Create app.py

app.py

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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()