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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
# Run the training script in the same directory
os.system("python train.py")
# Load the freshly trained model
machine_failure_predictor = joblib.load('model.joblib')
# Prepare the logging functionality
log_file = Path("logs/") / f"data_{uuid.uuid4()}.json"
log_folder = log_file.parent
scheduler = CommitScheduler(
repo_id="machine-failure-logs",
repo_type="dataset",
folder_path=log_folder,
path_in_repo="data",
every=2
)
# Set up UI components for input and output
air_temperature_input = gr.Number(label='Air temperature [K]')
process_temperature_input = gr.Number(label='Process temperature [K]')
rotational_speed_input = gr.Number(label='Rotational speed [rpm]')
torque_input = gr.Number(label='Torque [Nm]')
tool_wear_input = gr.Number(label='Tool wear [min]')
type_input = gr.Dropdown(
['L', 'M', 'H'],
label='Type'
)
model_output = gr.Label(label="Machine failure")
# Define the predict function that runs when 'Submit' is clicked or when a API request is made
def predict_machine_failure(air_temperature, process_temperature, rotational_speed, torque, tool_wear, type):
sample = {
'Air temperature [K]': air_temperature,
'Process temperature [K]': process_temperature,
'Rotational speed [rpm]': rotational_speed,
'Torque [Nm]': torque,
'Tool wear [min]': tool_wear,
'Type': type
}
data_point = pd.DataFrame([sample])
prediction = machine_failure_predictor.predict(data_point).tolist()
with scheduler.lock:
with log_file.open("a") as f:
f.write(json.dumps(
{
'Air temperature [K]': air_temperature,
'Process temperature [K]': process_temperature,
'Rotational speed [rpm]': rotational_speed,
'Torque [Nm]': torque,
'Tool wear [min]': tool_wear,
'Type': type,
'prediction': prediction[0]
}
))
f.write("\n")
return prediction[0]
# Create the interface
demo = gr.Interface(
fn=predict_machine_failure,
inputs=[air_temperature_input, process_temperature_input, rotational_speed_input,
torque_input, tool_wear_input, type_input],
outputs=model_output,
title="Machine Failure Predictor",
description="This API allows you to predict the machine failure status of an equipment",
allow_flagging="auto",
concurrency_limit=8
)
# Launch with a load balancer
demo.queue()
demo.launch(share=False) |