Update app.py

app.py

@@ -9,6 +9,20 @@ with open('./trained_model.pkl', 'rb') as 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,
@@ -50,7 +64,7 @@ bathrooms = st.slider('Bathrooms', min_value=min_dict['bathrooms'], max_value=ma
 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.slider('Waterfront', min_value=min_dict['waterfront'], max_value=max_dict['waterfront'], value=min_dict['waterfront'])
+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'])
@@ -77,14 +91,14 @@ if (bedrooms != min_dict['bedrooms'] or
     new_pred['sqft_living'] = sqft_living
     new_pred['sqft_lot'] = sqft_lot
     new_pred['floors'] = floors
-    new_pred['waterfront'] = waterfront
+    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['city_Bellevue'] = 1
+    new_pred[f'city_{city}'] = 1
 
     # Process the prediction
     new_pred = pd.DataFrame([new_pred])