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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) | |
| EVALUATION_METRIC="accuracy" | |
| SORT_METRICS_BY="max" # your sorting criteria | |
| # get best model based on custom metrics | |
| best_model = mr.get_best_model("titanic_modal", | |
| EVALUATION_METRIC, | |
| SORT_METRICS_BY) | |
| model = best_model | |
| model_dir = model.download() | |
| model = joblib.load(model_dir + "/titanic_model.pkl") | |
| def passenger(Pclass, Age, SibSp, Parch, Fare, Sex, Embarked): | |
| input_list = [] | |
| if Pclass == "First Class": | |
| input_list.append(1) | |
| elif Pclass == "Second Class": | |
| input_list.append(2) | |
| else: | |
| input_list.append(3) | |
| input_list.append(Age) | |
| input_list.append(SibSp) | |
| input_list.append(Parch) | |
| input_list.append(Fare) | |
| if Sex == "Male": | |
| input_list.append(0) | |
| input_list.append(1) | |
| else: | |
| input_list.append(1) | |
| input_list.append(0) | |
| if Embarked == "Cherbourg": | |
| input_list.append(1) | |
| input_list.append(0) | |
| input_list.append(0) | |
| elif Embarked == "Queenstown": | |
| input_list.append(0) | |
| input_list.append(1) | |
| input_list.append(0) | |
| else: | |
| input_list.append(0) | |
| input_list.append(0) | |
| input_list.append(1) | |
| # 'res' is a list of predictions returned as the label. | |
| res = model.predict(np.asarray(input_list).reshape(1, -1)) | |
| res = str(res[0]) | |
| # We add '[0]' to the result of the transformed 'res', because 'res' is a list, and we only want | |
| # the first element. | |
| passenger_url = "https://raw.githubusercontent.com/GianlucaRub/Scalable-Machine-Learning-and-Deep-Learning/main/Lab1/assets/" + res + ".png" | |
| img = Image.open(requests.get(passenger_url, stream=True).raw) | |
| return img | |
| demo = gr.Interface( | |
| fn=passenger, | |
| title="Titanic Predictive Analytics", | |
| description="Insert passenger class, age, number of sibilings/spouse on board of the Titanic, number of parents/children on board of the Titanic, fare, sex, port of embarkation and see if he/she survived ", | |
| allow_flagging="never", | |
| inputs=[ | |
| gr.inputs.Radio(choices=["First Class", "Second Class", "Third Class"], label="Passenger Class"), | |
| gr.inputs.Number(default=20, label="Age (years)"), | |
| gr.inputs.Number(default=1.0, label="Number of sibilings/spouse on board of the Titanic"), | |
| gr.inputs.Number(default=1.0, label="Number of parents/children on board of the Titanic"), | |
| gr.inputs.Number(default=10.0, label="Fare (USD)"), | |
| gr.inputs.Radio(choices=["Male","Female"], label = "Sex"), | |
| gr.inputs.Radio(choices=["Cherbourg","Queenstown","Southampton"], label = "Port of embarkation") | |
| ], | |
| outputs=gr.Image(type="pil")) | |
| demo.launch() | |