basic multioutput example with reqs

app.py

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+import gradio as gr
+import pandas as pd
+
+from sklearn.multioutput import MultiOutputRegressor
+
+from sklearn.datasets import load_linnerud
+from sklearn.multioutput import MultiOutputRegressor
+from sklearn.linear_model import Ridge
+
+X, y = load_linnerud(return_X_y=True, as_frame=True)
+regr = MultiOutputRegressor(Ridge(random_state=123)).fit(X, y)
+# regr.predict(X.iloc[[0]])
+
+
+iface = gr.Interface(
+    fn=regr.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.Numpy(
+        value=regr.predict(X.head(1)),
+        headers=list(y.columns),
+        col_count=(y.shape[1], "fixed"),
+        datatype=y.dtypes.apply(str).replace("float64", "number").values.tolist(),
+        min_width=50,
+    ),
+)
+iface.launch()

requirements.txt

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+numpy
+gradio
+scikit-learn