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import gradio as gr
import joblib
import numpy as np
import pandas as pd
# Load the model and unique brand values
model = joblib.load('model.joblib')
unique_values = joblib.load('unique_values.joblib')
brand_values = unique_values['Brand']
# Define the prediction function
def predict(brand, screen_size, resolution_width, resolution_height):
# Convert inputs to appropriate types
screen_size = float(screen_size)
resolution_width = int(resolution_width)
resolution_height = int(resolution_height)
# Prepare the input array for prediction
input_data = pd.DataFrame({
'Brand': [brand],
'Screen Size': [screen_size],
'Resolution (Width)': [resolution_width],
'Resolution (Height)': [resolution_height]
})
# Perform the prediction
prediction = model.predict(input_data)
return prediction[0]
# Create the Gradio interface
interface = gr.Interface(
fn=predict,
inputs=[
gr.Dropdown(choices=list(brand_values), label="Brand"),
gr.Textbox(label="Screen Size"),
gr.Textbox(label="Resolution (Width)"),
gr.Textbox(label="Resolution (Height)")
],
outputs="text",
title="Monitor Predictor",
description="Enter the brand, screen size, and resolution to predict the target value."
)
# Launch the app
interface.launch()
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