Add application file

app.py

@@ -0,0 +1,52 @@
+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=1)
+model_dir = model.download()
+model = joblib.load(model_dir + "/titanic_model.pkl")
+
+
+def passenger(passengerid, survived, pclass,age, sex, sibsp,parch):
+    input_list = []
+    input_list.append(passengerid)
+    input_list.append(survived)
+    input_list.append(pclass)
+    input_list.append(age)
+    input_list.append(sex)
+    input_list.append(sibsp)
+    input_list.append(parch)
+    # '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.
+    titanic_url = "https://raw.githubusercontent.com/AbyelT/ID2223-Scalable-ML-and-DL/main/Lab1/Titanic/assets/" + res[0] + ".png"
+    img = Image.open(requests.get(titanic_url, stream=True).raw)            
+    return img
+        
+demo = gr.Interface(
+    fn=passenger,
+    title="Titanic Survival Predictive Analytics",
+    #description="Experiment with sepal/petal lengths/widths to predict which flower it is.",
+    allow_flagging="never",
+    inputs=[
+        gr.inputs.Number(default=1.0, label="passengerid"),
+        gr.inputs.Number(default=1.0, label="survived"),
+        gr.inputs.Number(default=1.0, label="pclass"),
+        gr.inputs.Number(default=1.0, label="age"),
+        gr.inputs.Number(default=1.0, label="sex"),
+        gr.inputs.Number(default=1.0, label="sibsp"),
+        gr.inputs.Number(default=1.0, label="parch"),
+        ],
+    outputs=gr.Image(type="pil"))
+
+demo.launch()