update app.py

app.py

@@ -128,19 +128,21 @@ if parameters_changed():
 # Function to perform index calculations
 def calculate_ndvi(image, geometry):
     ndvi = image.normalizedDifference(['B8', 'B4']).rename('NDVI')
-    return ndvi.reduceRegion(
+    result = ndvi.reduceRegion(
         reducer=ee.Reducer.mean(),
         geometry=geometry,
         scale=30
     )
+    return result.get('NDVI')
 
 def calculate_ndwi(image, geometry):
     ndwi = image.normalizedDifference(['B3', 'B8']).rename('NDWI')
-    return ndwi.reduceRegion(
+    result = ndwi.reduceRegion(
         reducer=ee.Reducer.mean(),
         geometry=geometry,
         scale=30
     )
+    return result.get('NDWI')
 
 def calculate_avg_no2_sentinel5p(image, geometry):
     no2 = image.select('NO2').reduceRegion(
@@ -151,13 +153,14 @@ def calculate_avg_no2_sentinel5p(image, geometry):
     return no2
 
 def calculate_custom_formula(image, geometry, formula):
-    return image.expression(formula).rename('Custom Index').reduceRegion(
+    result = image.expression(formula).rename('Custom Index').reduceRegion(
         reducer=ee.Reducer.mean(),
         geometry=geometry,
         scale=30
     )
+    return result.get('Custom Index')
 
-# Process each point or polygon
+# Process each point
 if file_upload:
     locations_df = None  # Initialize locations_df to None
     polygons_df = None   # Initialize polygons_df to None
@@ -182,46 +185,49 @@ if file_upload:
         else:
             st.error("Unsupported file type. Please upload a GeoJSON or KML file for polygons.")
 
-    # Display a preview of the dataframe
     if locations_df is not None:
+        # Display a preview of the points data
         st.write("Preview of the uploaded points data:")
         st.dataframe(locations_df.head())
 
-        # Check column names in the uploaded data
-        st.write("Column names in the uploaded data:", locations_df.columns)
-
-        # Check for latitude and longitude columns in different cases
-        latitude_column = None
-        longitude_column = None
-        
-        for col in locations_df.columns:
-            if 'lat' in col.lower():
-                latitude_column = col
-            if 'lon' in col.lower() or 'long' in col.lower():
-                longitude_column = col
-
-        if latitude_column is None or longitude_column is None:
-            st.error("Latitude and/or Longitude columns not found in the uploaded file. Please make sure the data includes these columns.")
-        else:
-            # Create a LeafMap object to display the points
-            m = leafmap.Map(center=[locations_df[latitude_column].mean(), locations_df[longitude_column].mean()], zoom=10)
+        # Create a LeafMap object to display the points
+        m = leafmap.Map(center=[locations_df['latitude'].mean(), locations_df['longitude'].mean()], zoom=10)
+
+        # Add points to the map
+        for _, row in locations_df.iterrows():
+            m.add_marker(location=[row['latitude'], row['longitude']], popup=row.get('name', 'No Name'))
+
+        # Display map
+        st.write("Map of Uploaded Points:")
+        m.to_streamlit()
+
+        # Store the map in session_state
+        st.session_state.map_data = m
+
+    if polygons_df is not None:
+        # Display a preview of the polygons data
+        st.write("Preview of the uploaded polygons data:")
+        st.dataframe(polygons_df.head())
+
+        # Create a LeafMap object to display the polygons
+        m = leafmap.Map(center=[polygons_df.geometry.centroid.y.mean(), polygons_df.geometry.centroid.x.mean()], zoom=4)
 
-            # Add points to the map
-            for _, row in locations_df.iterrows():
-                m.add_marker(location=[row[latitude_column], row[longitude_column]], popup=row.get('name', 'No Name'))
+        # Plot polygons on the map
+        for _, row in polygons_df.iterrows():
+            m.add_geojson(geojson=row['geometry'].__geo_interface__)
 
-            # Display map
-            st.write("Map of Uploaded Points:")
-            m.to_streamlit()
+        # Display map
+        st.write("Map of Uploaded Polygons:")
+        m.to_streamlit()
 
-            # Store the map in session_state
-            st.session_state.map_data = m
+        # Store the map in session_state
+        st.session_state.map_data = m
 
     # Process each point
-    if locations_df is not None and latitude_column and longitude_column:
+    if locations_df is not None:
         for idx, row in locations_df.iterrows():
-            latitude = row[latitude_column]
-            longitude = row[longitude_column]
+            latitude = row['latitude']
+            longitude = row['longitude']
             location_name = row.get('name', f"Location_{idx}")
 
             # Define the region of interest (ROI)
@@ -241,6 +247,7 @@ if file_upload:
                 image = collection.first()
 
                 # Perform the calculation based on user selection
+                result = None
                 if index_choice == 'NDVI':
                     result = calculate_ndvi(image, roi)
                 elif index_choice == 'NDWI':
@@ -250,13 +257,13 @@ if file_upload:
                         result = calculate_avg_no2_sentinel5p(image, roi)
                     else:
                         st.warning(f"No NO2 band found for {location_name}. Please use Sentinel-5P for NO₂ data.")
-                        result = None
                 elif index_choice == 'Custom Formula' and custom_formula:
                     result = calculate_custom_formula(image, roi, custom_formula)
 
-                if result:
-                    # Extract just the numeric value, not the dictionary with 'NDVI' label
-                    calculated_value = result.getInfo()
+                if result is not None:
+                    # Only store the numeric value (not the dictionary structure)
+                    calculated_value = result.getInfo()  # Get the numeric value
+
                     # Store the result in session state
                     st.session_state.results.append({
                         'Location Name': location_name,
@@ -265,21 +272,19 @@ if file_upload:
                         'Calculated Value': calculated_value
                     })
 
-
-    # Generate the dynamic filename
-    filename = f"{main_selection}_{sub_selection}_{start_date.strftime('%Y/%m/%d')}_{end_date.strftime('%Y/%m/%d')}_{shape_type}.csv"
-
-    if st.session_state.results:
-        # Convert the results to a DataFrame for better visualization
-        result_df = pd.DataFrame(st.session_state.results)
-    
-        # Show the results in a table format
-        st.write("Processed Results Table:")
-        st.dataframe(result_df)
-
-        st.download_button(
-            label="Download results as CSV",
-            data=result_df.to_csv(index=False).encode('utf-8'),
-            file_name=filename,
-            mime='text/csv'
-        )
+# After processing, show the results
+if st.session_state.results:
+    # Convert the results to a DataFrame for better visualization
+    result_df = pd.DataFrame(st.session_state.results)
+
+    # Show the results in a table format
+    st.write("Processed Results Table:")
+    st.dataframe(result_df[['Location Name', 'Latitude', 'Longitude', 'Calculated Value']])
+
+    # Allow downloading of the results as CSV
+    st.download_button(
+        label="Download results as CSV",
+        data=result_df.to_csv(index=False).encode('utf-8'),
+        file_name="calculated_results.csv",
+        mime='text/csv'
+    )