Update app.py

app.py

@@ -6,23 +6,6 @@ from utils import create_new_features, normalize, init_new_pred
 with open('./trained_model.pkl', 'rb') as file:
     model = pickle.load(file)
 
-# Placeholder for displaying the predicted price at the top
-price_placeholder = st.empty()
-
-city = st.selectbox(
-    'Select City', 
-    ['Algona', 'Auburn', 'Beaux Arts Village', 'Bellevue',
-     'Black Diamond', 'Bothell', 'Burien', 'Carnation', 'Clyde Hill',
-     'Covington', 'Des Moines', 'Duvall', 'Enumclaw', 'Fall City',
-     'Federal Way', 'Inglewood-Finn Hill', 'Issaquah', 'Kenmore',
-     'Kent', 'Kirkland', 'Lake Forest Park', 'Maple Valley', 'Medina',
-     'Mercer Island', 'Milton', 'Newcastle', 'Normandy Park',
-     'North Bend', 'Pacific', 'Preston', 'Ravensdale', 'Redmond',
-     'Renton', 'Sammamish', 'SeaTac', 'Seattle', 'Shoreline',
-     'Skykomish', 'Snoqualmie', 'Snoqualmie Pass', 'Tukwila', 'Vashon',
-     'Woodinville', 'Yarrow Point']
-)
-
 # Define min and max values from the dictionaries
 min_dict = {
     'bedrooms': 0,
@@ -57,56 +40,89 @@ max_dict = {
     'house_age': 114,
     'years_since_renovation': 2014
 }
+    
+# Display the predicted price at the top of the app
+price_placeholder.write(f"Predicted Price: ${predicted_price[0][0]:,.2f}")
 
-# Create sliders for each item in the dictionaries
-bedrooms = st.slider('Bedrooms', min_value=min_dict['bedrooms'], max_value=max_dict['bedrooms'], value=min_dict['bedrooms'])
-bathrooms = st.slider('Bathrooms', min_value=min_dict['bathrooms'], max_value=max_dict['bathrooms'], value=min_dict['bathrooms'])
-sqft_living = st.slider('Square Feet (Living)', min_value=min_dict['sqft_living'], max_value=max_dict['sqft_living'], value=min_dict['sqft_living'])
-sqft_lot = st.slider('Square Feet (Lot)', min_value=min_dict['sqft_lot'], max_value=max_dict['sqft_lot'], value=min_dict['sqft_lot'])
-floors = st.slider('Floors', min_value=min_dict['floors'], max_value=max_dict['floors'], value=min_dict['floors'])
-waterfront = st.checkbox('Waterfront', value=False)
-view = st.slider('View', min_value=min_dict['view'], max_value=max_dict['view'], value=min_dict['view'])
-condition = st.slider('Condition', min_value=min_dict['condition'], max_value=max_dict['condition'], value=min_dict['condition'])
-sqft_above = st.slider('Square Feet (Above)', min_value=min_dict['sqft_above'], max_value=max_dict['sqft_above'], value=min_dict['sqft_above'])
-sqft_basement = st.slider('Square Feet (Basement)', min_value=min_dict['sqft_basement'], max_value=max_dict['sqft_basement'], value=min_dict['sqft_basement'])
-yr_built = st.slider('Year Built', min_value=min_dict['yr_built'], max_value=max_dict['yr_built'], value=min_dict['yr_built'])
-yr_renovated = st.slider('Year Renovated', min_value=min_dict['yr_renovated'], max_value=max_dict['yr_renovated'], value=min_dict['yr_renovated'])
+# Create two columns: one for the city and one for the map
+col1, col2 = st.columns([1, 2])  # Adjust the width ratios as needed
 
-if (bedrooms != min_dict['bedrooms'] or
-    bathrooms != min_dict['bathrooms'] or
-    sqft_living != min_dict['sqft_living'] or
-    sqft_lot != min_dict['sqft_lot'] or
-    floors != min_dict['floors'] or
-    waterfront != min_dict['waterfront'] or
-    view != min_dict['view'] or
-    condition != min_dict['condition'] or
-    sqft_above != min_dict['sqft_above'] or
-    sqft_basement != min_dict['sqft_basement'] or
-    yr_built != min_dict['yr_built'] or
-    yr_renovated != min_dict['yr_renovated']):
+# Display city dropdown in the first column
+with col1:
+    # Placeholder for displaying the predicted price at the top
+    price_placeholder = st.empty()
     
