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", version=1) model_dir = model.download() model = joblib.load(model_dir + "/titanic_model.pkl") CLASS_TO_VALUE = { "1st class": "1", "2nd class": "2", "3rd class": "3", } PORT_TO_VALUE = { "Cherbourg": "C", "Queenstown": "Q", "Southampton": "S", } def titanic(ticket_class, sex, port, fare, age, sibsp, parch): input_list = [] input_list.append(CLASS_TO_VALUE[ticket_class]) input_list.append(sex) input_list.append(PORT_TO_VALUE[port]) input_list.append(fare) input_list.append(age) input_list.append(int(sibsp)) input_list.append(int(parch)) # '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: url = "https://m.media-amazon.com/images/I/71M6k7ZQNcL._RI_.jpg" else: url = "https://thumbs.dreamstime.com/b/allvarlig-sten-med-skallen-34707626.jpg" img = Image.open(requests.get(url, stream=True).raw) return img demo = gr.Interface( fn=titanic, title="Titanic survival prediction", description="Experiment with parameters to predict if the fictional passenger survived", allow_flagging="never", inputs=[ gr.inputs.Dropdown(["1st class", "2nd class", "3rd class"], label="Ticket class"), gr.inputs.Dropdown(["female", "male"], label="Sex"), gr.inputs.Dropdown(["Cherbourg", "Queenstown", "Southampton"], label="Port of Embarkation"), gr.inputs.Number(default=50.0, label="Fare"), gr.inputs.Number(default=20.0, label="Age"), gr.inputs.Number(default=0, label="Number of siblings/spouses aboard the Titanic"), gr.inputs.Number(default=0, label="Number of parents/children aboard the Titanic"), ], outputs=gr.Image(type="pil")) demo.launch()