Built the gradio implementation of the example.

app.py

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+# Gradio Implementation: Lenix Carter
+# License: BSD 3-Clause or CC-0
+
+import gradio as gr
+
+import numpy as np
+import matplotlib.pyplot as plt
+from sklearn.metrics import r2_score
+
+from sklearn.model_selection import train_test_split
+from sklearn.linear_model import LinearRegression
+
+plt.switch_backend("agg")
+
+def compare_reg(n_samples, n_features):
+    np.random.seed(42)
+
+    X = np.random.randn(n_samples, n_features)
+    true_coef = 3 * np.random.randn(n_features)
+
+    # Threshold coefficients to render them non-negative
+    true_coef[true_coef < 0] = 0
+    y = np.dot(X, true_coef)
+
+    # Add some noise
+    y += 5 * np.random.normal(size=(n_samples,))
+
+    # Split the data in train set and test set
+    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.5)
+
+    reg_nnls = LinearRegression(positive=True)
+    y_pred_nnls = reg_nnls.fit(X_train, y_train).predict(X_test)
+    r2_score_nnls = r2_score(y_test, y_pred_nnls)
+
+    reg_ols = LinearRegression()
+    y_pred_ols = reg_ols.fit(X_train, y_train).predict(X_test)
+    r2_score_ols = r2_score(y_test, y_pred_ols)
+
+    fig, ax = plt.subplots()
+    ax.plot(reg_ols.coef_, reg_nnls.coef_, linewidth=0, marker=".")
+
+    low_x, high_x = ax.get_xlim()
+    low_y, high_y = ax.get_ylim()
+    low = max(low_x, low_y)
+    high = min(high_x, high_y)
+    ax.plot([low, high], [low, high], ls="--", c=".3", alpha=0.5)
+    ax.set_xlabel("OLS regression coefficients", fontweight="bold")
+    ax.set_ylabel("NNLS regression coefficients", fontweight="bold")
+
+    scores = "The R2 for NNLS is {}\nThe R2 for OLS is {}".format(r2_score_nnls, r2_score_ols)
+    return fig, scores
+
+title = "Non-negative Least Squares"
+with gr.Blocks() as demo:
+    gr.Markdown(f" # {title}")
+    gr.Markdown("""
+                     This example fits a linear model with positivity constraints on the regression coefficients and compares the estimated coefficients to a classic linear regression. 
+
+                     This is based on the example [here](https://scikit-learn.org/stable/auto_examples/linear_model/plot_nnls.html#sphx-glr-auto-examples-linear-model-plot-nnls-py).
+                     """)
+    with gr.Row():
+        with gr.Column():
+            n_samp = gr.Slider(100, 1000, 200, step=1, label="Number of Samples")
+            n_feat = gr.Slider(3, 100, 50, step=1, label="Number of Features")
+            btn = gr.Button(label="Run")
+        with gr.Column():
+            scores = gr.Textbox(label="R2 Scores")
+            coeff_comp_graph = gr.Plot(label="Comparison of Coefficients")
+    btn.click(
+            fn=compare_reg,
+            inputs=[n_samp, n_feat],
+            outputs=[coeff_comp_graph, scores]
+            )
+    with gr.Row():
+        gr.Markdown("This shows a high degree of correlation between the the regression coefficients of OLS and NNLS. However, we observe that some coefficients in the NNLS regression shrink to 0.")
+
+if __name__ == '__main__':
+    demo.launch()

requirements.txt

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+matplotlib==3.6.3
+scikit-learn==1.2.2