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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
log_file = Path("logs/") / f"data_{uuid.uuid4()}.json"
log_folder = log_file.parent
scheduler = CommitScheduler(
repo_id="term-deposit-logs",
repo_type="dataset",
folder_path=log_folder,
path_in_repo="data",
every=2
)
term_deposit_predictor = joblib.load('model.joblib')
age_input = gr.Number(label="Age")
duration_input = gr.Number(label='Duration(Sec)')
cc_contact_freq_input = gr.Number(label='CC Contact Freq')
days_since_pc_input = gr.Number(label='Days Since PC')
pc_contact_freq_input = gr.Number(label='Pc Contact Freq')
job_input = gr.Dropdown(['admin.', 'blue-collar', 'technician', 'services', 'management',
'retired', 'entrepreneur', 'self-employed', 'housemaid', 'unemployed',
'student', 'unknown'],label="Job")
marital_input = gr.Dropdown(['married', 'single', 'divorced', 'unknown'],label='Marital Status')
education_input = gr.Dropdown(['experience', 'university degree', 'high school', 'professional.course',
'Others', 'illiterate'],label='Education')
defaulter_input = gr.Dropdown(['no', 'unknown', 'yes'],label='Defaulter')
home_loan_input = gr.Dropdown(['yes', 'no', 'unknown'],label='Home Loan')
personal_loan_input = gr.Dropdown(['yes', 'no', 'unknown'],label='Personal Loan')
communication_type_input = gr.Dropdown(['cellular', 'telephone'],label='Communication Type')
last_contacted_input = gr.Dropdown(['may', 'jul', 'aug', 'jun', 'nov', 'apr', 'oct', 'mar', 'sep', 'dec'],label='Last Contacted')
day_of_week_input = gr.Dropdown(['thu', 'mon', 'wed', 'tue', 'fri'],label='Day of Week')
pc_outcome_input = gr.Dropdown(['nonexistent', 'failure', 'success'], label='PC Outcome')
model_output = gr.Label(label="Subscribed")
def predict_term_deposit(age, duration, cc_contact_freq, days_since_pc, pc_contact_freq, job, marital_status, education,
defaulter, home_loan, personal_loan, communication_type, last_contacted,
day_of_week, pc_outcome):
sample = {
'Age': age,
'Duration(Sec)': duration,
'CC Contact Freq': cc_contact_freq,
'Days Since PC': days_since_pc,
'PC Contact Freq': pc_contact_freq,
'Job': job,
'Marital Status': marital_status,
'Education': education,
'Defaulter': defaulter,
'Home Loan': home_loan,
'Personal Loan': personal_loan,
'Communication Type': communication_type,
'Last Contacted': last_contacted,
'Day of Week': day_of_week,
'PC Outcome': pc_outcome,
}
data_point = pd.DataFrame([sample])
prediction = term_deposit_predictor.predict(data_point).tolist()
with scheduler.lock:
with log_file.open("a") as f:
f.write(json.dumps(
{
'Age': age,
'Duration(Sec)': duration,
'CC Contact Freq': cc_contact_freq,
'Days Since PC': days_since_pc,
'PC Contact Freq': pc_contact_freq,
'Job': job,
'Marital Status': marital_status,
'Education': education,
'Defaulter': defaulter,
'Home Loan': home_loan,
'Personal Loan': personal_loan,
'Communication Type': communication_type,
'Last Month Contacted': last_contacted,
'Day of Week': day_of_week,
'PC Outcome': pc_outcome,
'prediction': prediction[0]
}
))
f.write("\n")
return prediction[0]
demo = gr.Interface(
fn=predict_term_deposit,
inputs=[age_input,
duration_input,
cc_contact_freq_input,
days_since_pc_input,
pc_contact_freq_input,
job_input,
marital_input,
education_input,
defaulter_input,
home_loan_input,
personal_loan_input,
communication_type_input,
last_contacted_input,
day_of_week_input,
pc_outcome_input],
outputs=model_output,
title="Term Deposit Prediction",
description="This API allows you to predict the person who are going to likely subscribe the term deposit",
allow_flagging="auto",
concurrency_limit=8
)
demo.queue()
demo.launch(share=False) |