creates a one hot encoding from a dataframe and columns dataframe containing categorical features.
+    Inputs
+    ------
+    data : DataFrame
+        The input dataframe containing the categorical features.
 
-Returns
--------
-DataFrame
-    The one hot encoding of the input features.
+    Parameters
+    ----------
+    feature_col: DataFrame, optional
+        A dataframe whose columns are used to create the one hot encoding.
+        For example, if 'data' has columns ['a', 'b', 'c'] and 'feature_col' has columns ['a', 'b'],
+        then the one hot encoding will be created only for columns ['a', 'b'] against 'data'.
+        Defaults to None, meaning that all columns of categorizable objects are encoded.
+
+    Returns
+    -------
+    DataFrame
+        The one hot encoding of the input features.

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

@@ -0,0 +1,59 @@
+
+[//]: # (Custom component imports)
+
+import DocString from '@site/src/components/DocString';
+import PythonCode from '@site/src/components/PythonCode';
+import AppDisplay from '@site/src/components/AppDisplay';
+import SectionBreak from '@site/src/components/SectionBreak';
+import AppendixSection from '@site/src/components/AppendixSection';
+
+[//]: # (Docstring)
+
+import DocstringSource from '!!raw-loader!./a1-[autogen]/docstring.txt';
+import PythonSource from '!!raw-loader!./a1-[autogen]/python_code.txt';
+
+<DocString>{DocstringSource}</DocString>
+<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'
+
+
+
+<AppDisplay 
+    nodeLabel='TORCHSCRIPT_CLASSIFIER'
+    appImg={appImg}
+    outputImg={outputImg}
+    >
+    {App1}
+</AppDisplay>
+
+<Example1 />
+
+<SectionBreak />
+
+
+
+[//]: # (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/TORCHSCRIPT_CLASSIFIER/appendix/'><Notes /></AppendixSection>
+<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>
+
+

docs/nodes/AI_ML/CLASSIFICATION/TORCHSCRIPT_CLASSIFIER/a1-[autogen]/docstring.txt

@@ -0,0 +1,19 @@
+
+Execute a torchscript classifier against an input image.
+
+Inputs
+----------
+input_image : Image
+    The image to classify.
+class_names : DataFrame
+    A dataframe containing the class names.
+
+Parameters
+----------
+model_path : str
+    The path to the torchscript model.
+
+Returns
+----------
+DataFrame
+    A dataframe containing the class name and confidence score.

docs/nodes/AI_ML/CLASSIFICATION/TORCHSCRIPT_CLASSIFIER/a1-[autogen]/python_code.txt

@@ -0,0 +1,51 @@
+from flojoy import flojoy, run_in_venv, Image, DataFrame
+
+
+@flojoy
+@run_in_venv(
+    pip_dependencies=[
+        "torch==2.0.1",
+        "torchvision==0.15.2",
+        "numpy",
+        "Pillow",
+    ]
+)
+def TORCHSCRIPT_CLASSIFIER(
+    input_image: Image, class_names: DataFrame, model_path: str
+) -> DataFrame:
+
+
+    import torch
+    import torchvision
+    import pandas as pd
+    import numpy as np
+    import PIL.Image
+
+    # Load model
+    model = torch.jit.load(model_path)
+    channels = [input_image.r, input_image.g, input_image.b]
+    mode = "RGB"
+
+    if input_image.a is not None:
+        channels.append(input_image.a)
+        mode += "A"
+
+    input_image_pil = PIL.Image.fromarray(
+        np.stack(channels).transpose(1, 2, 0), mode=mode
+    ).convert("RGB")
+    input_tensor = torchvision.transforms.functional.to_tensor(
+        input_image_pil
+    ).unsqueeze(0)
+
+    # Run model
+    with torch.inference_mode():
+        output = model(input_tensor)
+
+    # Get class name and confidence score
+    _, pred = torch.max(output, 1)
+    class_name = class_names.m.iloc[pred.item()].item()
+    confidence = torch.nn.functional.softmax(output, dim=1)[0][pred.item()].item()
+
+    return DataFrame(
+        df=pd.DataFrame({"class_name": [class_name], "confidence": [confidence]})
+    )

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

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+This node does not require any peripheral hardware to operate. Please see INSTRUMENTS for nodes that interact with the physical world through connected hardware.

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

@@ -0,0 +1 @@
+No supporting screenshots, photos, or videos have been added to the media.md file for this node.

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

@@ -0,0 +1 @@
+No theory or technical notes have been contributed for this node yet.