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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(api_key_value="4CY1rwa8iz8Yu6gG.TwayrYmsX4GQfhSp3LNKYTLvyFMfqAvnzNUQp5ae9K5HhfYxb5mcnLAutm1K18zV") | |
| 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 passenger(pclass, sex, family, groupedage): | |
| input_list = [] | |
| input_list.append(pclass) | |
| input_list.append(sex) | |
| input_list.append(family) | |
| input_list.append(groupedage) | |
| # '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: | |
| passenger_url = "https://cdn.pixabay.com/photo/2018/08/02/18/58/survival-3580200_960_720.png" | |
| else: | |
| passenger_url = "https://pngimg.com/uploads/death/death_PNG55.png" | |
| img = Image.open(requests.get(passenger_url, stream=True).raw) | |
| return img | |
| #return res[0] | |
| demo_titanic = gr.Interface( | |
| fn=passenger, | |
| title="Titanic Predictive Analytics", | |
| description="Experiment to predict if a passenger survived or died in the titanic", | |
| allow_flagging="never", | |
| inputs=[ | |
| gr.inputs.Number(default=1.0, label="Pclass (Min:1, Max=3"), | |
| gr.inputs.Number(default=1.0, label="Sex (Female:0 and Male:1)"), | |
| gr.inputs.Number(default=1.0, label="Family (Number of family members in the boat[0,7])"), | |
| gr.inputs.Number(default=1.0, label="Age (Child:0, Adult:1 and Old:2)") | |
| ], | |
| outputs=gr.Image(type="pil")) | |
| #outputs=gr.Label(num_top_classes=2) | |
| demo_titanic.launch() | |