Update src/streamlit_app.py
Browse files- src/streamlit_app.py +24 -13
src/streamlit_app.py
CHANGED
@@ -13,6 +13,10 @@ st.set_page_config(layout="wide", page_title="Multiplex Coop Housing Filter")
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st.title("🗺️ Multiplex Coop Housing Filter (Hugging Face Data)")
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st.write("This app uses the `ProjectMultiplexCoop/PropertyBoundaries` dataset from Hugging Face. Draw a polygon on the map to spatially filter properties. Use the form below to apply additional filters based on property attributes. **Note: FSI, Building Coverage, Height, and Stories are synthetic for demonstration as they are not directly available in the dataset.**")
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# --- 1. Load Data from Hugging Face and Process ---
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@st.cache_data
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def load_and_process_data():
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@@ -202,8 +206,7 @@ with st.form("attribute_filters"):
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st.info("Adjust filters and click 'Apply Attribute Filters'.")
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# --- 4. Display Filtered Data on a New Map and as a Table
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# The expander is expanded by default to show results immediately after filtering
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with st.expander("Filtered Properties Display", expanded=True):
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if not filtered_df.empty:
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# Calculate bounds for filtered data to set appropriate zoom
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@@ -235,12 +238,20 @@ with st.expander("Filtered Properties Display", expanded=True):
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fill_opacity=0.5
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).add_to(filtered_m)
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# Convert filtered_df
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filtered_gdf = gpd.GeoDataFrame(filtered_df, geometry='geometry')
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# Add filtered polygons to the map as GeoJSON layer
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folium.GeoJson(
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style_function=lambda x: {
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'fillColor': 'green',
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'color': 'darkgreen',
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@@ -257,13 +268,11 @@ with st.expander("Filtered Properties Display", expanded=True):
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st_folium(filtered_m, width=1000, height=500)
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st.subheader("Filtered Properties Table")
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# Limit rows displayed in the dataframe to prevent MessageSizeError
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MAX_ROWS_DISPLAY = 1000 # Define a reasonable limit
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display_cols = ['PARCELID', 'zn_type', 'zn_area', 'fsi_total', 'prcnt_cver', 'height_metres', 'stories', 'ADDRESS_NUMBER', 'LINEAR_NAME_FULL']
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if len(filtered_df) >
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st.warning(f"Displaying only the first {
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st.dataframe(filtered_df[display_cols].head(
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else:
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st.dataframe(filtered_df[display_cols])
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@@ -284,10 +293,12 @@ st.markdown("---")
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st.markdown(
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"""
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**Troubleshooting Large Data:**
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If you encounter a `MessageSizeError`
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it means the data size exceeds Streamlit's
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You can
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However, be aware that increasing this limit can lead to longer loading times and higher
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memory consumption in your browser and on the Streamlit server.
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"""
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st.title("🗺️ Multiplex Coop Housing Filter (Hugging Face Data)")
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st.write("This app uses the `ProjectMultiplexCoop/PropertyBoundaries` dataset from Hugging Face. Draw a polygon on the map to spatially filter properties. Use the form below to apply additional filters based on property attributes. **Note: FSI, Building Coverage, Height, and Stories are synthetic for demonstration as they are not directly available in the dataset.**")
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# --- Configuration Constants ---
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MAX_ROWS_DATAFRAME_DISPLAY = 1000 # Max rows to show in st.dataframe
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MAX_MAP_FEATURES_DISPLAY = 5000 # Max features to plot on the Folium map to prevent MessageSizeError
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# --- 1. Load Data from Hugging Face and Process ---
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@st.cache_data
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def load_and_process_data():
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st.info("Adjust filters and click 'Apply Attribute Filters'.")
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# --- 4. Display Filtered Data on a New Map and as a Table ---
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with st.expander("Filtered Properties Display", expanded=True):
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if not filtered_df.empty:
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# Calculate bounds for filtered data to set appropriate zoom
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fill_opacity=0.5
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).add_to(filtered_m)
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# Convert filtered_df to GeoDataFrame for plotting
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filtered_gdf = gpd.GeoDataFrame(filtered_df, geometry='geometry')
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# --- Apply map display limit ---
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features_to_plot_count = len(filtered_gdf)
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if features_to_plot_count > MAX_MAP_FEATURES_DISPLAY:
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st.warning(f"Displaying a random sample of {MAX_MAP_FEATURES_DISPLAY} properties on the map (out of {features_to_plot_count} total filtered) to prevent performance issues.")
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filtered_gdf_for_map = filtered_gdf.sample(MAX_MAP_FEATURES_DISPLAY, random_state=42)
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else:
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filtered_gdf_for_map = filtered_gdf
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# Add filtered polygons to the map as GeoJSON layer
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folium.GeoJson(
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filtered_gdf_for_map.to_json(),
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style_function=lambda x: {
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'fillColor': 'green',
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'color': 'darkgreen',
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st_folium(filtered_m, width=1000, height=500)
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st.subheader("Filtered Properties Table")
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display_cols = ['PARCELID', 'zn_type', 'zn_area', 'fsi_total', 'prcnt_cver', 'height_metres', 'stories', 'ADDRESS_NUMBER', 'LINEAR_NAME_FULL']
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if len(filtered_df) > MAX_ROWS_DATAFRAME_DISPLAY:
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st.warning(f"Displaying only the first {MAX_ROWS_DATAFRAME_DISPLAY} rows of the filtered data ({len(filtered_df)} total properties). Download the full dataset below.")
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st.dataframe(filtered_df[display_cols].head(MAX_ROWS_DATAFRAME_DISPLAY))
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else:
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st.dataframe(filtered_df[display_cols])
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st.markdown(
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"""
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**Troubleshooting Large Data:**
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If you still encounter a `MessageSizeError` despite the display limits,
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it means the data size still exceeds Streamlit's internal limit, or the sampled data is still too complex.
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You can try decreasing `MAX_MAP_FEATURES_DISPLAY` and `MAX_ROWS_DATAFRAME_DISPLAY` further.
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Alternatively, you can increase Streamlit's default message size limit by adding
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`server.maxMessageSize = <size_in_mb>` (e.g., `server.maxMessageSize = 500`)
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to your Streamlit `config.toml` file.
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However, be aware that increasing this limit can lead to longer loading times and higher
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memory consumption in your browser and on the Streamlit server.
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"""
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