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import numpy as np
import pandas as pd
from joblib import load
import gradio as gr
from tensorflow.keras.models import load_model
# filepaths
column_transform_path = "assets/column_transformer.joblib" # change during the creation of the huggingface app
model_path = "assets/insurance_regression_model.keras" # change during the creation of the huggingface app
# instance the models
column_transformer = load(column_transform_path)
insurance_model = load_model(model_path)
# short description of the stuff
title = "Insurance Regressor Model"
description = "POC of a deep learning regression model for estimating insurance charges based on 11 input variables."
article = "The model was implemented in tensorflow. It is simple as i made it as a refresher for reviewing the Tensorflow fundamentals (the problem most likely could be solved using classical regression). The dataset used was [Medical Cost Personal Datasets](https://www.kaggle.com/datasets/mirichoi0218/insurance)."
# processing function
def insurance_prediction(age,gender,bmi,children,smoker,region):
columns = ['age', 'sex', 'bmi', 'children', 'smoker', 'region']
# transform some data to the datatypes compatible with column transfomer
age = int(age)
gender = "male" if gender else "female"
children = int(children)
smoker = "yes" if smoker else "no"
region = region[0] if len(region) > 0 else 'northeast' # since it is a proof of concept, i left the value there.
# arange the values into a list
values = [age,gender,bmi,children,smoker,region]
# create a dictionary to structure the sample at hand
sample = {k:v for k,v in zip(columns,values)}
# create a dataframe using the sample
X = pd.DataFrame(data = pd.Series(sample)).T
# preprocess the dataframe
X_preprocessed = column_transformer.transform(X)
# make the prediction accordingly
y_pred = insurance_model.predict(X_preprocessed)
return y_pred[0][0]
demo = gr.Interface(
fn = insurance_prediction,
inputs = [
gr.Number(value = 40,label = "Age",show_label=True),
gr.Checkbox(value = False,label = "Male",show_label = True),
gr.Number(value = 30,label = "Body Mass Index",show_label=True),
gr.Number(value = 1,label = "Number of Children",show_label=True),
gr.Checkbox(value = False,label = "Smoker",show_label = True),
gr.CheckboxGroup(choices = ['northeast', 'northwest', 'southeast', 'southwest'],
value = ['northeast', 'northwest', 'southeast', 'southwest'],
label="Region",
info="Will only take into consideration the fist one selected.")
],
outputs = [
gr.Number(label = "Insurance Charges Estimate (American Dollars)")
],
title = title,
description = description,
article = article
)
demo.launch()
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