Create app.py

app.py

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+import gradio as gr
+import numpy as np
+from sklearn.datasets import load_iris
+from sklearn.pipeline import Pipeline
+from sklearn.feature_selection import SelectPercentile, f_classif
+from sklearn.preprocessing import StandardScaler
+from sklearn.svm import SVC
+import matplotlib.pyplot as plt
+from sklearn.model_selection import cross_val_score
+
+def svm_anova_app(percentiles):
+    X, y = load_iris(return_X_y=True)
+
+    # Add non-informative features
+    rng = np.random.RandomState(0)
+    X = np.hstack((X, 2 * rng.random((X.shape[0], 36))))
+
+    # Create a feature-selection transform, a scaler, and an instance of SVM
+    clf = Pipeline([
+        ("anova", SelectPercentile(f_classif)),
+        ("scaler", StandardScaler()),
+        ("svc", SVC(gamma="auto")),
+    ])
+
+    score_means = []
+    score_stds = []
+
+    for p in percentiles:
+        clf.set_params(anova__percentile=float(p))
+        this_scores = cross_val_score(clf, X, y)
+        score_means.append(this_scores.mean())
+        score_stds.append(this_scores.std())
+
+    plt.errorbar(percentiles, score_means, np.array(score_stds))
+    plt.title("Performance of the SVM-Anova varying the percentile of features selected")
+    plt.xticks(np.linspace(0, 100, 11, endpoint=True))
+    plt.xlabel("Percentile")
+    plt.ylabel("Accuracy Score")
+    plt.axis("tight")
+
+    # Save the plot to a file
+    plt.savefig("plot.png")
+    plt.close()
+
+    return "plot.png"
+
+iface = gr.Interface(
+    fn=svm_anova_app,
+    inputs=gr.inputs.CheckboxGroup(['1', '3', '6', '10', '15', '20', '25', '30', '35', '40', '45', '50', '55', '60', '65', '70', '75', '80', '85', '90', '95', '100'], label="Percentiles"),
+    outputs="image",
+    title="SVM-Anova Performance",
+    description="This example shows how to perform univariate feature selection before running a SVC (support vector classifier) to improve the classification scores. We use the iris dataset (4 features) and add 36 non-informative features. We can find that our model achieves best performance when we select around 10 percent of features. See the original scikit-learn example here: https://scikit-learn.org/stable/auto_examples/svm/plot_svm_anova.html"
+)
+
+iface.launch()