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app.py

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+# -*- coding: utf-8 -*-
+"""
+Created on Wed Nov 23 06:07:56 2022
+
+@author: limei
+"""
+
+
+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=6)
+model_dir = model.download()
+model = joblib.load(model_dir + "/titanic_model.pkl")
+
+
+def titanic(pclass, sex, fare, embarked, familysize, family, appellation, cabin):
+    input_list = []
+    # PClass
+    if pclass == "1":
+        input_list.extend([1,0,0])
+    elif pclass == "2":
+        input_list.extend([0,1,0])
+    elif pclass == "3":
+        input_list.extend([0,0,1])
+
+    # Gender
+    if sex == "Male":
+        input_list.append(0)
+    else:
+        input_list.append(1)
+
+    # Age
+    #input_list.append(age)
+
+    # Fare
+    input_list.append(fare)
+
+    # Embarked
+    if embarked == "S":
+        input_list.extend([1,0,0])
+    elif embarked == "C":
+        input_list.extend([0,1,0])
+    elif embarked == "Q":
+        input_list.extend([0,0,1])
+
+    # Family Size
+    input_list.append(familysize)
+    
+    if family == "Family_Single":
+        input_list.extend([1,0,0])
+    elif family == "Family_Small":
+        input_list.extend([0,1,0])
+    elif family == "Family_Large":
+        input_list.extend([0,0,1])
+    
+        
+
+    # Appellation
+    if appellation == "master":
+        input_list.extend([1,0,0,0,0,0])
+    elif appellation == "miss":
+        input_list.extend([0,1,0,0,0,0])
+    elif appellation == "mr":
+        input_list.extend([0,0,1,0,0,0])
+    elif appellation == "mrs":
+        input_list.extend([0,0,0,1,0,0])
+    elif appellation == "officer":
+        input_list.extend([0,0,0,0,1,0])
+    elif appellation == "royalty":
+        input_list.extend([0,0,0,0,0,1])
+
+    # Cabin
+    if cabin == "A":
+        input_list.extend([1,0,0,0,0,0,0,0,0])
+    elif cabin == "B":
+        input_list.extend([0,1,0,0,0,0,0,0,0])
+    elif cabin == "C":
+        input_list.extend([0,0,1,0,0,0,0,0,0])
+    elif cabin == "D":
+        input_list.extend([0,0,0,1,0,0,0,0,0])
+    elif cabin == "E":
+        input_list.extend([0,0,0,0,1,0,0,0,0])
+    elif cabin == "F":
+        input_list.extend([0,0,0,0,0,1,0,0,0])
+    elif cabin == "G":
+        input_list.extend([0,0,0,0,0,0,1,0,0])
+    elif cabin == "T":
+        input_list.extend([0,0,0,0,0,0,0,1,0])
+    else:
+        input_list.extend([0,0,0,0,0,0,0,0,1])
+
+
+    # 'res' is a list of predictions returned as the label.
+    res = model.predict(np.asarray(input_list).reshape(1, -1))
+    res = res.astype(int)
+    # We add '[0]' to the result of the transformed 'res', because 'res' is a list, and we only want
+    # the first element.
+    titanic_url = "https://raw.githubusercontent.com/M75583/Machinelearning/main/" + str(res[0]) + ".png"
+    #titanic_url = "https://github.com/Qinglin2000/ID2223/blob/main/" + str(res[0]) + ".png?raw=true"
+    img = Image.open(requests.get(titanic_url, stream=True).raw)
+    return img
+
+
+demo = gr.Interface(
+    fn=titanic,
+    title="Titanic Predictive Analytics",
+    description="Experiment with titanic dataset values.",
+    allow_flagging="never",
+    inputs=[
+        gr.Dropdown(choices=["1", "2", "3"], label="PClass", value="1"),
+        gr.Radio(choices=["Male", "Female"], label="Gender",  value="Male"),
+        #gr.inputs.Number(default=30.0, label="Age"),
+        gr.inputs.Number(default=40.99, label="Fare"),
+        gr.Dropdown(choices=["S","C","Q"], label="Embarked",  value="S"),
+        gr.Number(label="Family Size", precision=0, value=1),
+        gr.Dropdown(choices=["Family_Single","Family_Small","Family_Large"], label="Family", value="Family_Single"),
+        gr.Dropdown(choices=["master", "miss", "mr", "mrs", "officer", "royalty"], label="Appellation", value="master"),
+        gr.Dropdown(choices=["A", "B", "C", "D", "E", "F", "G", "T", "U"], label="Cabin", value="A"),
+    ],
+    outputs=gr.Image(type="pil"))
+demo.launch()
+
+

requirements.txt

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+hopsworks
+joblib
+scikit-learn