Update app.py

app.py

@@ -5,6 +5,7 @@ from sklearn.neural_network import MLPRegressor
 from sklearn.metrics import mean_squared_error
 import gradio as gr
 import plotly.express as px
+import plotly.graph_objects as go
 
 # Load the dataset
 df = pd.read_csv('california_housing_train.csv')
@@ -22,15 +23,25 @@ scaler = StandardScaler()
 X_train_scaled = scaler.fit_transform(X_train)
 X_test_scaled = scaler.transform(X_test)
 
+# Initialize lists to store loss metrics
+training_losses = []
+validation_losses = []
+
+# Custom MLPRegressor class to capture loss metrics
+class CustomMLPRegressor(MLPRegressor):
+    def _fit(self, X, y, incremental):
+        result = super()._fit(X, y, incremental)
+        training_loss = self.loss_
+        predictions = self.predict(X_test_scaled)
+        validation_loss = mean_squared_error(y_test, predictions)
+        training_losses.append(training_loss)
+        validation_losses.append(validation_loss)
+        return result
+
 # Train the model
-model = MLPRegressor(hidden_layer_sizes=(100,), activation='relu', solver='adam', max_iter=1000)
+model = CustomMLPRegressor(hidden_layer_sizes=(100,), activation='relu', solver='adam', max_iter=1000)
 model.fit(X_train_scaled, y_train)
 
-# Evaluate the model
-predictions = model.predict(X_test_scaled)
-mse = mean_squared_error(y_test, predictions)
-print(f'Mean Squared Error: {mse}')
-
 # Create prediction function
 def predict_house_price(longitude, latitude, housing_median_age, total_rooms,
                         total_bedrooms, population, households, median_income):
@@ -51,7 +62,12 @@ def create_dashboard():
     fig3 = px.histogram(df, x='housing_median_age', nbins=30, title="Distribution of Housing Median Age",
                         labels={'housing_median_age': 'Housing Median Age'})
     
-    return fig1, fig2, fig3
+    fig4 = go.Figure()
+    fig4.add_trace(go.Scatter(y=training_losses, mode='lines', name='Training Loss'))
+    fig4.add_trace(go.Scatter(y=validation_losses, mode='lines', name='Validation Loss'))
+    fig4.update_layout(title="Model Loss Over Time", xaxis_title="Epoch", yaxis_title="Loss")
+    
+    return fig1, fig2, fig3, fig4
 
 # Gradio interface for prediction
 iface_predict = gr.Interface(
@@ -75,9 +91,9 @@ iface_predict = gr.Interface(
 iface_dashboard = gr.Interface(
     fn=create_dashboard,
     inputs=[],
-    outputs=[gr.Plot(), gr.Plot(), gr.Plot()],
+    outputs=[gr.Plot(), gr.Plot(), gr.Plot(), gr.Plot()],
     title="House Price Dashboard",
-    description="Visualizations of the housing dataset."
+    description="Visualizations of the housing dataset and model performance."
 )
 
 # Launch both interfaces