update app.py

app.py

@@ -94,84 +94,6 @@ reducer_choice = st.selectbox(
     index=0  # Default to 'mean'
 )
 
-# Function to get the corresponding reducer based on user input
-def get_reducer(reducer_name):
-    """
-    Map user-friendly reducer names to Earth Engine reducer objects.
-
-    Args:
-        reducer_name (str): The name of the reducer (e.g., 'mean', 'sum', 'median').
-
-    Returns:
-        ee.Reducer: The corresponding Earth Engine reducer.
-    """
-    reducers = {
-        'mean': ee.Reducer.mean(),
-        'sum': ee.Reducer.sum(),
-        'median': ee.Reducer.median(),
-        'min': ee.Reducer.min(),
-        'max': ee.Reducer.max(),
-        'count': ee.Reducer.count(),
-    }
-    
-    # Default to 'mean' if the reducer_name is not recognized
-    return reducers.get(reducer_name.lower(), ee.Reducer.mean())
-
-def calculate_index_by_frequency(image_collection, frequency, roi, reducer):
-    """
-    Calculate the index based on the selected frequency (daily, weekly, monthly, yearly).
-    Args:
-        image_collection (ee.ImageCollection): The Earth Engine image collection.
-        frequency (str): The frequency ('daily', 'weekly', 'monthly', 'yearly').
-        roi (ee.Geometry): The region of interest.
-        reducer (ee.Reducer): The reducer to apply.
-
-    Returns:
-        tuple: The result of applying the calculation, and the date associated with that result.
-    """
-    if frequency == 'daily':
-        # Apply daily filtering logic (each individual image)
-        image_collection = image_collection.filterDate(ee.Date(start_date_str), ee.Date(end_date_str)).filterBounds(roi)
-    elif frequency == 'weekly':
-        # Apply weekly logic by filtering by week of year
-        image_collection = image_collection.filterDate(ee.Date(start_date_str), ee.Date(end_date_str)).filterBounds(roi).filter(ee.Filter.calendarRange(1, 1, 'month'))
-    elif frequency == 'monthly':
-        # Apply monthly logic by filtering by month
-        image_collection = image_collection.filterDate(ee.Date(start_date_str), ee.Date(end_date_str)).filterBounds(roi).filter(ee.Filter.calendarRange(1, 1, 'month'))
-    elif frequency == 'yearly':
-        # Apply yearly logic
-        image_collection = image_collection.filterDate(ee.Date(start_date_str), ee.Date(end_date_str)).filterBounds(roi).filter(ee.Filter.calendarRange(1, 1, 'year'))
-    else:
-        raise ValueError(f"Unsupported frequency: {frequency}")
-
-    # Apply the reducer on the filtered collection
-    result = image_collection.reduce(reducer)
-
-    # Get the date for the result
-    # If using daily frequency, you can take the date of the first image
-    if frequency == 'daily':
-        result_date = image_collection.first().date().getInfo()  # Get the date of the first image
-    elif frequency == 'weekly':
-        result_date = image_collection.mean().date().getInfo()  # Take the mean date (or any representative date)
-    elif frequency == 'monthly':
-        result_date = image_collection.mean().date().getInfo()
-    elif frequency == 'yearly':
-        result_date = image_collection.mean().date().getInfo()
-    else:
-        result_date = "Unknown"  # Fallback if something goes wrong
-
-    # Return the result and the associated date
-    return result, result_date
-
-# Add your current Streamlit UI and Earth Engine initialization code here...
-
-# Get the reducer based on user selection
-reducer = get_reducer(reducer_choice)
-
-# Get the frequency selected by the user (you will need to add this UI in your Streamlit app)
-frequency_choice = st.selectbox("Select Frequency", ['daily', 'weekly', 'monthly', 'yearly'])
-
-
 # Function to check if the polygon geometry is valid and convert it to the correct format
 def convert_to_ee_geometry(geometry):
     if geometry.is_valid:
@@ -242,6 +164,82 @@ if parameters_changed():
         'file_upload': file_upload
     }
 
