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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(age, sex, pclass):
+    input_list = []
+
+    input_list.append(int(age))
+    input_list.append(int(sex)) # value returned by dropdown is index of option selected
+    input_list.append(int(pclass+1)) # index starts at 0 so increment by 1
+
+    # Bin input age to bin index of range
+    if 0 < input_list[0] <= 20:
+        input_list[0] = 0
+    elif 20 < input_list[0] <= 50:
+        input_list[0] = 1
+    elif 50 < input_list[0] <= 75:
+        input_list[0] = 2
+    elif input_list[0] > 75:
+        input_list[0] = 3
+    else:
+        # we should just assume < 0 = 0..
+        print("Incorrect age value set. Try again.")
+
+    #print(input_list)
+    # 'res' is a list of predictions returned as the label.
+    #res = model.predict(np.asarray(input_list).reshape(1, -1), ntree_limit=model.best_ntree_limit)  # for xgboost
+    #print(np.asarray(input_list).reshape(1, -1))
+    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.
+    #print(res[0]) # 0/1
+    # below is just for testing
+    if res[0] == 0: #ded
+        passenger_url = "https://media.istockphoto.com/id/157612035/sv/foto/shipwreck.jpg?s=612x612&w=0&k=20&c=BSVml8_SqgvSmEijAprhniyp_Wa_l5qIIVIxhmmBgBQ="
+    else:
+        passenger_url = "https://i.chzbgr.com/full/5420028160/hD88BD9FE/like-a-boss"
+    img = Image.open(requests.get(passenger_url, stream=True).raw)
+    return img
+
+
+demo = gr.Interface(
+    fn=titanic,
+    title="Titanic Passenger Survival Predictive Analytics",
+    description="Experiment with some passenger features to predict whether your passenger would have survived or not.",
+    allow_flagging="never",
+    inputs=[
+        gr.inputs.Number(default=1, label="Age"),
+        gr.inputs.Dropdown(choices=["Male", "Female"], type="index", label="Sex"),
+        gr.inputs.Dropdown(choices=["Class 1", "Class 2", "Class 3"],
+                           type="index", label="Pclass"),
+    ],
+    outputs=gr.Image(type="pil"))
+
+demo.launch()

requirements.txt

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+hopsworks
+joblib
+scikit-learn