Create app.py

app.py

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+import gradio as gr
+import numpy as np
+import pandas as pd
+from sklearn.datasets import fetch_california_housing
+from sklearn.linear_model import LinearRegression
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import mean_squared_error
+
+# Load dataset directly from sklearn (safe for Hugging Face Spaces)
+def load_data():
+    data = fetch_california_housing(as_frame=True, data_home="/tmp")
+    return data.frame
+
+# Train model once at startup
+def train_model():
+    df = load_data()
+    X = df.drop("MedHouseVal", axis=1)
+    y = df["MedHouseVal"]
+    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+    model = LinearRegression()
+    model.fit(X_train, y_train)
+    mse = mean_squared_error(y_test, model.predict(X_test))
+    return model, mse
+
+model, mse = train_model()
+
+# Prediction function
+def predict(MedInc, HouseAge, AveRooms, AveBedrms, Population, AveOccup, Latitude, Longitude):
+    input_array = np.array([[MedInc, HouseAge, AveRooms, AveBedrms, Population, AveOccup, Latitude, Longitude]])
+    prediction = model.predict(input_array)[0]
+    return f"Estimated Median House Value: ${prediction * 100000:.2f}"
+
+# Gradio UI
+inputs = [
+    gr.Number(label="Median Income"),
+    gr.Number(label="House Age"),
+    gr.Number(label="Average Rooms"),
+    gr.Number(label="Average Bedrooms"),
+    gr.Number(label="Population"),
+    gr.Number(label="Average Occupancy"),
+    gr.Number(label="Latitude"),
+    gr.Number(label="Longitude"),
+]
+
+iface = gr.Interface(
+    fn=predict,
+    inputs=inputs,
+    outputs="text",
+    title="🏠 California House Price Predictor",
+    description=f"Trained on California housing data. Model MSE: {mse:.4f}",
+)
+
+iface.launch()