First try

README.md

@@ -1,10 +1,10 @@
 ---
-title: Titanic
-emoji: 💩
-colorFrom: red
-colorTo: red
+title: Iris
+emoji: 🐢
+colorFrom: purple
+colorTo: green
 sdk: gradio
-sdk_version: 3.11.0
+sdk_version: 3.5
 app_file: app.py
 pinned: false
 license: apache-2.0

app.py

@@ -0,0 +1,47 @@
+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", version=4)
+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
+        
+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.Image(type="pil"))
+
+demo.launch()
+

requirements.txt

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+hopsworks
+joblib
+scikit-learn