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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_model", version=6) | |
| model_dir = model.download() | |
| model = joblib.load(model_dir + "/titanic_modal.pkl") | |
| def titanic(pclass, sex, age, fare, embarked, familysize, appellation, cabin): | |
| input_list = [] | |
| # PClass | |
| input_list.append(int(pclass)) | |
| # Gender | |
| if sex == "Male": | |
| input_list.append(0) | |
| else: | |
| input_list.append(1) | |
| # Age | |
| input_list.append(age) | |
| # Fare | |
| input_list.append(fare) | |
| # Embarked | |
| if embarked == "S": | |
| input_list.append(0) | |
| elif embarked == "C": | |
| input_list.append(1) | |
| elif embarked == "Q": | |
| input_list.append(2) | |
| # Family Size | |
| input_list.append(familysize) | |
| # Appellation | |
| if appellation == "master": | |
| input_list.extend([1,0,0,0,0,0]) | |
| elif appellation == "miss": | |
| input_list.extend([0,1,0,0,0,0]) | |
| elif appellation == "mr": | |
| input_list.extend([0,0,1,0,0,0]) | |
| elif appellation == "mrs": | |
| input_list.extend([0,0,0,1,0,0]) | |
| elif appellation == "officer": | |
| input_list.extend([0,0,0,0,1,0]) | |
| elif appellation == "royalty": | |
| input_list.extend([0,0,0,0,0,1]) | |
| # Cabin | |
| if cabin == "A": | |
| input_list.extend([1,0,0,0,0,0,0,0,0]) | |
| elif cabin == "B": | |
| input_list.extend([0,1,0,0,0,0,0,0,0]) | |
| elif cabin == "C": | |
| input_list.extend([0,0,1,0,0,0,0,0,0]) | |
| elif cabin == "D": | |
| input_list.extend([0,0,0,1,0,0,0,0,0]) | |
| elif cabin == "E": | |
| input_list.extend([0,0,0,0,1,0,0,0,0]) | |
| elif cabin == "F": | |
| input_list.extend([0,0,0,0,0,1,0,0,0]) | |
| elif cabin == "G": | |
| input_list.extend([0,0,0,0,0,0,1,0,0]) | |
| elif cabin == "T": | |
| input_list.extend([0,0,0,0,0,0,0,1,0]) | |
| else: | |
| input_list.extend([0,0,0,0,0,0,0,0,1]) | |
| # 'res' is a list of predictions returned as the label. | |
| res = model.predict(np.asarray(input_list).reshape(1, -1)) | |
| res = res.astype(int) | |
| # We add '[0]' to the result of the transformed 'res', because 'res' is a list, and we only want | |
| # the first element. | |
| titanic_url = "https://github.com/Qinglin2000/ID2223/blob/main/" + str(res[0]) + ".png?raw=true" | |
| img = Image.open(requests.get(titanic_url, stream=True).raw) | |
| return img | |
| demo = gr.Interface( | |
| fn=titanic, | |
| title="Titanic Predictive Analytics", | |
| description="Experiment with titanic dataset values.", | |
| allow_flagging="never", | |
| inputs=[ | |
| gr.Dropdown(choices=["1", "2", "3"], label="PClass", value="1"), | |
| gr.Radio(choices=["Male", "Female"], label="Gender", value="Male"), | |
| gr.inputs.Number(default=30.0, label="Age"), | |
| gr.inputs.Number(default=40.99, label="Fare"), | |
| gr.Dropdown(choices=["S","C","Q"], label="Embarked", value="S"), | |
| gr.Number(label="Family Size", precision=0, value=1), | |
| gr.Dropdown(choices=["master", "miss", "mr", "mrs", "officer", "royalty"], label="Appellation", value="master"), | |
| gr.Dropdown(choices=["A", "B", "C", "D", "E", "F", "G", "T", "U"], label="Cabin", value="A"), | |
| ], | |
| outputs=gr.Image(type="pil")) | |
| demo.launch() |