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| import gradio as gr | |
| import numpy as np | |
| from PIL import Image | |
| import requests | |
| import hopsworks | |
| import joblib | |
| import pandas as pd | |
| 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,age,sibsp,parch,fare,sex,embarked): | |
| input_list = [] | |
| input_list.append(pclass) | |
| 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) | |
| elif sex == "female": | |
| input_list.append(1) | |
| input_list.append(0) | |
| if embarked == "C": | |
| input_list.append(1) | |
| input_list.append(0) | |
| input_list.append(0) | |
| input_list.append(0) | |
| elif embarked == "Q": | |
| input_list.append(0) | |
| input_list.append(1) | |
| input_list.append(0) | |
| input_list.append(0) | |
| elif embarked == "S": | |
| input_list.append(0) | |
| input_list.append(0) | |
| input_list.append(1) | |
| input_list.append(0) | |
| elif embarked == "Unknown": | |
| input_list.append(0) | |
| input_list.append(0) | |
| input_list.append(0) | |
| input_list.append(1) | |
| # input_df = pd.DataFrame(data=input_list, columns = ['Pclass', 'Age', 'SibSp', 'Parch', | |
| # 'Fare', 'Sex_female','Sex_male', | |
| # 'Embarked_C', 'Embarked_Q', 'Embarked_S', | |
| # 'Embarked_Unknown']) | |
| # '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. | |
| if res[0] == 1: | |
| res_str = "survivor" | |
| else: | |
| res_str = "victim" | |
| passenger_url = "https://raw.githubusercontent.com/daniel-rdt/serverless_ml_titanic_dr/main/assets/" + res_str + ".png" | |
| img = Image.open(requests.get(passenger_url, stream=True).raw) | |
| return img | |
| # if res[0] == 1: | |
| # return "The passenger is predicted to be a survivor." | |
| # else: | |
| # return "The passenger is predicted to be a victim." | |
| demo = gr.Interface( | |
| fn=titanic, | |
| title="Titanic Passenger Predictive Analytics", | |
| description="Experiment with passenger data to predict whether the passenger is a survivor or not.", | |
| allow_flagging="never", | |
| inputs=[ | |
| gr.inputs.Number(default=2, label="Passenger class (choose from either 1, 2 or 3)"), | |
| gr.inputs.Number(default=30, label="Age in full years (if child younger than 1 round up to 1)"), | |
| gr.inputs.Number(default=1, label="Number of siblings or spouses"), | |
| gr.inputs.Number(default=0, label="Number of parents or children"), | |
| gr.inputs.Number(default=100, label="Fare (cost between 0 and 513)"), | |
| gr.inputs.Textbox(default="male", label="Sex (choose from either male or female)"), | |
| gr.inputs.Textbox(default="Unknown", label="Embarked (choose from either C, Q, S or Unknown)"), | |
| ], | |
| # outputs=gr.outputs.Textbox()) | |
| outputs=gr.Image(type="pil")) | |
| demo.launch() |