Create app.py

app.py

@@ -0,0 +1,53 @@
+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=10)
+model_dir = model.download()
+model = joblib.load(model_dir + "/titanic_model.pkl")
+
+def titanic(age, embarked, fare, parch, pclass, sex, sibsp):
+    input_list = []
+    input_list.append(age)
+    input_list.append(embarked)
+    input_list.append(fare)
+    input_list.append(parch)
+    input_list.append(pclass)
+    input_list.append(sex)
+    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)            
+    if res == [1]:
+        res = 'survive'
+    else:
+        res = 'die'
+    return res
+        
+demo = gr.Interface(
+    fn=titanic,
+    title="Titanic Survivor Predictive Analytics",
+    description="Experiment with age/embarked/fare/parch/pclass/sex/sibsp to predict if the passenger survived.",
+    allow_flagging="never",
+    inputs=[
+        gr.inputs.Number(default=2.0, label="age"),
+        gr.inputs.Number(default=1.0, label="embarked (0 for S, 1 for C, 2 for Q)"),
+        gr.inputs.Number(default=35.0, label="fare"),
+        gr.inputs.Number(default=1.0, label="parch"),
+        gr.inputs.Number(default=1.0, label="pclass"),
+        gr.inputs.Number(default=1.0, label="sex (0 for male, 1 for male)"),
+        gr.inputs.Number(default=1.0, label="sibsp")
+        ],
+    outputs=gr.Textbox())
+demo.launch()