update app.py

app.py

@@ -115,8 +115,6 @@ if 'last_params' not in st.session_state:
     st.session_state.last_params = {}
 if 'map_data' not in st.session_state:
     st.session_state.map_data = None  # Initialize map_data
-if 'image_collection' not in st.session_state:
-    st.session_state.image_collection = None  # Initialize image collection storage
 
 # Function to check if parameters have changed
 def parameters_changed():
@@ -128,10 +126,9 @@ def parameters_changed():
         st.session_state.last_params.get('end_date_str') != end_date_str
     )
 
-# If parameters have changed, reset the results and clear the image collection from session state
+# If parameters have changed, reset the results
 if parameters_changed():
     st.session_state.results = []  # Clear the previous results
-    st.session_state.image_collection = None  # Clear the image collection
     # Update the last parameters to the current ones
     st.session_state.last_params = {
         'main_selection': main_selection,
@@ -228,40 +225,37 @@ if file_upload:
             
             m.add_marker(location=[latitude, longitude], popup=row.get('name', 'No Name'))
 
-        st.write(m)
+        # Display map
+        st.write("Map of Uploaded Points:")
+        m.to_streamlit()
 
-        # Perform the calculation for each point in the DataFrame
-        for idx, row in locations_df.iterrows():
-            latitude = row['latitude']
-            longitude = row['longitude']
-            location_name = row.get('name', f"Location_{idx}")
-
-            # Skip processing if latitude or longitude is NaN
-            if pd.isna(latitude) or pd.isna(longitude):
-                continue  # Skip this row and move to the next one
+        # Store the map in session_state
+        st.session_state.map_data = m
 
-            # Define the region of interest (ROI) and create a buffer around the point
-            roi = ee.Geometry.Point([longitude, latitude]).buffer(1000)  # 1 km buffer around the point
+    # Process each point for index calculation
+    for idx, row in locations_df.iterrows():
+        latitude = row['latitude']
+        longitude = row['longitude']
+        location_name = row.get('name', f"Location_{idx}")
 
-            # Check if the image collection is already in session state, else load it
-            if st.session_state.image_collection is None:
-                # Load Sentinel-2 image collection
-                collection = ee.ImageCollection(sub_options[sub_selection]) \
-                    .filterDate(ee.Date(start_date_str), ee.Date(end_date_str)) \
-                    .filterBounds(roi)
+        # Skip processing if latitude or longitude is NaN
+        if pd.isna(latitude) or pd.isna(longitude):
+            continue  # Skip this row and move to the next one
 
-                # Check if the collection has images for the selected date range
-                image_count = collection.size().getInfo()
-                if image_count == 0:
-                    st.warning(f"No images found for {location_name}.")
-                else:
-                    st.write(f"Found {image_count} images for {location_name}.")
-                    st.session_state.image_collection = collection  # Store the collection in session state
+        # Define the region of interest (ROI)
+        roi = ee.Geometry.Point([longitude, latitude])
 
-            else:
-                collection = st.session_state.image_collection  # Use the cached collection
+        # Load Sentinel-2 image collection
+        collection = ee.ImageCollection(sub_options[sub_selection]) \
+            .filterDate(ee.Date(start_date_str), ee.Date(end_date_str)) \
+            .filterBounds(roi)
 
-            # Get the first image from the collection
+        # Check if the collection has images for the selected date range
+        image_count = collection.size().getInfo()
+        if image_count == 0:
+            st.warning(f"No images found for {location_name}.")
+        else:
+            st.write(f"Found {image_count} images for {location_name}.")
             image = collection.first()
 
             # Perform the calculation based on user selection
@@ -290,42 +284,58 @@ if file_upload:
                     'Calculated Value': calculated_value
                 })
 
-                # Sanitize the location name for use in the export task
-                sanitized_location_name = sanitize_description(location_name)
-
-                # Now allow user to download the image
-                st.write(f"Click below to download the image for {sanitized_location_name}:")
-                
-                # Export image as GeoTIFF
-                export_image = image.clip(roi)  # Clip the image to the region of interest (ROI)
-                export_task = ee.batch.Export.image.toDrive(
-                    image=export_image,
-                    description=sanitized_location_name,
-                    folder='Sentinel_Images',
-                    fileNamePrefix=f'{sanitized_location_name}_image',
-                    region=roi,
-                    scale=30,
-                    fileFormat='GeoTIFF',
-                    crs='EPSG:4326'
-                )
-                export_task.start()
-
-                # Show export status
-                st.info(f"Exporting image for {sanitized_location_name}. This might take some time...")
-
-                # Poll for the task status (to check if export is finished)
-                while export_task.active():
-                    time.sleep(5)
-                    st.write("Export in progress...")
-
-                st.write(f"Export complete! The image is ready for download.")
-                
-                # Generate a link to the exported image in Google Drive
-                # Replace 'your_google_drive_folder_link' with the actual folder ID in your Drive
-                st.write(f"Click below to download the GeoTIFF image for {sanitized_location_name}:")
-                st.download_button(
-                    label=f"Download {sanitized_location_name} Image",
-                    data=export_image.getDownloadURL(),
-                    file_name=f'{sanitized_location_name}_image.tif'
-                )
-
+            # Sanitize the location name for use in the export task
+            sanitized_location_name = sanitize_description(location_name)
+
+            # Now allow user to download the image
+            st.write(f"Click below to download the image for {sanitized_location_name}:")
+
+            # Export image as GeoTIFF
+            export_image = image.clip(roi)  # Clip the image to the region of interest (ROI)
+            export_task = ee.batch.Export.image.toDrive(
+                image=export_image,
+                description=sanitized_location_name,
+                folder='Sentinel_Images',
+                fileNamePrefix=f'{sanitized_location_name}_image',
+                region=roi,
+                scale=30,
+                fileFormat='GeoTIFF',
+                crs='EPSG:4326'
+            )
+            export_task.start()
+
+            # Show export status
+            st.info(f"Exporting image for {sanitized_location_name}. This might take some time...")
+
+            # Poll for the task status (to check if export is finished)
+            while export_task.active():
+                time.sleep(5)
+                st.write("Export in progress...")
+
+            st.write(f"Export complete! The image is ready for download.")
+            
+            # Generate a link to the exported image in Google Drive
+            # Replace 'your_google_drive_folder_link' with the actual folder ID in your Drive
+            st.write(f"Click below to download the GeoTIFF image for {sanitized_location_name}:")
+            st.download_button(
+                label=f"Download {sanitized_location_name} Image",
+                data=export_image.getDownloadURL(),
+                file_name=f'{sanitized_location_name}_image.tif'
+            )
+
+# 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'
+    )