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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=1) | |
| model_dir = model.download() | |
| model = joblib.load(model_dir + "/titanic_model.pkl") | |
| def titanic(pclass, sex, age, fare): | |
| input_list = [] | |
| bins = [-np.infty, 20, 25, 29, 30, 40, np.infty] | |
| input_list.append(int(np.digitize([age], bins)[0])) | |
| input_list.append(int(sex)) | |
| input_list.append(int(pclass + 1)) | |
| input_list.append(fare) | |
| # '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. | |
| # print('The result we get :: ', str(res[0])) | |
| passenger_survival_url = "https://raw.githubusercontent.com/abdullabdull/id2223-images/main/" + str(res[0]) + ".png" | |
| img = Image.open(requests.get(passenger_survival_url, stream=True).raw) | |
| return img | |
| demo = gr.Interface( | |
| fn=titanic, | |
| title="Titanic Survival Predictive Analytics", | |
| description="Experiment with different passenger features to predict if they survived or not.", | |
| allow_flagging="never", | |
| inputs=[ | |
| gr.inputs.Dropdown(choices=["Class 1", "Class 2", "Class 3"], type="index", label="Pclass"), | |
| gr.inputs.Dropdown(choices=["Male", "Female"], type="index", label="Sex"), | |
| gr.inputs.Number(default=1, label="Age"), | |
| gr.inputs.Slider(minimum=0, maximum=550, default=50, label="Fare"), | |
| ], | |
| outputs=gr.Image(type="pil")) | |
| demo.launch() | |