Create app.py

app.py

@@ -0,0 +1,45 @@
+import joblib
+import os
+
+import gradio as gr
+
+from huggingface_hub import hf_hub_download
+
+
+file_path = hf_hub_download("osanseviero/wine-quality", "sklearn_model.joblib", use_auth_token=os.environ['TOKEN'])
+model = joblib(filepath)
+
+def predict(data):
+    return model.predict(data.to_numpy())
+
+
+headers = [
+    "fixed acidity",
+    "volatile acidity",
+    "citric acid",
+    "residual sugar",
+    "chlorides",
+    "free sulfur dioxide",
+    "total sulfur dioxide",
+    "density",
+    "pH",
+    "sulphates",
+    "alcohol",
+]
+default = [
+    [7.4, 0.7, 0, 1.9, 0.076, 11, 34, 0.9978, 3.51, 0.56, 9.4],
+    [7.8, 0.88, 0, 2.6, 0.098, 25, 67, 0.9968, 3.2, 0.68, 9.8],
+    [7.8, 0.76, 0.04, 2.3, 0.092, 15, 54, 0.997, 3.26, 0.65, 9.8],
+]
+
+iface = gr.Interface(
+    wine_quality_predictor,
+    title="Wine Quality predictor with SKLearn",
+    inputs=gr.inputs.Dataframe(
+        headers=headers,
+        default=default,
+    ),
+    outputs="numpy",
+    description="Learn how to create demos of private models at https://huggingface.co/spaces/osanseviero/tips-and-tricks"
+)
+iface.launch()