Init

.hw_api_key

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+Ri2FZ9IsPDNHnuly.uYd6MFsZTxbZSQ23ssaVzdzQtJNAS8fBMQ5owgIM9Wr5hUQ7Qj7Yhe7wOtPOnIDp

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_passanger(age, sex, sibsp, parch, fare, embarked, pclass):
+    input_list = []
+    sex_value = 1 if sex=='female' else 0
+    pclass_value = int(pclass)
+    if embarked == 'S':
+        embarked_value = 0
+    elif embarked == 'C':
+        embarked_value = 1
+    else:
+        embarked_value = 2
+    input_list.append(pclass_value)
+    input_list.append(sex_value)
+    input_list.append(age)
+    input_list.append(sibsp)
+    input_list.append(parch)
+    input_list.append(fare)
+    input_list.append(embarked_value)
+
+
+    # '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.
+    img_urls=["https://uxwing.com/wp-content/themes/uxwing/download/health-sickness-organs/skull-icon.png", "https://uxwing.com/wp-content/themes/uxwing/download/emoji-emoticon/happy-icon.png"]
+    img_url  = img_urls[res[0]]
+    img = Image.open(requests.get(img_url, stream=True).raw)
+    return img
+        
+demo = gr.Interface(
+    fn=titanic_passanger,
+    title="Titanic Survivor Predictive Analytics",
+    description="Experiment with the features to predict survivor status.",
+    allow_flagging="never",
+    inputs=[
+        gr.inputs.Number(default=22.0, label="Age"),
+        gr.inputs.Radio(['female', 'male'], label="Sex"),
+
+        gr.inputs.Number(default=1.0, label="Number of siblings and spouses aboard"),
+        gr.inputs.Number(default=1.0, label="Number of parents and children aboard"),
+
+        gr.inputs.Number(default=1.0, label="Fare"),
+
+        gr.inputs.Radio(['S', 'C', 'Q'], label="Port embarked"),
+        gr.inputs.Radio(['1', '2', '3'], label="Ticket class"),
+    ],
+    outputs=gr.Image(type="pil"))
+
+demo.launch()
+

requirements.txt

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+hopsworks
+joblib
+scikit-learn