Add DEM and Slope

app.py

@@ -407,16 +407,13 @@ def process_and_display(file_obj, url_str, buffer_m, progress=gr.Progress()):
     progress(1, desc="Done!")
     return m._repr_html_(), None, stats_df, dem_html, slope_html, geometry_json, buffer_geometry_json
 
-
-
-
 def calculate_indices(
     geometry_json, buffer_geometry_json, veg_indices, evi_vars, date_range,
     min_year, max_year, progress=gr.Progress()
 ):
     """Calculates vegetation indices based on user inputs."""
     one_time_setup()
-    
+
     if not all([geometry_json, buffer_geometry_json, veg_indices]):
         return "Please process a file and select at least one index first.", None, None, None
 
@@ -428,7 +425,7 @@ def calculate_indices(
         # Convert to EE geometry
         ee_geometry = ee.Geometry(json.loads(geometry_gdf.to_crs(4326).to_json())['features'][0]['geometry'])
         buffer_ee_geometry = ee.Geometry(json.loads(buffer_geometry_gdf.to_crs(4326).to_json())['features'][0]['geometry'])
-        
+
         # Date ranges
         start_day, start_month = date_range[0].day, date_range[0].month
         end_day, end_month = date_range[1].day, date_range[1].month
@@ -454,35 +451,38 @@ def calculate_indices(
             if filtered_collection.size().getInfo() == 0:
                 continue
 
+            # **MODIFIED**: Add 'Year' column data to the row
             year_val = int(start_date.split('-')[0])
             row = {'Year': year_val, 'Date Range': f"{start_date}_to_{end_date}"}
 
             for veg_index in veg_indices:
                 mosaic = filtered_collection.qualityMosaic(veg_index)
-                
+
                 mean_val = mosaic.reduceRegion(reducer=ee.Reducer.mean(), geometry=ee_geometry, scale=10, maxPixels=1e9).get(veg_index).getInfo()
                 buffer_mean_val = mosaic.reduceRegion(reducer=ee.Reducer.mean(), geometry=buffer_ee_geometry, scale=10, maxPixels=1e9).get(veg_index).getInfo()
 
                 row[veg_index] = mean_val
                 row[f"{veg_index}_buffer"] = buffer_mean_val
-                row[f"{veg_index}_ratio"] = (mean_val / buffer_mean_val) if buffer_mean_val else np.nan
+                row[f"{veg_index}_ratio"] = (mean_val / buffer_mean_val) if buffer_mean_val and buffer_mean_val != 0 else np.nan
             result_rows.append(row)
 
         if not result_rows:
             return "No satellite imagery found for the selected dates.", None, None, None
 
-        result_df = pd.DataFrame(result_rows).set_index('daterange')
+        result_df = pd.DataFrame(result_rows)
         result_df = result_df.round(3)
 
         # Create plots
         plots = []
         for veg_index in veg_indices:
-             plot_cols = [veg_index, f"{veg_index}_buffer", f"{veg_index}_ratio"]
+            # **MODIFIED**: Plot using the new 'Year' column for the x-axis
+            plot_cols = [veg_index, f"{veg_index}_buffer", f"{veg_index}_ratio"]
             existing_plot_cols = [col for col in plot_cols if col in result_df.columns]
             
             plot_df = result_df[['Year'] + existing_plot_cols].dropna()
+
             if not plot_df.empty:
-                fig = px.line(plot_df, x=plot_df.index, y=plot_df.columns, markers=True, title=f"{veg_index} Time Series")
+                fig = px.line(plot_df, x='Year', y=existing_plot_cols, markers=True, title=f"{veg_index} Time Series")
                 fig.update_layout(xaxis_title="Year", yaxis_title="Index Value")
                 # Ensure x-axis ticks are whole numbers for years
                 fig.update_xaxes(dtick=1)