app.py

# Code source: Gaël Varoquaux
# License: BSD 3 clause

# This code is a MOD with Gradio Demo
import numpy as np
import matplotlib.pyplot as plt
import matplotlib

from sklearn import decomposition
from sklearn import datasets

# unused but required import for doing 3d projections with matplotlib < 3.2
import mpl_toolkits.mplot3d  # noqa: F401
matplotlib.use('agg')

import gradio as gr

np.random.seed(5)

## PCA
def PCA_Pred(x1, x2, x3, x4):
    #Load Data from iris dataset:
    iris = datasets.load_iris()
    X = iris.data
    y = iris.target

    fig, ax = plt.subplots(1, subplot_kw={'projection': '3d', 'elev': 48, 'azim': 134})
    ax.set_position([0, 0, 0.95, 1])
    plt.cla()

    #Create the model with 3 principal components:
    pca = decomposition.PCA(n_components=3)
    
    #Fit model and transform (decrease dimensions) iris dataset:
    pca.fit(X)
    X = pca.transform(X)

    #Set labels to data clusters
    for name, label in [("Setosa", 0), ("Versicolour", 1), ("Virginica", 2)]:
        ax.text3D(
            X[y == label, 0].mean(),
            X[y == label, 1].mean() + 1.5,
            X[y == label, 2].mean(),
            name,
            horizontalalignment="center",
            bbox=dict(alpha=0.5, edgecolor="w", facecolor="w"),
        )
    # Reorder the labels to have colors matching the cluster results
    y = np.choose(y, [1, 2, 0]).astype(float)
    ax.scatter(X[:, 0], X[:, 1], X[:, 2], c=y, cmap=plt.cm.nipy_spectral, edgecolor="k")

    user_iris_data = np.array([[x1, x2, x3, x4]], ndmin=2)

    #Perform reduction to user data
    pc_output = pca.transform(user_iris_data)
    ax.scatter(pc_output[0, 0], pc_output[0, 1], pc_output[0, 2], c='r', marker='*')

    ax.xaxis.set_ticklabels([])
    ax.yaxis.set_ticklabels([])
    ax.zaxis.set_ticklabels([])

    return [pc_output, fig]
    
title = "🌺 PCA example with Iris Data-set"
with gr.Blocks(title=title) as demo:
    gr.Markdown(f"## {title}")
    gr.Markdown(
        """
        ## The following app is a demo for PCA decomposition. It takes 4 dimensions as input, in reference \
        to the Following image, and returns the transformed first 3 principal components (feature \
        reduction) taken from a pre-trained model with Iris dataset. 
        """)
    html = (
        "<div >"
        "<img  src='file/iris_dataset_info.png' alt='image one'>"
        + "</div>"
        )
    gr.HTML(html)
    with gr.Row():
        with gr.Column():
            inp1 = gr.Slider(0, 7, value=1, step=0.1, label="Sepal Length (cm)")
            inp2 = gr.Slider(0, 5, value=1, step=0.1, label="Sepal Width (cm)")
            inp3 = gr.Slider(0, 7, value=1, step=0.1, label="Petal Length (cm)")
            inp4 = gr.Slider(0, 5, value=1, step=0.1, label="Petal Width (cm)")
            output = gr.Textbox(label="PCA Axes")
        with gr.Column():
            plot = gr.Plot(label="PCA 3D Space")

    Reduction = gr.Button("PCA Transform")
    Reduction.click(fn=PCA_Pred, inputs=[inp1, inp2, inp3, inp4], outputs=[output, plot])
    demo.load(fn=PCA_Pred, inputs=[inp1, inp2, inp3, inp4], outputs=[output, plot])

demo.launch()