bhuvan / app.py
Zeel's picture
index=True.
7480d60
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2.76 kB
import re
import json
import streamlit as st
import pandas as pd
import geopandas as gpd
import leafmap.foliumap as leafmap
# wide streamlit display
st.set_page_config(layout="wide")
file_url = st.query_params.get("file_url", [None])
print(f"{file_url=}")
if file_url:
if file_url.startswith("https://drive.google.com/file/d/"):
ID = file_url.replace("https://drive.google.com/file/d/", "").split("/")[0]
file_url = f"https://drive.google.com/uc?id={ID}"
input_gdf = gpd.read_file(file_url)
input_gdf = input_gdf.to_crs(epsg=7761) # Gujarat zone
def format_fn(x):
return input_gdf.drop(columns=["geometry"]).loc[x].to_dict()
input_geometry_idx = st.selectbox("Select the geometry", input_gdf.index, format_func=format_fn)
geometry_gdf = input_gdf[input_gdf.index == input_geometry_idx]
m = leafmap.Map()
m.add_wms_layer(
"https://wayback.maptiles.arcgis.com/arcgis/rest/services/World_Imagery/WMTS/1.0.0/GoogleMapsCompatible/MapServer/tile/56450/{z}/{y}/{x}",
layers="0",
)
m.add_gdf(
geometry_gdf.to_crs(epsg=4326),
layer_name="Geometry",
zoom_to_layer=True,
style_function=lambda x: {"color": "red", "fillOpacity": 0.0},
)
m.to_streamlit()
# Metrics
stats_df = pd.DataFrame()
stats_df["Points"] = json.loads(geometry_gdf.to_crs(4326).to_json())["features"][0]["geometry"]["coordinates"]
stats_df["Area (ha)"] = geometry_gdf.geometry.area.item() / 10000
stats_df["Perimeter (m)"] = geometry_gdf.geometry.length.item()
st.write("<h3><div style='text-align: center;'>Geometry Metrics</div></h3>", unsafe_allow_html=True)
# st.markdown(
# f"""| Metric | Value |
# | --- | --- |
# | Area (ha) | {stats_df['Area (ha)'].item():.2f} ha|
# | Perimeter (m) | {stats_df['Perimeter (m)'].item():.2f} m |"""
# unsafe_allow_html=True)
st.markdown(
f"""
<div style="display: flex; justify-content: center;">
<table>
<tr>
<th>Metric</th>
<th>Value</th>
</tr>
<tr>
<td>Area (ha)</td>
<td>{stats_df['Area (ha)'].item():.2f} ha</td>
</tr>
<tr>
<td>Perimeter (m)</td>
<td>{stats_df['Perimeter (m)'].item():.2f} m</td>
</tr>
</table>
</div>
""",
unsafe_allow_html=True,
)
csv = stats_df.T.to_csv(index=True)
st.download_button(
"Download Geometry Metrics", csv, f"{file_url}_metrics.csv", "text/csv", use_container_width=True
)
else:
st.warning("Please provide a KML or GeoJSON URL as a query parameter, e.g., `?file_url=<your_file_url>`")