'gg'

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="otd1BvtKwvlF8OC1.Y8Kyt1QpZqDPMRNPIF3KvVGuFJpRdxIy39879ueQwymTgSDUU9vWKFMOnBqsyxfk")
+fs = project.get_feature_store()
+#q
+
+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, 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))
+    # We add '[0]' to the result of the transformed 'res', because 'res' is a list, and we only want
+    # the first element.
+    return res[0]
+
+        
+demo = gr.Interface(
+    fn=titanic,
+    title="Titanic Predictive Analytics",
+    description="Experiment to predict if a passenger survived the Titanic disaster",
+    allow_flagging="never",
+    inputs=[
+        gr.inputs.Number(default=1.0, label="PClass"),
+        gr.inputs.Number(default=1.0, label="Sex: Female = 0, Male = 1"),
+        gr.inputs.Number(default=1.0, label="Age"),
+        gr.inputs.Number(default=1.0, label="SibSp"),
+        gr.inputs.Number(default=1.0, label="Parch"),
+        gr.inputs.Number(default=1.0, label="Fare"),
+        gr.inputs.Number(default=1.0, label="Embarked: S = 0, C = 1, Q = 2"),
+        ],
+    outputs=gr.Textbox())
+
+demo.launch()
+