Upload app.py

app.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", 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.
+    survival_url = "https://raw.githubusercontent.com/Yasaman97/ID2223-Titanic-Yasaman/main/img/" + str(res[0]) + ".jpg"
+    img = Image.open(requests.get(survival_url, stream=True).raw)            
+    return img
+        
+demo = gr.Interface(
+    fn=titanic,
+    title="Titanic Survival Analytics",
+    description="Experiment with Passenger class, Sex, Age, SibSp, Parch, Fare, and Embarked to predict Survival.",
+    allow_flagging="never",
+    inputs=[
+        gr.inputs.Number(default=1.0, label="Pclass (int 1-3)"),
+        gr.inputs.Number(default=1.0, label="Sex (1: female, 0: male)"),
+        gr.inputs.Number(default=1.0, label="Age (float (in the dataset: 1-80))"),
+        gr.inputs.Number(default=1.0, label="SibSp (int 0-3)"),
+        gr.inputs.Number(default=1.0, label="Parch (int 0-4)"),
+        gr.inputs.Number(default=1.0, label="Fare (float (in the dataset: 0-512)"),
+        gr.inputs.Number(default=1.0, label="Embarked (int 0-2)"),
+        ],
+    outputs=gr.Image(type="pil"))
+
+demo.launch()
+