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README.md

@@ -1,8 +1,8 @@
 ---
 title: Titanic
-emoji: 📚
-colorFrom: indigo
-colorTo: pink
+emoji: 🚢
+colorFrom: green
+colorTo: red
 sdk: gradio
 sdk_version: 3.10.1
 app_file: 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=2)
+model_dir = model.download()
+model = joblib.load(model_dir + "/titanic_model.pkl")
+
+
+def titanic(pclass, sex, age_bin, fare_bin):
+    input_list = []
+    input_list.append(pclass)
+    input_list.append(sex)
+    input_list.append(age_bin)
+    input_list.append(fare_bin)
+    # 'res' is a list of predictions returned as the label.
+    res = model.predict(np.asarray(input_list).reshape(1, -1)) 
+    res_0 = str(res[0])
+    # We add '[0]' to the result of the transformed 'res', because 'res' is a list, and we only want 
+    # the first element.
+    prediction_url = "https://raw.githubusercontent.com/torileatherman/serverless_ml_titanic/main/src/assets/"+res_0+".png"
+    img = Image.open(requests.get(prediction_url, stream=True).raw)            
+    return img
+        
+demo = gr.Interface(
+    fn=titanic,
+    title="Titanic Survival Predictive Analytics",
+    description="Experiment with class, sex, age, and fare type to predict if the passenger survived",
+    allow_flagging="never",
+    inputs=[
+        gr.inputs.Number(default=1, label="Class (1 is highest, 3 is lowest"),
+        gr.inputs.Number(default=1, label="Gender (0 is male, 1 is female)"),
+        gr.inputs.Number(default=20, label="Age (years)"),
+        gr.inputs.Number(default=1, label="Fare Type (1 is lowest, 4 is highest)"),
+        ],
+    outputs=gr.Image(type="pil"))
+
+demo.launch(share=True)

requirements.txt

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+hopsworks
+joblib
+scikit-learn