+# Function to get the corresponding reducer based on user input
+def get_reducer(reducer_name):
+    """
+    Map user-friendly reducer names to Earth Engine reducer objects.
+
+    Args:
+        reducer_name (str): The name of the reducer (e.g., 'mean', 'sum', 'median').
+
+    Returns:
+        ee.Reducer: The corresponding Earth Engine reducer.
+    """
+    reducers = {
+        'mean': ee.Reducer.mean(),
+        'sum': ee.Reducer.sum(),
+        'median': ee.Reducer.median(),
+        'min': ee.Reducer.min(),
+        'max': ee.Reducer.max(),
+        'count': ee.Reducer.count(),
+    }
+    
+    # Default to 'mean' if the reducer_name is not recognized
+    return reducers.get(reducer_name.lower(), ee.Reducer.mean())
+
+# def calculate_index_by_frequency(image_collection, frequency, roi, reducer):
+#     """
+#     Calculate the index based on the selected frequency (daily, weekly, monthly, yearly).
+#     Args:
+#         image_collection (ee.ImageCollection): The Earth Engine image collection.
+#         frequency (str): The frequency ('daily', 'weekly', 'monthly', 'yearly').
+#         roi (ee.Geometry): The region of interest.
+#         reducer (ee.Reducer): The reducer to apply.
+
+#     Returns:
+#         tuple: The result of applying the calculation, and the date associated with that result.
+#     """
+#     if frequency == 'daily':
+#         # Apply daily filtering logic (each individual image)
+#         image_collection = image_collection.filterDate(ee.Date(start_date_str), ee.Date(end_date_str)).filterBounds(roi)
+#     elif frequency == 'weekly':
+#         # Apply weekly logic by filtering by week of year
+#         image_collection = image_collection.filterDate(ee.Date(start_date_str), ee.Date(end_date_str)).filterBounds(roi).filter(ee.Filter.calendarRange(1, 1, 'month'))
+#     elif frequency == 'monthly':
+#         # Apply monthly logic by filtering by month
+#         image_collection = image_collection.filterDate(ee.Date(start_date_str), ee.Date(end_date_str)).filterBounds(roi).filter(ee.Filter.calendarRange(1, 1, 'month'))
+#     elif frequency == 'yearly':
+#         # Apply yearly logic
+#         image_collection = image_collection.filterDate(ee.Date(start_date_str), ee.Date(end_date_str)).filterBounds(roi).filter(ee.Filter.calendarRange(1, 1, 'year'))
+#     else:
+#         raise ValueError(f"Unsupported frequency: {frequency}")
+
+#     # Apply the reducer on the filtered collection
+#     result = image_collection.reduce(reducer)
+
+#     # Get the date for the result
+#     # If using daily frequency, you can take the date of the first image
+#     if frequency == 'daily':
+#         result_date = image_collection.first().date().getInfo()  # Get the date of the first image
+#     elif frequency == 'weekly':
+#         result_date = image_collection.mean().date().getInfo()  # Take the mean date (or any representative date)
+#     elif frequency == 'monthly':
+#         result_date = image_collection.mean().date().getInfo()
+#     elif frequency == 'yearly':
+#         result_date = image_collection.mean().date().getInfo()
+#     else:
+#         result_date = "Unknown"  # Fallback if something goes wrong
+
+#     # Return the result and the associated date
+#     return result, result_date
+
+# # Add your current Streamlit UI and Earth Engine initialization code here...
+
+# # Get the reducer based on user selection
+# reducer = get_reducer(reducer_choice)
+
+# # Get the frequency selected by the user (you will need to add this UI in your Streamlit app)
+# frequency_choice = st.selectbox("Select Frequency", ['daily', 'weekly', 'monthly', 'yearly'])
 
 
 # Function to perform index calculations
@@ -393,28 +391,27 @@ if file_upload:
                 .filterDate(ee.Date(start_date_str), ee.Date(end_date_str)) \
                 .filterBounds(roi)
 
-            # Apply frequency-based filtering and reduction
-            result, result_date = calculate_index_by_frequency(collection, frequency_choice, roi, reducer)
-
-            if result is not None:
-            #     st.warning(f"No images found for {location_name}.")
-            # else:
-            #     st.write(f"Found images for {location_name}.")
-            #     # Perform the calculation based on user selection
-            #     # Perform the calculation based on user selection
-            #     result = None
-                calculated_value = None  # Initialize the calculated_value as None
+            # # Apply frequency-based filtering and reduction
+            # result, result_date = calculate_index_by_frequency(collection, frequency_choice, roi, reducer)
+            image = get_most_recent_image(collection)
+            if not image:
+                st.warning(f"No images found for {location_name}.")
+            else:
+                st.write(f"Found images for {location_name}.")
+                # Perform the calculation based on user selection
+                # Perform the calculation based on user selection
+                result = None
                 if index_choice == 'NDVI':
-                    result = calculate_ndvi(result, roi)
+                    result = calculate_ndvi(image, roi)
                 elif index_choice == 'NDWI':
-                    result = calculate_ndwi(result, roi)
+                    result = calculate_ndwi(image, roi)
                 elif index_choice == 'Average NO₂':
                     if 'NO2' in image.bandNames().getInfo():
-                        result = calculate_avg_no2_sentinel5p(result, roi)
+                        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.")
                 elif index_choice.lower() == 'custom formula' and custom_formula:
-                    result = process_custom_formula(result, roi, custom_formula)
+                    result = process_custom_formula(image, roi, custom_formula)
 
                 # Validate result before using getInfo
                 if result is not None:
@@ -437,8 +434,8 @@ if file_upload:
                             'Location Name': location_name,
                             'Latitude': latitude,
                             'Longitude': longitude,
-                            'Calculated Value': calculated_value,
-                            'Date': result_date  # Add the date of the calculation
+                            'Calculated Value': calculated_value
+                            # 'Date': result_date  # Add the date of the calculation
                         })
                     else:
                         st.warning(f"No value calculated for {location_name}.")