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cb7becb
1
Parent(s):
5b3270a
Add gradio for hugging spaces
Browse files- app.py +48 -0
- requirements.txt +3 -0
app.py
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import gradio as gr
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import numpy as np
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from PIL import Image
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import requests
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import hopsworks
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import joblib
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project = hopsworks.login()
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fs = project.get_feature_store()
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mr = project.get_model_registry()
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model = mr.get_model("titanic_modal", version=1)
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model_dir = model.download()
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model = joblib.load(model_dir + "/titanic_model.pkl")
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# "Pclass", "Sex", "Age", "Parch"
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def titanic(pclass, sex, age, parch):
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input_list = []
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input_list.append(pclass)
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input_list.append(sex)
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input_list.append(age)
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input_list.append(parch)
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# 'res' is a list of predictions returned as the label.
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res = model.predict(np.asarray(input_list).reshape(1, -1))
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# We add '[0]' to the result of the transformed 'res', because 'res' is a list, and we only want
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# the first element.
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# TODO(Change URL)
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flower_url = "https://raw.githubusercontent.com/featurestoreorg/serverless-ml-course/main/src/01-module/assets/" + res[0] + ".png"
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img = Image.open(requests.get(flower_url, stream=True).raw)
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return img
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demo = gr.Interface(
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fn=titanic,
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title="Titanic Passenger Survival Predictive Analytics",
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description="Experiment to predict passenger survival on the Titanic.",
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allow_flagging="never",
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inputs=[
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gr.inputs.Number(default=1.0, label="Pclass (1 -> 3)"),
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gr.inputs.Number(default=1.0, label="Sex (0 -> 1)"),
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gr.inputs.Number(default=1.0, label="Age (0 -> 100)"),
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gr.inputs.Number(default=1.0, label="Parch (0 -> 2)"),
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],
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outputs=gr.Image(type="pil"))
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demo.launch()
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requirements.txt
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hopsworks
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joblib
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scikit-learn
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