Create app.py

app.py

@@ -0,0 +1,56 @@
+import joblib
+import os
+import warnings
+
+import gradio as gr
+
+from huggingface_hub import hf_hub_download
+
+warnings.filterwarnings("ignore")
+TOKEN = os.environ['TOKEN']
+REPO_ID = "SimonRaviv/wine-quality"
+MODEL_FILENAME = "sklearn_model.joblib"
+
+model_file_path = hf_hub_download(REPO_ID, MODEL_FILENAME, use_auth_token=TOKEN)
+model = joblib.load(model_file_path)
+
+
+def predict(data):
+    prediction = model.predict(data.to_numpy()).tolist()
+    prediction = [[p] for p in prediction]
+    return prediction
+
+
+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],
+]
+
+inputs = gr.inputs.Dataframe(
+    row_count=(3, "dynamic"),
+    col_count=(11, "dynamic"),
+    headers=headers,
+    default=default)
+outputs = gr.outputs.Dataframe(type="numpy", headers=["Quality"])
+
+interface = gr.Interface(
+    fn=predict,
+    title="Wine Quality predictor with SKLearn",
+    inputs=inputs,
+    outputs=outputs
+)
+interface.launch()