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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()
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