Create app.py

app.py

@@ -0,0 +1,50 @@
+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=2)
+model_dir = model.download()
+model = joblib.load(model_dir + "/titanic_model.pkl")
+
+
+def titanic(Pclass,Sex,Age,SibSp,Parch,Fare,Embarked):
+    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(Embarked)
+    # 'res' is a list of predictions returned as the label.
+    res = model.predict(np.asarray(input_list).reshape(1, -1))     
+    return res
+        
+demo = gr.Interface(
+    fn=titanic,
+    title="Titanic Survival Predictive Analytics",
+    description="Experiment with passengers information to predict whether they can survive in titanic.",
+    allow_flagging="never",
+    inputs=[
+        gr.inputs.Number(default=1.0, label="Class [0, 1, 2]"),
+        gr.inputs.Number(default=1.0, label="Sex [0(male), 1(female)]"),
+        gr.inputs.Number(default=1.0, label="Age [y/o]"),
+        gr.inputs.Number(default=1.0, label="sibsp [0-5]]"),
+        gr.inputs.Number(default=1.0, label="Parch [0-6]]"),
+        gr.inputs.Number(default=1.0, label="Fare [USD]"),
+        gr.inputs.Number(default=1.0, label="Embarked [0 (S), 1 (C),  2 (Q)]"),
+        ],
+    outputs=gr.Text(value="none")
+    )
+
+demo.launch()
+