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| import gradio as gr | |
| import matplotlib.pyplot as plt | |
| import seaborn as sns | |
| import numpy as np | |
| from sklearn.datasets import fetch_california_housing | |
| from sklearn.model_selection import train_test_split | |
| from sklearn.linear_model import LinearRegression | |
| from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score | |
| # --- Step 1: Load dataset --- | |
| data = fetch_california_housing(as_frame=True) | |
| df = data.frame | |
| X = df.drop(columns=['MedHouseVal']) | |
| y = df['MedHouseVal'] | |
| feature_names = X.columns | |
| # --- Step 2: Split dataset --- | |
| X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) | |
| # --- Step 3: Train model --- | |
| model = LinearRegression() | |
| model.fit(X_train, y_train) | |
| # --- Step 4: Evaluate model --- | |
| y_pred = model.predict(X_test) | |
| mae = mean_absolute_error(y_test, y_pred) | |
| mse = mean_squared_error(y_test, y_pred) | |
| r2 = r2_score(y_test, y_pred) | |
| # --- Step 5: Graph 1 - Actual vs Predicted --- | |
| def plot_actual_vs_predicted(): | |
| fig, ax = plt.subplots(figsize=(5, 4)) | |
| ax.scatter(y_test, y_pred, alpha=0.5) | |
| ax.plot([y_test.min(), y_test.max()], [y_test.min(), y_test.max()], 'r--') | |
| ax.set_xlabel("Actual Prices") | |
| ax.set_ylabel("Predicted Prices") | |
| ax.set_title("Actual vs Predicted Prices") | |
| fig.canvas.draw() | |
| img = np.array(fig.canvas.renderer.buffer_rgba()) | |
| plt.close(fig) | |
| return img | |
| # --- Step 6: Graph 2 - Feature Importance --- | |
| def plot_feature_importance(): | |
| importance = np.abs(model.coef_) | |
| fig, ax = plt.subplots(figsize=(6, 4)) | |
| sns.barplot(x=importance, y=feature_names, palette="coolwarm") | |
| plt.title("Feature Importance (Linear Regression Coefficients)") | |
| fig.canvas.draw() | |
| img = np.array(fig.canvas.renderer.buffer_rgba()) | |
| plt.close(fig) | |
| return img | |
| # --- Step 7: Prediction Function --- | |
| def predict_price(MedInc, HouseAge, AveRooms, AveBedrms, Population, AveOccup, Latitude, Longitude): | |
| features = np.array([[MedInc, HouseAge, AveRooms, AveBedrms, Population, AveOccup, Latitude, Longitude]]) | |
| predicted_price = model.predict(features)[0] | |
| return f"🏡 Predicted Median House Value: ${predicted_price * 100000:.2f}" | |
| # --- Step 8: Gradio Interface --- | |
| with gr.Blocks() as demo: | |
| gr.Markdown("## 🏠 House Price Prediction with Graphs") | |
| gr.Markdown(f"**Model Performance:** R² = {r2:.2f}, MAE = {mae:.2f}, MSE = {mse:.2f}") | |
| with gr.Row(): | |
| MedInc = gr.Number(label="Median Income (10k USD)") | |
| HouseAge = gr.Number(label="House Age (years)") | |
| AveRooms = gr.Number(label="Average Rooms per House") | |
| AveBedrms = gr.Number(label="Average Bedrooms per House") | |
| Population = gr.Number(label="Population in Block") | |
| AveOccup = gr.Number(label="Average Occupancy") | |
| Latitude = gr.Number(label="Latitude") | |
| Longitude = gr.Number(label="Longitude") | |
| output_text = gr.Textbox(label="Prediction Result") | |
| predict_btn = gr.Button("🔍 Predict House Price") | |
| predict_btn.click( | |
| predict_price, | |
| inputs=[MedInc, HouseAge, AveRooms, AveBedrms, Population, AveOccup, Latitude, Longitude], | |
| outputs=output_text | |
| ) | |
| gr.Markdown("### 📊 Model Performance Graphs") | |
| gr.Image(value=plot_actual_vs_predicted(), label="Actual vs Predicted Prices") | |
| gr.Image(value=plot_feature_importance(), label="Feature Importance") | |
| # --- Step 9: Launch the app --- | |
| demo.launch() |