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app.py

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+import gradio as gr
+import numpy as np
+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=3)
+model_dir = model.download()
+model = joblib.load(model_dir + "/titanic_model.pkl")
+
+
+def titanic(age, sex, pclass, fare):
+    input_list = []
+    input_list.append(age)
+    input_list.append(sex)
+    input_list.append(pclass)
+    input_list.append(fare)
+    # '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:
+        output = "This individual probably survived the crash."
+    else:
+        output = "This individual unfortunately probably did not survive the crash."
+    return output
+        
+demo = gr.Interface(
+    fn=titanic,
+    title="Titanic survivor analytics",
+    description="Experiment with personal data to predict whether a person would survive the Titanic",
+    allow_flagging="never",
+    inputs=[
+        gr.inputs.Number(default=30.0, label=" Age "),
+        gr.inputs.Number(default=1, label=" Sex (0 = Female, 1 = Male) "),
+        gr.inputs.Number(default=2, label=" Ticket class (1 = first, 2 = second, 3 = third) "),
+        gr.inputs.Number(default=1.0, label=" Passenger fare ()"),
+        ],
+    outputs=gr.outputs.Textbox(label='Prediction'))
+
+demo.launch()
+

requirements.txt

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+hopsworks