api example and max rows workaround

api_example.py

@@ -0,0 +1,35 @@
+from gradio_client import Client
+from sklearn.datasets import load_linnerud
+import pandas as pd
+import numpy as np
+from time import time
+
+X, y = load_linnerud(return_X_y=True, as_frame=True)
+
+# create a dataframe with 1000 randomly generated values for predicting
+rng = np.random.default_rng(42)
+num_pred = 1000
+X_pred = pd.DataFrame(
+    {
+        "Chins": 50 * rng.random(num_pred),
+        "Situps": 80 * rng.random(num_pred),
+        "Jumps": 20 * rng.random(num_pred),
+    }
+)
+
+client = Client("AccelerationConsortium/sklearn-train-basic")
+
+t0 = time()
+result = client.predict(
+    {
+        "headers": X_pred.columns.tolist(),
+        "data": X_pred.values.tolist(),
+    },  # Dict(headers: List[str], data: List[List[Any]], metadata: Dict(str, List[Any] | None) | None) in 'X' Dataframe component
+    api_name="/predict",
+)
+
+print(f"Time taken: {time() - t0:.2f}s")
+
+result_df = pd.DataFrame(result["data"], columns=result["headers"])
+
+print(result_df)

app.py

@@ -10,8 +10,19 @@ X, y = load_linnerud(return_X_y=True, as_frame=True)
 regr = MultiOutputRegressor(Ridge(random_state=123)).fit(X, y)
 # example usage: regr.predict(X.iloc[[0]])
 
+
+def predict(X):
+    max_rows = 100000
+    if X.shape[0] > max_rows:
+        raise ValueError(
+            f"Too many rows ({X.shape[0]}), please use less than {max_rows} rows."
+        )
+    return regr.predict(X)
+
+
 iface = gr.Interface(
-    fn=regr.predict,
+    title="MultiOutputRegressor Example",
+    fn=predict,
     inputs=gr.Dataframe(
         value=X.head(1),
         headers=list(X.columns),
@@ -24,7 +35,6 @@ iface = gr.Interface(
         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()