Create app.py

app.py

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+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("titanic_modal", version=1)
+model_dir = model.download()
+model = joblib.load(model_dir + "/titanic_model.pkl")
+
+
+def titanic(pclass, sex, age, sibsp, parch, fare, embarked):
+    input_list = []
+    if sex == 'female':
+        input_list.append(1.0)
+        input_list.append(0.0)
+    elif sex == 'male':
+        input_list.append(0.0)
+        input_list.append(1.0)
+    else:
+        print("ERROR!")
+        exit()
+    if embarked == "C":
+        input_list.append(1.0)
+        input_list.append(0.0)
+        input_list.append(0.0)
+    elif embarked == "Q":
+        input_list.append(0.0)
+        input_list.append(1.0)
+        input_list.append(0.0)
+    elif embarked == "S":
+        input_list.append(0.0)
+        input_list.append(0.0)
+        input_list.append(1.0)
+    else:
+        print("ERROR!")
+        exit()
+    if age < 18:
+        input_list.append(1.0)
+    elif age < 55:
+        input_list.append(2.0)
+    else:
+        input_list.append(3.0)
+    input_list.append(sibsp)
+    input_list.append(parch)
+    input_list.append(fare)
+    input_list.append(pclass)
+
+    # '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.
+    if res[0] == 1.0:
+        survive_url = "https://raw.githubusercontent.com/Hope-Liang/ID2223Lab1/main/serverless-ml-titanic/images/survived.png"
+    else:
+        survive_url = "https://raw.githubusercontent.com/Hope-Liang/ID2223Lab1/main/serverless-ml-titanic/images/died.png"
+    img = Image.open(requests.get(survive_url, stream=True).raw)
+    return img
+
+
+demo = gr.Interface(
+    fn=titanic,
+    title="Titanic Survival Predictive Analytics",
+    description="Experiment with titanic passenger features to predict whether survived or not.",
+    allow_flagging="never",
+    inputs=[
+        gr.inputs.Number(default=1, label="Pclass (1,2,3)"),
+        gr.inputs.Textbox(default="female", label="Sex (female/male)"),
+        gr.inputs.Number(default=30.0, label="age (years)"),
+        gr.inputs.Number(default=1.0, label="SibSp"),
+        gr.inputs.Number(default=1.0, label="Parch"),
+        gr.inputs.Number(default=10.0, label="Fare (GBP)"),
+        gr.inputs.Textbox(default="S", label="Embarked (S,C,Q)")
+    ],
+    outputs=gr.Image(type="pil"))
+
+demo.launch()