Merge branch 'main' of https://huggingface.co/spaces/IITGN-GFD/NDVI_PERG into main

app.py

@@ -91,6 +91,8 @@ def shape_3d_to_2d(shape):
 
 def preprocess_gdf(gdf):
     gdf = gdf.to_crs(epsg=7761) # epsg for Gujarat
+
+
     gdf["geometry"] = gdf["geometry"].apply(shape_3d_to_2d)
     return gdf
 
@@ -231,8 +233,8 @@ dec_31 = pd.to_datetime(f"{max_year}/12/31", format="%Y/%m/%d")
 nov_15 = pd.to_datetime(f"{max_year}/11/15", format="%Y/%m/%d")
 dec_15 = pd.to_datetime(f"{max_year}/12/15", format="%Y/%m/%d")
 input_daterange = st.date_input("Date Range (Ignore year. App will compute indices for all possible years)", (nov_15, dec_15), jan_1, dec_31)
-min_year = int(st.number_input("Minimum Year", value=2010, min_value=2010, step=1))
-max_year = int(st.number_input("Maximum Year", value=max_year, min_value=2010, step=1))
+min_year = int(st.number_input("Minimum Year", value=2019, min_value=2015, step=1))
+max_year = int(st.number_input("Maximum Year", value=max_year, min_value=2015, step=1))
 
 # Input: GeoJSON/KML file
 file_url = st.query_params.get("file_url", None)
@@ -251,13 +253,14 @@ def format_fn(x):
 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]
 buffer_geometry_gdf = geometry_gdf.copy()
-buffer_geometry_gdf["geometry"] = buffer_geometry_gdf["geometry"].buffer(buffer)
+buffer_geometry_gdf["geometry"] = buffer_geometry_gdf["geometry"].buffer(buffer).difference(geometry_gdf.geometry.unary_union)
 check_valid_geometry(geometry_gdf)
 
 # Derived Inputs
 ee_geometry = ee.Geometry(geometry_gdf.to_crs(4326).geometry.item().__geo_interface__)
 ee_feature_collection = ee.FeatureCollection(ee_geometry)
 buffer_ee_geometry = ee.Geometry(buffer_geometry_gdf.to_crs(4326).geometry.item().__geo_interface__)
+buffer_ee_geometry = buffer_ee_geometry.difference(ee_geometry)
 buffer_ee_feature_collection = ee.FeatureCollection(buffer_ee_geometry)
 
 # visualize the geometry
@@ -365,7 +368,7 @@ if "result" in st.session_state:
     with cols[1]:
         year_2 = st.selectbox("Year 2", result_df.index, index=len(result_df.index) - 1, format_func=lambda x: daterange_str_to_year(x))
             
-    vis_params = {'min': 0, 'max': 1, 'palette': ['white', 'green']}  # Example visualization for Sentinel-2
+    vis_params = {'min': 0, 'max': 1, 'palette': ['white', 'green']}  # Example visualisation for Sentinel-2
 
     # Create a colormap and name it as NDVI
     colormap = cm.LinearColormap(