Create app.py

app.py

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+import gradio as gr
+import matplotlib.pyplot as plt
+from sklearn.datasets import load_iris
+from sklearn.svm import SVC
+from sklearn.model_selection import StratifiedKFold, permutation_test_score
+import numpy as np
+import tempfile
+import os
+
+def run_permutation_test(display_option, kernel, random_state, n_permutations):
+    iris = load_iris()
+    X = iris.data
+    y = iris.target
+
+    n_uncorrelated_features = 20
+    rng = np.random.RandomState(seed=0)
+    X_rand = rng.normal(size=(X.shape[0], n_uncorrelated_features))
+
+    clf = SVC(kernel=kernel, random_state=random_state)
+    cv = StratifiedKFold(2, shuffle=True, random_state=0)
+
+    score_iris, perm_scores_iris, pvalue_iris = permutation_test_score(
+        clf, X, y, scoring="accuracy", cv=cv, n_permutations=n_permutations
+    )
+
+    score_rand, perm_scores_rand, pvalue_rand = permutation_test_score(
+        clf, X_rand, y, scoring="accuracy", cv=cv, n_permutations=n_permutations
+    )
+
+    original_plot_path = None
+    random_plot_path = None
+
+    if display_option in ['original', 'both']:
+        # Original data
+        fig, ax = plt.subplots()
+        ax.hist(perm_scores_iris, bins=20, density=True)
+        ax.axvline(score_iris, ls="--", color="r")
+        score_label = f"Score on original\ndata: {score_iris:.2f}\n(p-value: {pvalue_iris:.3f})"
+        ax.text(0.7, 10, score_label, fontsize=12)
+        ax.set_xlabel("Accuracy score")
+        ax.set_ylabel("Probability")
+        original_plot_path = os.path.join(tempfile.mkdtemp(), "original_plot.png")
+        plt.savefig(original_plot_path)
+        plt.close()
+
+    if display_option in ['random', 'both']:
+        # Random data
+        fig, ax = plt.subplots()
+        ax.hist(perm_scores_rand, bins=20, density=True)
+        ax.set_xlim(0.13)
+        ax.axvline(score_rand, ls="--", color="r")
+        score_label = f"Score on original\ndata: {score_rand:.2f}\n(p-value: {pvalue_rand:.3f})"
+        ax.text(0.14, 7.5, score_label, fontsize=12)
+        ax.set_xlabel("Accuracy score")
+        ax.set_ylabel("Probability")
+        random_plot_path = os.path.join(tempfile.mkdtemp(), "random_plot.png")
+        plt.savefig(random_plot_path)
+        plt.close()
+
+    return original_plot_path, random_plot_path
+
+iface = gr.Interface(
+    fn=run_permutation_test,
+    inputs=[
+        gr.inputs.Dropdown(
+            choices=["original", "random", "both"],
+            label="Display Option",
+            default="both"
+        ),
+        gr.inputs.Dropdown(
+            choices=["linear", "rbf", "poly"],
+            label="Kernel",
+            default="linear"
+        ),
+        gr.inputs.Slider(
+            minimum=0, maximum=10, step=1,
+            label="Random State",
+            default=7
+        ),
+        gr.inputs.Slider(
+            minimum=100, maximum=2000, step=100,
+            label="Number of Permutations",
+            default=1000
+        )
+    ],
+    outputs=["image", "image"],
+    title="Test with permutations the significance of a classification score",
+    description="This example demonstrates the use of permutation_test_score to evaluate the significance of a cross-validated score using permutations. This operation is being performed on the Iris Dataset. See the original scikit-learn example here: https://scikit-learn.org/stable/auto_examples/model_selection/plot_permutation_tests_for_classification.html",
+    examples=[
+        ["both", "linear", 7, 1000],
+        ["original", "rbf", 3, 500],
+        ["random", "poly", 5, 1500]
+    ],
+    allow_flagging=False
+)
+iface.launch()