Add wine predictor app

app.py

@@ -0,0 +1,53 @@
+import gradio as gr
+from sklearn.hub import HubLoader 
+
+hub = HubLoader("risingodegua/wine-quality-model", "sklearn_model.joblib")
+model = hub.load()
+
+def wine_quality_predictor(X):
+    '''Predicts the quality of wine
+
+    Parameters
+    ----------
+
+    X : numpy, list
+        A list containing values used for prediction. 
+
+    Returns
+    -------
+    List
+        The list of predicted values.
+       
+    '''
+    return model.predict(X.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,
+    gr.inputs.Dataframe(
+        headers=headers,
+        default=default,
+    ),
+    ["numpy"],
+    description="Enter wine properties for prediction"
+)
+iface.launch()