Update app.py

app.py

@@ -1,19 +1,17 @@
-# prompt: make a web app for house price using Deep Learning ang and dashbording using Gradio
-#!pip install scikit-learn --upgrade
 import pandas as pd
 from sklearn.model_selection import train_test_split
 from sklearn.preprocessing import StandardScaler
 from sklearn.neural_network import MLPRegressor
 from sklearn.metrics import mean_squared_error
 import gradio as gr
+import plotly.express as px
 
-# ... (Rest of your code)
 # Load the dataset
 df = pd.read_csv('california_housing_train.csv')
 
 # Select features and target
 features = df[['longitude', 'latitude', 'housing_median_age', 'total_rooms',
-       'total_bedrooms', 'population', 'households', 'median_income']]
+               'total_bedrooms', 'population', 'households', 'median_income']]
 target = df['median_house_value']
 
 # Split the data
@@ -33,29 +31,56 @@ predictions = model.predict(X_test_scaled)
 mse = mean_squared_error(y_test, predictions)
 print(f'Mean Squared Error: {mse}')
 
-# Create Gradio interface
+# Create prediction function
 def predict_house_price(longitude, latitude, housing_median_age, total_rooms,
-total_bedrooms, population, households, median_income):
-  input_data = scaler.transform([[longitude, latitude, housing_median_age, total_rooms,
-total_bedrooms, population, households, median_income]])
-  prediction = model.predict(input_data)[0]
-  return prediction
-
-iface = gr.Interface(
-  fn=predict_house_price,
-  inputs=[
-    gr.Number(label="Longitude"),  # Use gr.Number directly
-    gr.Number(label="Latitude"),
-    gr.Number(label="Housing Median Age"),
-    gr.Number(label="Total Rooms"),
-    gr.Number(label="Total Bedrooms"),
-    gr.Number(label="Population"),
-    gr.Number(label="Households"),
-    gr.Number(label="Median Income"),
-  ],
-  outputs="text",
-  title="House Price Prediction",
-  description="Enter the features to get the predicted house price."
+                        total_bedrooms, population, households, median_income):
+    input_data = scaler.transform([[longitude, latitude, housing_median_age, total_rooms,
+                                    total_bedrooms, population, households, median_income]])
+    prediction = model.predict(input_data)[0]
+    return prediction
+
+# Create dashboard function
+def create_dashboard():
+    fig1 = px.scatter(df, x='longitude', y='latitude', color='median_house_value',
+                      title="House Prices by Location",
+                      labels={'longitude': 'Longitude', 'latitude': 'Latitude', 'median_house_value': 'House Value'})
+    
+    fig2 = px.histogram(df, x='median_income', nbins=30, title="Distribution of Median Income",
+                        labels={'median_income': 'Median Income'})
+    
+    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
+
+# Gradio interface for prediction
+iface_predict = gr.Interface(
+    fn=predict_house_price,
+    inputs=[
+        gr.Number(label="Longitude"),  # Use gr.Number directly
+        gr.Number(label="Latitude"),
+        gr.Number(label="Housing Median Age"),
+        gr.Number(label="Total Rooms"),
+        gr.Number(label="Total Bedrooms"),
+        gr.Number(label="Population"),
+        gr.Number(label="Households"),
+        gr.Number(label="Median Income"),
+    ],
+    outputs="text",
+    title="House Price Prediction",
+    description="Enter the features to get the predicted house price."
 )
 
-iface.launch()
+# Gradio interface for dashboard
+iface_dashboard = gr.Interface(
+    fn=create_dashboard,
+    inputs=[],
+    outputs=[gr.Plot(), gr.Plot(), gr.Plot()],
+    title="House Price Dashboard",
+    description="Visualizations of the housing dataset."
+)
+
+# Launch both interfaces
+iface = gr.TabbedInterface([iface_predict, iface_dashboard], ["Prediction", "Dashboard"])
+
+iface.launch()