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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=10) | |
| model_dir = model.download() | |
| model = joblib.load(model_dir + "/titanic_model.pkl") | |
| def titanic(age, embarked, fare, parch, pclass, sex, sibsp): | |
| input_list = [] | |
| input_list.append(age) | |
| input_list.append(embarked) | |
| input_list.append(fare) | |
| input_list.append(parch) | |
| input_list.append(pclass) | |
| input_list.append(sex) | |
| input_list.append(sibsp) | |
| # '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. | |
| # flower_url = "https://raw.githubusercontent.com/featurestoreorg/serverless-ml-course/main/src/01-module/assets/" + res[0] + ".png" | |
| # img = Image.open(requests.get(flower_url, stream=True).raw) | |
| if res == [1]: | |
| res = 'survive' | |
| else: | |
| res = 'die' | |
| return res | |
| demo = gr.Interface( | |
| fn=titanic, | |
| title="Titanic Survivor Predictive Analytics", | |
| description="Experiment with age/embarked/fare/parch/pclass/sex/sibsp to predict if the passenger survived.", | |
| allow_flagging="never", | |
| inputs=[ | |
| gr.inputs.Number(default=2.0, label="age"), | |
| gr.inputs.Number(default=1.0, label="embarked (0 for S, 1 for C, 2 for Q)"), | |
| gr.inputs.Number(default=35.0, label="fare"), | |
| gr.inputs.Number(default=1.0, label="parch"), | |
| gr.inputs.Number(default=1.0, label="pclass"), | |
| gr.inputs.Number(default=1.0, label="sex (0 for male, 1 for male)"), | |
| gr.inputs.Number(default=1.0, label="sibsp") | |
| ], | |
| outputs=gr.Textbox()) | |
| demo.launch() |