Upload main.py

main.py

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+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_more_specs_grad_boosted", version=1)
+model_dir = model.download()
+model = joblib.load(model_dir + "/titanic_model.pkl")
+
+
+def titanic(pclass,sex,age,sibsp,parch,embarked,fare_per_customer,embarked_remapped,cabin_remapped):
+    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(embarked)
+    input_list.append(fare_per_customer)
+    input_list.append(embarked_remapped)
+    input_list.append(cabin_remapped)
+    # '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.
+
+demo = gr.Interface(
+    fn=titanic,
+    title="Titanic Predictive Analytics",
+    description="Predict survivals.",
+    allow_flagging="never",
+    inputs=[
+        gr.inputs.Number(default=1.0, label="pclass"),
+        gr.inputs.Number(default=1.0, label="gender(male=0, female=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="embarked(C=1,S=2,Q=3)"),
+        gr.inputs.Number(default=1.0, label="fare_per_customer"),
+        gr.inputs.Number(default=1.0, label="cabin_remapped(if the passanger has one cabin =1, else =0)"),
+
+    ],
+    outputs=gr.Image(type="pil"))
+
+demo.launch()