added app.py

app.py

@@ -0,0 +1,57 @@
+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(Pclass, Age, SibSp, Parch, Fare, Sex_female, Sex_male, Embarked_C, Embarked_Q, Embarked_S):
+    input_list = []
+    input_list.append(Pclass)
+    input_list.append(Age)
+    input_list.append(SibSp)
+    input_list.append(Parch)
+    input_list.append(Fare)
+    input_list.append(Sex_female)
+    input_list.append(Sex_male)
+    input_list.append(Embarked_C)
+    input_list.append(Embarked_Q)
+    input_list.append(Embarked_S)
+    
+    # '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.
+    passenger_url = "https://raw.githubusercontent.com/GianlucaRub/Scalable-Machine-Learning-and-Deep-Learning/main/lab1/assets/" + res[0] + ".png"
+    img = Image.open(requests.get(passenger_url, stream=True).raw)            
+    return img
+        
+demo = gr.Interface(
+    fn=passenger,
+    title="Titanic Predictive Analytics",
+    description="Insert passenger class, age, number of sibilings/spouse on board of the Titanic, number of parents/children on board of the Titanic, fare, sex, port of embarkation and see if he/she survived ",
+    allow_flagging="never",
+    inputs=[
+        gr.inputs.Radio(choices=["First Class", "Second Class", "Third Class", label="Passenger Class"),
+        gr.inputs.Number(default=20, label="Age"),
+        gr.inputs.Number(default=1.0, label="Number of sibilings/spouse on board of the Titanic"),
+        gr.inputs.Number(default=1.0, label="Number of parents/children on board of the Titanic"),
+        gr.inputs.Number(defalut=10.0, label="Fare"),
+        gr.inputs.Radio(choices=["Male","Female"], label = "Sex"),
+        gr.inputs.Radio(choices=["Cherbourg","Queenstown","Southampton"], label = "Port of embarkation")
+        ],
+    outputs=gr.Image(type="pil"))
+
+demo.launch()
+

requirements.txt

@@ -0,0 +1,3 @@
+hopsworks
+joblib
+scikit-learn