-    new_pred = init_new_pred()
-    new_pred['bedrooms'] = bedrooms
-    new_pred['bathrooms'] = bathrooms
-    new_pred['sqft_living'] = sqft_living
-    new_pred['sqft_lot'] = sqft_lot
-    new_pred['floors'] = floors
-    new_pred['waterfront'] = int(waterfront)
-    new_pred['view'] = view
-    new_pred['condition'] = condition
-    new_pred['sqft_above'] = sqft_above
-    new_pred['sqft_basement'] = sqft_basement
-    new_pred['yr_built'] = yr_built
-    new_pred['yr_renovated'] = yr_renovated
-    new_pred[f'city_{city}'] = 1
+    st.subheader('City Selection')
+    city = st.selectbox(
+        'Select City', 
+        ['Algona', 'Auburn', 'Beaux Arts Village', 'Bellevue',
+         'Black Diamond', 'Bothell', 'Burien', 'Carnation', 'Clyde Hill',
+         'Covington', 'Des Moines', 'Duvall', 'Enumclaw', 'Fall City',
+         'Federal Way', 'Inglewood-Finn Hill', 'Issaquah', 'Kenmore',
+         'Kent', 'Kirkland', 'Lake Forest Park', 'Maple Valley', 'Medina',
+         'Mercer Island', 'Milton', 'Newcastle', 'Normandy Park',
+         'North Bend', 'Pacific', 'Preston', 'Ravensdale', 'Redmond',
+         'Renton', 'Sammamish', 'SeaTac', 'Seattle', 'Shoreline',
+         'Skykomish', 'Snoqualmie', 'Snoqualmie Pass', 'Tukwila', 'Vashon',
+         'Woodinville', 'Yarrow Point']
+    )
 
-    # Process the prediction
-    new_pred = pd.DataFrame([new_pred])
-    new_pred = create_new_features(new_pred)
-    new_pred = normalize(new_pred)
+    # Create sliders for each item in the dictionaries
+    bedrooms = st.slider('Bedrooms', min_value=min_dict['bedrooms'], max_value=max_dict['bedrooms'], value=min_dict['bedrooms'])
+    bathrooms = st.slider('Bathrooms', min_value=min_dict['bathrooms'], max_value=max_dict['bathrooms'], value=min_dict['bathrooms'])
+    sqft_living = st.slider('Square Feet (Living)', min_value=min_dict['sqft_living'], max_value=max_dict['sqft_living'], value=min_dict['sqft_living'])
+    sqft_lot = st.slider('Square Feet (Lot)', min_value=min_dict['sqft_lot'], max_value=max_dict['sqft_lot'], value=min_dict['sqft_lot'])
+    floors = st.slider('Floors', min_value=min_dict['floors'], max_value=max_dict['floors'], value=min_dict['floors'])
+    waterfront = st.checkbox('Waterfront', value=False)
+    view = st.slider('View', min_value=min_dict['view'], max_value=max_dict['view'], value=min_dict['view'])
+    condition = st.slider('Condition', min_value=min_dict['condition'], max_value=max_dict['condition'], value=min_dict['condition'])
+    sqft_above = st.slider('Square Feet (Above)', min_value=min_dict['sqft_above'], max_value=max_dict['sqft_above'], value=min_dict['sqft_above'])
+    sqft_basement = st.slider('Square Feet (Basement)', min_value=min_dict['sqft_basement'], max_value=max_dict['sqft_basement'], value=min_dict['sqft_basement'])
+    yr_built = st.slider('Year Built', min_value=min_dict['yr_built'], max_value=max_dict['yr_built'], value=min_dict['yr_built'])
+    yr_renovated = st.slider('Year Renovated', min_value=min_dict['yr_renovated'], max_value=max_dict['yr_renovated'], value=min_dict['yr_renovated'])
 
-    # Predict the price
-    predicted_price = model.predict(new_pred)
+    if (bedrooms != min_dict['bedrooms'] or
+        bathrooms != min_dict['bathrooms'] or
+        sqft_living != min_dict['sqft_living'] or
+        sqft_lot != min_dict['sqft_lot'] or
+        floors != min_dict['floors'] or
+       # waterfront != min_dict['waterfront'] or
+        view != min_dict['view'] or
+        condition != min_dict['condition'] or
+        sqft_above != min_dict['sqft_above'] or
+        sqft_basement != min_dict['sqft_basement'] or
+        yr_built != min_dict['yr_built'] or
+        yr_renovated != min_dict['yr_renovated']):
+        
+        new_pred = init_new_pred()
+        new_pred['bedrooms'] = bedrooms
+        new_pred['bathrooms'] = bathrooms
+        new_pred['sqft_living'] = sqft_living
+        new_pred['sqft_lot'] = sqft_lot
+        new_pred['floors'] = floors
+        new_pred['waterfront'] = int(waterfront)
+        new_pred['view'] = view
+        new_pred['condition'] = condition
+        new_pred['sqft_above'] = sqft_above
+        new_pred['sqft_basement'] = sqft_basement
+        new_pred['yr_built'] = yr_built
+        new_pred['yr_renovated'] = yr_renovated
+        new_pred[f'city_{city}'] = 1
     
-    # Display the predicted price at the top of the app
-    price_placeholder.write(f"Predicted Price: ${predicted_price[0][0]:,.2f}")
+        # Process the prediction
+        new_pred = pd.DataFrame([new_pred])
+        new_pred = create_new_features(new_pred)
+        new_pred = normalize(new_pred)
+    
+        # Predict the price
+        predicted_price = model.predict(new_pred)
+
+# Display the map in the second column
+with col2:
+    if ()
+    st.subheader('Map')
+    data = pd.DataFrame({
+        'latitude': [47.6097, 47.6205, 47.6762],  # Example latitudes
+        'longitude': [-122.3331, -122.3493, -122.3198]  # Example longitudes
+    })
+    st.map(data)