Add application file

app.py

@@ -0,0 +1,107 @@
+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, fare, embarked, familysize, appellation, cabin):
+    input_list = []
+    # PClass
+    input_list.append(int(pclass))
+
+    # Gender
+    if sex == "Male":
+        input_list.append(0)
+    else:
+        input_list.append(1)
+
+    # Age
+    input_list.append(age)
+
+    # Fare
+    input_list.append(fare)
+
+    # Embarked
+    if embarked == "S":
+        input_list.append(0)
+    elif embarked == "C":
+        input_list.append(1)
+    elif embarked == "Q":
+        input_list.append(2)
+
+    # Family Size
+    input_list.append(familysize)
+
+    # Appellation
+    if appellation == "master":
+        input_list.append(1).append(0).append(0).append(0).append(0).append(0)
+    elif appellation == "miss":
+        input_list.append(0).append(1).append(0).append(0).append(0).append(0)
+    elif appellation == "mr":
+        input_list.append(0).append(0).append(1).append(0).append(0).append(0)
+    elif appellation == "mrs":
+        input_list.append(0).append(0).append(0).append(1).append(0).append(0)
+    elif appellation == "officer":
+        input_list.append(0).append(0).append(0).append(0).append(1).append(0)
+    elif appellation == "royalty":
+        input_list.append(0).append(0).append(0).append(0).append(0).append(1)
+
+    # Cabin
+    if cabin == "A":
+        input_list.append(1).append(0).append(0).append(0).append(0).append(0).append(0).append(0).append(0)
+    elif cabin == "B":
+        input_list.append(0).append(1).append(0).append(0).append(0).append(0).append(0).append(0).append(0)
+    elif cabin == "C":
+        input_list.append(0).append(0).append(1).append(0).append(0).append(0).append(0).append(0).append(0)
+    elif cabin == "D":
+        input_list.append(0).append(0).append(0).append(1).append(0).append(0).append(0).append(0).append(0)
+    elif cabin == "E":
+        input_list.append(0).append(0).append(0).append(0).append(1).append(0).append(0).append(0).append(0)
+    elif cabin == "F":
+        input_list.append(0).append(0).append(0).append(0).append(0).append(1).append(0).append(0).append(0)
+    elif cabin == "G":
+        input_list.append(0).append(0).append(0).append(0).append(0).append(0).append(1).append(0).append(0)
+    elif cabin == "T":
+        input_list.append(0).append(0).append(0).append(0).append(0).append(0).append(0).append(1).append(0)
+    else:
+        input_list.append(0).append(0).append(0).append(0).append(0).append(0).append(0).append(0).append(1)
+
+
+    # '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.
+    titanic_url = "https://github.com/Qinglin2000/ID2223" + str(res[0]) + ".png?raw=true"
+    img = Image.open(requests.get(titanic_url, stream=True).raw)
+    return img
+
+
+demo = gr.Interface(
+    fn=titanic,
+    title="Titatnic Predictive Analytics",
+    description="Experiment with titanic dataset values.",
+    allow_flagging="never",
+    inputs=[
+        gr.Dropdown(choices=["1", "2", "3"], label="PClass", value="1"),
+        gr.Radio(choices=["Male", "Female"], label="Gender", value="Male"),
+        gr.inputs.Number(default=30.0, label="Age"),
+        gr.inputs.Number(default=40.99, label="Fare"),
+        gr.Dropdown(choices=["S","C","Q"], label="Embarked", value="S"),
+        gr.Number(label="Family Size", precision=0, value=1),
+        gr.Dropdown(choices=["master", "miss", "mr", "mrs", "officer", "royalty"], label="Appellation", value="master"),
+        gr.Dropdown(choices=["A", "B", "C", "D", "E", "F", "G", "T", "U"], label="Cabin", value="A"),
+    ],
+    outputs=gr.Image(type="pil"))
+
+demo.launch()

requirements.txt

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+hopsworks
+joblib
+scikit-learn
+