Create app.py

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")
+]
+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).launch()