Create app.py

app.py

@@ -0,0 +1,47 @@
+import gradio as gr
+import numpy as np
+from PIL import Image
+import requests
+
+import hopsworks
+import joblib
+
+project = hopsworks.login()
+fs = project.get_feature_store()
+
+mr = project.get_model_registry()
+model = mr.get_model("iris_modal", version=1)
+model_dir = model.download()
+model = joblib.load(model_dir + "/iris_model.pkl")
+
+
+def iris(sepal_length, sepal_width, petal_length, petal_width):
+    input_list = []
+    input_list.append(sepal_length)
+    input_list.append(sepal_width)
+    input_list.append(petal_length)
+    input_list.append(petal_width)
+    # 'res' is a list of predictions returned as the label.
+    res = model.predict(np.asarray(input_list).reshape(1, -1))
+    # We add '[0]' to the result of the transformed 'res', because 'res' is a list, and we only want
+    # the first element.
+    flower_url = "https://raw.githubusercontent.com/featurestoreorg/serverless-ml-course/main/src/01-module/assets/" + \
+                 res[0] + ".png"
+    img = Image.open(requests.get(flower_url, stream=True).raw)
+    return img
+
+
+demo = gr.Interface(
+    fn=iris,
+    title="Iris Flower Predictive Analytics",
+    description="Experiment with sepal/petal lengths/widths to predict which flower it is.",
+    allow_flagging="never",
+    inputs=[
+        gr.inputs.Number(default=1.0, label="sepal length (cm)"),
+        gr.inputs.Number(default=1.0, label="sepal width (cm)"),
+        gr.inputs.Number(default=1.0, label="petal length (cm)"),
+        gr.inputs.Number(default=1.0, label="petal width (cm)"),
+    ],
+    outputs=gr.Image(type="pil"))
+
+demo.launch()