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README.md

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 ---
-title: Titanic
+title: Iris
 emoji: 🐢
 colorFrom: purple
-colorTo: blue
+colorTo: green
 sdk: gradio
-sdk_version: 3.9.1
+sdk_version: 3.5
 app_file: app.py
 pinned: false
 license: apache-2.0

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, sibs, par_ch, fare, deck, embarked):
+    input_list = []
+    input_list.append(pclass)
+    input_list.append(sex)
+    input_list.append(age)
+    input_list.append(sibs)
+    input_list.append(par_ch)
+    input_list.append(fare)
+    input_list.append(deck)
+    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 Survival Predictive Analytics",
+    description="Experiment with passenger class, sex, age, siblings, parents/children, fare, deck, and embarked to predict if a hypothetical passenger survived titanic.",
+    allow_flagging="never",
+    inputs=[
+        gr.inputs.Number(default=0.0, label="Passenger class"),
+        gr.inputs.Number(default=0.0, label="Sex"),
+        gr.inputs.Number(default=0.0, label="Age"),
+        gr.inputs.Number(default=0.0, label="Siblings"),
+        gr.inputs.Number(default=0.0, label="Parents/Children"),
+        gr.inputs.Number(default=0.0, label="Fare"),
+        gr.inputs.Number(default=0.0, label="Deck"),
+        gr.inputs.Number(default=0.0, label="Embarked"),
+        ],
+    outputs=gr.inputs.Number(label="Survived"))
+
+demo.launch()
+

requirements.txt

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+hopsworks
+joblib
+scikit-learn