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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") | |
| dataset_api = project.get_dataset_api() | |
| dataset_api.download("Resources/images/deadImage.png") | |
| dataset_api.download("Resources/images/survivedImage.png") | |
| def titanic(PassengerId, Pclass, Numeric_sex, Age, Fare): | |
| input_list = [] | |
| input_list.append(PassengerId) | |
| input_list.append(Pclass) | |
| input_list.append(Numeric_sex) | |
| input_list.append(Age) | |
| 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. | |
| l = int(res) | |
| if(l == 0): | |
| img = Image.open("deadImage.png") | |
| else: | |
| img = Image.open("survivedImage.png") | |
| #flower_url = "https://raw.githubusercontent.com/featurestoreorg/serverless-ml-course/main/src/01-module/assets/" + res[0] + ".png" | |
| # img = Image.open("deadImage.png")#requests.get(flower_url, stream=True).raw) | |
| return img | |
| demo = gr.Interface( | |
| fn=titanic, | |
| title="Titanic Predictive Analytics", | |
| description="Experiment to predict which passenger will survived.", | |
| allow_flagging="never", | |
| inputs=[ | |
| gr.inputs.Number(default=100, label="PassengerId"), | |
| gr.inputs.Number(default=2, label="Pclass"), | |
| gr.inputs.Number(default=1, label="Numeric_sex"), | |
| gr.inputs.Number(default=20, label="Age"), | |
| gr.inputs.Number(default=30, label="Fare"), | |
| ], | |
| outputs=gr.Image(type="pil")) | |
| demo.launch() | |