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fe4d81a | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 | import gradio as gr
import numpy as np
from miniml.linear_model import LinearRegression
from miniml.preprocessing.standard_scaler import StandardScaler
def train_demo_model():
rng = np.random.default_rng(4)
size = rng.uniform(600, 3200, 160)
bedrooms = rng.integers(1, 6, 160)
age = rng.uniform(0, 60, 160)
X = np.column_stack([size, bedrooms, age])
y = 50000 + 180 * size + 12000 * bedrooms - 900 * age
y = y + rng.normal(scale=25000, size=160)
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)
model = LinearRegression(learning_rate=0.05, epochs=800)
model.fit(X_scaled, y)
return model, scaler
MODEL, SCALER = train_demo_model()
def predict_price(size, bedrooms, age):
X = np.array([[size, bedrooms, age]])
X_scaled = SCALER.transform(X)
prediction = MODEL.predict(X_scaled)[0]
return f"${prediction:,.0f}"
demo = gr.Interface(
fn=predict_price,
inputs=[
gr.Slider(600, 3200, value=1600, step=50, label="Home size"),
gr.Slider(1, 6, value=3, step=1, label="Bedrooms"),
gr.Slider(0, 60, value=15, step=1, label="Home age"),
],
outputs=gr.Textbox(label="Predicted price"),
title="MiniML Linear Regression Demo",
description="A small Hugging Face Spaces demo using MiniML LinearRegression.",
)
if __name__ == "__main__":
demo.launch()
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