Update app.py

app.py

@@ -0,0 +1,51 @@
+import gradio as gr
+import numpy as np
+from PIL import Image
+import requests
+
+import hopsworks
+import joblib
+
+project = hopsworks.login(api_key_value="0rdWXlLgEd3mkGOg.iRZ7TtAkWGPlJHNQcAEph6Qbokoaq7QTBRI9ckwWUki8tIYGyBvrKhJvtLoUOGQ4")
+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):
+    input_list = []
+    input_list.append(Pclass)
+    input_list.append(Sex)
+    input_list.append(Age)
+    input_list.append(SibSp)
+    # '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.
+    # flower_url = "https://raw.githubusercontent.com/featurestoreorg/serverless-ml-course/main/src/01-module/assets/" + res[0] + ".png"
+    # img = Image.open(requests.get(flower_url, stream=True).raw)
+    # return img
+    if (res[0] == 0):
+        result = "I'm sorry, the person is dead"
+    else:
+        result = "Awesome, the person is survived!!!!!!"
+    return result
+
+
+demo = gr.Interface(
+    fn=titanic,
+    title="Titanic Predictive Analytics",
+    description="Experiment with Passenger class/Sex/Age/SibSp to predict if the person is survived or not.",
+    allow_flagging="never",
+    inputs=[
+        gr.inputs.Number(default=1.0, label="Pclass (Flight class 1/2/3)"),
+        gr.inputs.Number(default=1.0, label="Sex (male=1/female=2)"),
+        gr.inputs.Number(default=1.0, label="Age (in years)"),
+        gr.inputs.Number(default=1.0, label="SibSp (number of siblings)"),
+    ],
+    outputs=gr.Textbox(label="Result: "))
+
+demo.launch()