gpr example

gbr_example.py

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+import gradio as gr
+import pandas as pd
+
+from sklearn.ensemble import HistGradientBoostingRegressor
+from sklearn.datasets import load_diabetes
+from sklearn.multioutput import MultiOutputRegressor
+
+# Assume y is now a DataFrame with multiple columns
+X, y = load_diabetes(return_X_y=True, as_frame=True)
+y = pd.DataFrame([y, y]).T
+
+# Make the estimator multi-output capable
+est = MultiOutputRegressor(HistGradientBoostingRegressor()).fit(X, y)
+
+
+def predict(input_df):
+    prediction = est.predict(input_df)
+    # Assume y has columns named "target1", "target2", etc.
+    return pd.DataFrame(prediction, columns=y.columns)
+
+
+iface = gr.Interface(
+    fn=predict,
+    inputs=gr.Dataframe(
+        value=X.head(1),
+        headers=list(X.columns),
+        col_count=(X.shape[1], "fixed"),
+        row_count=(1, "dynamic"),
+        datatype=X.dtypes.apply(str).replace("float64", "number").values.tolist(),
+    ),
+    outputs=gr.Dataframe(
+        value=y.head(1),
+        headers=list(y.columns),
+        col_count=(y.shape[1], "fixed"),
+        datatype=y.dtypes.apply(str).replace("float64", "number").values.tolist(),
+    ),
+)
+iface.launch()