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)