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| import gradio as gr | |
| import numpy as np | |
| from PIL import Image | |
| import requests | |
| import pandas as pd | |
| 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) | |
| model_dir = model.download() | |
| model = joblib.load(model_dir + "/titanic_model.pkl") | |
| df = pd | |
| # features = pd.read_csv( | |
| # "https://raw.githubusercontent.com/Nathanotal/remoteFiles/main/titanicCleaned.csv") | |
| # features = features.drop(columns=["survived"]) | |
| # featureLabels = features.columns | |
| featureLabels = ["Pclass", "Name", "Sex", "Age", "SibSp", | |
| "Parch", "Ticket", "Fare", "Cabin", "Embarked"] | |
| def titanic(Pclass, Sex, Age, SibSp, Parch, Fare, Cabin, Embarked): | |
| input_list = [] | |
| sexToFeature = { | |
| "male": 0, | |
| "female": 1, | |
| } | |
| # Convert inputs to features | |
| input_list.append(Pclass) # Todo: Convert to feature | |
| input_list.append(sexToFeature.get(Sex)) # Todo: Convert to feature | |
| input_list.append(Age) # ! | |
| input_list.append(SibSp) # Todo: Convert to feature | |
| input_list.append(Parch) # Todo: Convert to feature | |
| input_list.append(Fare) # Todo: Convert to feature | |
| input_list.append(Cabin) # Todo: Convert to feature | |
| input_list.append(Embarked) # Todo: Convert to feature | |
| # '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. | |
| intLabelToText = {0: "Died", 1: "Survived"} | |
| age = input_list[0] | |
| gender = input_list[1] | |
| survived = res[0] | |
| survivedText = intLabelToText[survived] | |
| # Temp: | |
| age = 20 | |
| gender = "female" | |
| # Todo: survivor, "https://fakeface.rest/face/json?maximum_age=50&gender=female&minimum_age=49" | |
| generate_survivor_url = f'https://fakeface.rest/face/json?maximum_age={age}&gender={gender}&minimum_age={age}' | |
| randomized_face_url = requests.get( | |
| generate_survivor_url).json()["image_url"] | |
| survivor_url = randomized_face_url | |
| img = Image.open(requests.get(survivor_url, stream=True).raw) | |
| return img | |
| inputs = [] | |
| numericalInputs = ["age", "sibsp", "parch", "fare", "pclass"] | |
| worthlessInputs = ["name", "ticket"] | |
| categoricalInputs = ["sex", "embarked", "cabin"] | |
| for feature in featureLabels: | |
| if feature in numericalInputs: | |
| inputs.append(gr.inputs.Number(default=1.0, label=feature)) | |
| elif feature in worthlessInputs: | |
| pass | |
| # inputs.append(gr.Inputs.Textbox(default='text', label=feature)) | |
| elif feature in categoricalInputs: | |
| inputs.append(gr.inputs.Dropdown( | |
| choices=["a", "b"], default="a", label=feature)) | |
| else: | |
| raise Exception(f'Feature: "{feature}" not found') | |
| demo = gr.Interface( | |
| fn=titanic, | |
| title="Titanic Survivor Predictive Analytics", | |
| description="Experiment with person features to predict which survivor it is.", | |
| allow_flagging="never", | |
| inputs=inputs, | |
| outputs=gr.Image(type="pil")) | |
| demo.launch() | |