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Kernel-Density-Estimation / app.py

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	import gradio as gr
	import numpy as np
	import matplotlib.pyplot as plt

	from sklearn.datasets import load_digits
	from sklearn.neighbors import KernelDensity
	from sklearn.decomposition import PCA
	from sklearn.model_selection import GridSearchCV

	def generate_digits(bandwidth, num_samples):

	# convert bandwidth to integer
	bandwidth = int(bandwidth)

	# convert num_samples to integer
	num_samples = int(num_samples)

	# load the data
	digits = load_digits()

	# project the 64-dimensional data to a lower dimension
	pca = PCA(n_components=15, whiten=False)
	data = pca.fit_transform(digits.data)

	# use grid search cross-validation to optimize the bandwidth
	params = {"bandwidth": np.logspace(-1, 1, 20)}
	grid = GridSearchCV(KernelDensity(), params)
	grid.fit(data)

	# use the specified bandwidth to compute the kernel density estimate
	kde = KernelDensity(bandwidth=bandwidth)
	kde.fit(data)

	# sample new points from the data
	new_data = kde.sample(num_samples, random_state=0)
	new_data = pca.inverse_transform(new_data)

	# reshape the data into a 4x11 grid
	new_data = new_data.reshape((num_samples, 64))
	real_data = digits.data[:num_samples].reshape((num_samples, 64))

	# create the plot
	fig, ax = plt.subplots(9, 11, subplot_kw=dict(xticks=[], yticks=[]))
	for j in range(11):
	ax[4, j].set_visible(False)
	for i in range(4):
	index = i * 11 + j # Calculate the correct index
	if index < num_samples:
	im = ax[i, j].imshow(
	real_data[index].reshape((8, 8)), cmap=plt.cm.binary, interpolation="nearest"
	)
	im.set_clim(0, 16)
	im = ax[i + 5, j].imshow(
	new_data[index].reshape((8, 8)), cmap=plt.cm.binary, interpolation="nearest"
	)
	im.set_clim(0, 16)
	else:
	ax[i, j].axis("off")
	ax[i + 5, j].axis("off")

	ax[0, 5].set_title("Selection from the input data")
	ax[5, 5].set_title('"New" digits drawn from the kernel density model')


	# save the plot to a file
	plt.savefig("digits_plot.png")

	# return the path to the generated plot
	return "digits_plot.png"

	# create the Gradio interface
	inputs = [
	gr.inputs.Slider(minimum=1, maximum=10, step=1, label="Bandwidth"),
	# gr.inputs.Number(default=44, label="Number of Samples")
	# Change to Slider
	gr.inputs.Slider(minimum=1, maximum=100, step=1, label="Number of Samples")
	]
	output = gr.outputs.Image(type="pil")

	title = "Kernel Density Estimation"
	description = "This example shows how kernel density estimation (KDE), a powerful non-parametric density estimation technique, can be used to learn a generative model for a dataset. With this generative model in place, new samples can be drawn. These new samples reflect the underlying model of the data. See the original scikit-learn example here: https://scikit-learn.org/stable/auto_examples/neighbors/plot_digits_kde_sampling.html"
	examples = [
	[1, 44], # Changed to integer values
	[8, 22], # Changed to integer values
	[7, 51] # Changed to integer values
	]

	gr.Interface(generate_digits, inputs, output, title=title, description=description, examples=examples, live=True).launch()