Update app.py

app.py

@@ -41,8 +41,7 @@ max_dict = {
     '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 two columns: one for the city and one for the map
 col1, col2 = st.columns([1, 2])  # Adjust the width ratios as needed
@@ -82,48 +81,57 @@ with col1:
     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'])
 
-    if (city != 'Algona' or # Assuming Algona as the default value
-        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
-        int(waterfront) != 0 or  # Assuming default value for waterfront is 0
-        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']):
+    # if (city != 'Algona' or # Assuming Algona as the default value
+    #     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
+    #     int(waterfront) != 0 or  # Assuming default value for waterfront is 0
+    #     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
-    
-        # 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)
+    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
+
+    # 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 predicted price at the top of the app
+    price_placeholder.write(f"Predicted Price: ${predicted_price[0][0]:,.2f}")
 
 # Display the map in the second column
 with col2:
     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)
+    if city == 'Algona':
+        map_data = pd.DataFrame({
+            'latitude': [47.6097, 47.6205, 47.6762],
+            'longitude': [-122.3331, -122.3493, -122.3198]
+        })
+    elif city == 'Auburn':
+        map_data = pd.DataFrame({
+            'latitude': [47.6101, 47.6183],
+            'longitude': [-122.2015, -122.2046]
+        })
+    st.map(map_data)