| 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() |
|
|
