Upload app.py

app.py

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+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()