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import gradio as gr
import joblib
import numpy as np
# Load saved model and scaler
model = joblib.load("best_svr_model.pkl")
scaler = joblib.load("scaler.pkl")
def predict_wear(load, particle_size, sliding_speed):
input_data = np.array([[load, particle_size, sliding_speed]])
scaled_input = scaler.transform(input_data)
prediction = model.predict(scaled_input)
return round(prediction[0], 4)
iface = gr.Interface(
fn=predict_wear,
inputs=[
gr.Number(label="Load (N)"),
gr.Number(label="Particle Size (μm)"),
gr.Number(label="Sliding Speed (mm/s)")
],
outputs=gr.Number(label="Predicted Volume Loss (mm³/s)"),
title="Wear Performance Prediction",
description="SVR Model to predict wear volume loss based on input parameters."
)
iface.launch() |