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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, cabin):
+    input_list = []
+    input_list.append(pclass)
+    input_list.append(sex)
+    input_list.append(age)
+    input_list.append(sibsp)
+    input_list.append(parch)
+    input_list.append(fare)
+    input_list.append(cabin)
+
+    res = model.predict(np.asarray(input_list).reshape(1, -1))
+
+    if (res[0] == 1):
+        return "Passenger survived!"
+    else:
+        return "Passenger did not survive."
+
+
+demo = gr.Interface(
+    fn=titanic,
+    title="Titanic Passenger Survival Predictive Analysis",
+    description="Experiment with the variables below to determine if a passenger would survive the Titanic",
+    allow_flagging="never",
+    inputs=[
+        gr.inputs.Number(
+            default=1, label="Ticket class (1: upper, 2: middle, 3: lower)"),
+        gr.inputs.Number(default=1, label="Gender (1: female, 0: male)"),
+        gr.inputs.Number(
+            default=1, label="Age (0: child, 1: teenager, 2: young adult, 3: adult, 4: old)"),
+        gr.inputs.Number(default=1, label="Siblings/spouse onboard"),
+        gr.inputs.Number(default=1, label="Parents/children onboard"),
+        gr.inputs.Number(default=1, label="Passenger fare ($)"),
+        gr.inputs.Number(
+            default=1, label="Cabin deck (0-7 corresponds to A-G)")
+    ],
+
+    outputs=gr.Textbox())
+
+demo.launch()

requirements.txt

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+hopsworks
+joblib
+scikit-learn