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d4c8ac6 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 | # Import the libraries
import os
import uuid
import joblib
import json
import gradio as gr
import pandas as pd
from huggingface_hub import CommitScheduler
from pathlib import Path
# Prepare the logging functionality
log_file = Path("logs/") / f"data_{uuid.uuid4()}.json"
log_folder = log_file.parent
# scheduler = CommitScheduler(
# repo_id="insurance-charge-logs",
# repo_type="dataset",
# folder_path=log_folder,
# path_in_repo="data",
# every=2
# )
Load the freshly trained model from disk
machine_insurance_predictor = joblib.load('model.joblib')
age_input = gr.Number(label='Age')
bmi_input = gr.Number(label='BMI')
children_input = gr.Number(label='Children')
sex_input = gr.Dropdown(
['male', 'female'],
label='Sex'
)
smoker_input = gr.Dropdown(
['yes', 'no'],
label='Smoker'
)
region_input = gr.Dropdown(
['northeast', 'northwest', 'southeast', 'southwest'],
label='Region'
)
model_output = gr.Label(label="insurance charge")
# Define the predict function which will take features, convert to dataframe and make predictions using the saved model
# the functions runs when 'Submit' is clicked or when a API request is made
# While the prediction is made, log both the inputs and outputs to a log file
# While writing to the log file, ensure that the commit scheduler is locked to avoid parallel access
def predict_insurance_charge(age, bmi, children, sex, smoker, region):
sample = {
'Age': age,
'BMI': bmi,
'Children': children,
'Sex': sex,
'Smoker': smoker,
'Region': region,
}
data_point = pd.DataFrame([sample])
prediction = machine_insurance_predictor.predict(data_point).tolist()
# with scheduler.lock:
# with log_file.open("a") as f:
# f.write(json.dumps(
# {
# 'Age': age,
# 'BMI': bmi,
# 'Children': children,
# 'Sex': sex,
# 'Smoker': smoker,
# 'Region': region,
# 'prediction': prediction[0]
# }
# ))
# f.write("\n")
return prediction[0]
# Create the gradio interface, make title "HealthyLife Insurance Charge Prediction"
# Set up UI components for input and output
demo = gr.Interface(
fn=predict_insurance_charge,
inputs=[age_input, bmi_input, children_input, sex_input, smoker_input, region_input],
outputs=model_output,
title="Insurance Charge Predictor",
description="This API allows you to predict the companies insurance charges",
allow_flagging="auto",
concurrency_limit=8
)
# Launch with a load balancer
demo.queue()
demo.launch(share=False)
# Run the training script placed in the same directory as app.py
# The training script will train and persist a linear regression model with the filename 'model.joblib' |