Create app.py

app.py

@@ -0,0 +1,74 @@
+from turtle import title
+import gradio as gr
+import matplotlib.pyplot as plt
+from sklearn.datasets import load_diabetes
+from sklearn.linear_model import LinearRegression
+from sklearn.model_selection import cross_val_predict
+from sklearn.metrics import PredictionErrorDisplay
+
+
+def predict_diabetes(subsample, plot_type):
+    X, y = load_diabetes(return_X_y=True)
+    lr = LinearRegression()
+    y_pred = cross_val_predict(lr, X, y, cv=10)
+
+    fig, axs = plt.subplots(ncols=2, figsize=(8, 4))
+    if "Actual vs. Predicted" in plot_type:
+        PredictionErrorDisplay.from_predictions(
+            y,
+            y_pred=y_pred,
+            kind="actual_vs_predicted",
+            subsample=subsample,
+            ax=axs[0],
+            random_state=0,
+        )
+        axs[0].set_title("Actual vs. Predicted values")
+    if "Residuals vs. Predicted" in plot_type:
+        PredictionErrorDisplay.from_predictions(
+            y,
+            y_pred=y_pred,
+            kind="residual_vs_predicted",
+            subsample=subsample,
+            ax=axs[1],
+            random_state=0,
+        )
+        axs[1].set_title("Residuals vs. Predicted Values")
+
+    fig.suptitle("Plotting cross-validated predictions")
+    plt.tight_layout()
+    plt.close(fig)
+
+    # Save the figure as an image
+    image_path = "predictions.png"
+    fig.savefig(image_path)
+    return image_path
+
+
+# Define the Gradio interface
+inputs = [
+    gr.inputs.Slider(minimum=1, maximum=100, step=1, default=100, label="Subsample"),
+    gr.inputs.CheckboxGroup(["Actual vs. Predicted", "Residuals vs. Predicted"], label="Plot Types", default=["Actual vs. Predicted", "Residuals vs. Predicted"])
+]
+outputs = gr.outputs.Image(label="Cross-Validated Predictions", type="pil")
+
+title = "Plotting Cross-Validated Predictions"
+description="This app plots cross-validated predictions for a linear regression model trained on the diabetes dataset. See the original scikit-learn example here: https://scikit-learn.org/stable/auto_examples/model_selection/plot_cv_predict.html"
+examples = [
+    [
+        100,
+        ["Actual vs. Predicted"],
+        "Plotting cross-validated predictions with Actual vs. Predicted plot.",
+    ],
+    [
+        50,
+        ["Residuals vs. Predicted"],
+        "Plotting cross-validated predictions with Residuals vs. Predicted plot.",
+    ],
+    [
+        75,
+        ["Actual vs. Predicted", "Residuals vs. Predicted"],
+        "Plotting cross-validated predictions with both Actual vs. Predicted and Residuals vs. Predicted plots.",
+    ],
+]
+
+gr.Interface(fn=predict_diabetes, title=title, description=description, examples=examples, inputs=inputs, outputs=outputs).launch()