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from warnings import filterwarnings
filterwarnings('ignore')
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
# Configure the logging functionality
log_file = Path("logs/") / f"data_{uuid.uuid4()}.json"
log_folder = log_file.parent
repo_id = "operand-logs"
# Create a commit scheduler
scheduler = CommitScheduler(
repo_id=repo_id,
repo_type="dataset",
folder_path=log_folder,
path_in_repo="data",
every=2
)
# # Load the saved model
# #insurance_charge_predictor = joblib.load('model.joblib')
# # Define the input features
# #numeric_features = ['age', 'bmi', 'children']
# #categorical_features = ['sex', 'smoker', 'region']
# age_input = gr.Number(label="Age")
# bmi_input = gr.Number(label="BMI")
# children_input = gr.Number(label="Children")
# # sex: ['female' 'male']
# # smoker: ['yes' 'no']
# # region: ['southwest' 'southeast' 'northwest' 'northeast']
# sex_input = gr.Dropdown(['female','male'],label='Sex')
# smoker_input = gr.Dropdown(['yes','no'],label='Smoker')
# region_input = gr.Dropdown(['southwest', 'southeast', 'northwest', 'northeast'],label='Region')
# model_output = gr.Label(label="charges")
# 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
def dprocess(age, bmi, children, sex, smoker, region):
#Index(['age', 'sex', 'bmi', 'children', 'smoker', 'region'], dtype='object')
sample = {
'age': age,
'sex': sex,
'bmi': bmi,
'children': children,
'smoker': smoker,
'region': region
}
data_point = pd.DataFrame([sample])
prediction = insurance_charge_predictor.predict(data_point).tolist()
with scheduler.lock:
with log_file.open("a") as f:
f.write(json.dumps(
{
'age': age,
'sex': sex,
'bmi': bmi,
'children': children,
'smoker': smoker,
'region': region,
'prediction': prediction[0]
}
))
f.write("\n")
return prediction[0]
# Set-up the Gradio UI
textbox = gr.Textbox(label='Command:')
company = gr.Radio(label='Company:',
choices=["aws", "google", "IBM", "Meta", "msft"],
value="aws")
# Create Gradio interface
# For the inputs parameter of Interface provide [textbox,company] with outputs parameter of Interface provide prediction
demo = gr.Interface(fn=dprocess,
inputs=[textbox, company],
outputs="text",
title="operand data automation CLI",
description="",
theme=gr.themes.Soft())
demo.queue()
demo.launch() |