update app.py

app.py

@@ -12,7 +12,7 @@ import re
 st.set_page_config(layout="wide")
 
 # Custom button styling
-m = st.markdown(
+st.markdown(
     """
     <style>
     div.stButton > button:first-child {
@@ -25,7 +25,7 @@ m = st.markdown(
 
 # Logo
 st.write(
-    f"""
+    """
     <div style="display: flex; justify-content: space-between; align-items: center;">
         <img src="https://huggingface.co/spaces/YashMK89/GEE_Calculator/resolve/main/ISRO_Logo.png"  style="width: 20%; margin-right: auto;">
         <img src="https://huggingface.co/spaces/YashMK89/GEE_Calculator/resolve/main/SAC_Logo.png"  style="width: 20%; margin-left: auto;">
@@ -36,9 +36,7 @@ st.write(
 
 # Title
 st.markdown(
-    f"""
-    <h1 style="text-align: center;">Precision Analysis for Vegetation, Water, and Air Quality</h1>
-    """,
+    "<h1 style='text-align: center;'>Precision Analysis for Vegetation, Water, and Air Quality</h1>",
     unsafe_allow_html=True,
 )
 st.write("<h2><div style='text-align: center;'>User  Inputs</div></h2>", unsafe_allow_html=True)
@@ -185,36 +183,19 @@ def calculate_avg_no2_sentinel5p(image, geometry):
     return no2
 
 def calculate_custom_formula(image, geometry, formula, scale=30):
-    """
-    Calculate a custom formula on an image and return the result for a given geometry.
-    
-    Args:
-        image (ee.Image): The input image.
-        geometry (ee.Geometry): The region of interest (ROI) geometry.
-        formula (str): A string representing the mathematical formula to apply to the image.
-        scale (int): The scale for the reduceRegion operation (default is 30).
-
-    Returns:
-        ee.Dictionary: The result of applying the custom formula in the region.
-    """
-    # Dynamically generate the dictionary of band references from the image
     band_names = image.bandNames().getInfo()
     band_dict = {band: image.select(band) for band in band_names}
     
-    # Use the formula with the bands in the image
     result_image = image.expression(formula, band_dict).rename('CustomResult')
 
-    # Reduce the region to get the mean value for the given geometry
     result = result_image.reduceRegion(
         reducer=ee.Reducer.mean(),
         geometry=geometry,
         scale=scale
     )
     
-    # Return the result
     return result
 
-
 # Function to get the most recent image from the collection
 def get_most_recent_image(image_collection):
     image = image_collection.sort('system:time_start', False).first()
@@ -232,11 +213,7 @@ locations_df = None  # Initialize locations_df to None
 polygons_df = None   # Ensure polygons_df is initialized at the beginning
 
 # Process each point (with additional checks for file validity)
-# Check the shape type and assign polygons_df only for Polygon data
 if file_upload:
-    # locations_df = None  # Initialize locations_df to None
-    # polygons_df = None   # Ensure polygons_df is initialized at the beginning
-
     file_extension = os.path.splitext(file_upload.name)[1].lower()
 
     # Read file based on shape type
@@ -250,58 +227,45 @@ if file_upload:
         else:
             st.error("Unsupported file type. Please upload a CSV, GeoJSON, or KML file for points.")
     elif shape_type == 'Polygon':
-        if file_extension == '.geojson':
+        if file _extension == '.geojson':
             polygons_df = read_geojson(file_upload)
         elif file_extension == '.kml':
             polygons_df = read_kml(file_upload)
         else:
             st.error("Unsupported file type. Please upload a GeoJSON or KML file for polygons.")
 
-
     if locations_df is not None and not locations_df.empty:
-        # Ensure the necessary columns exist in the dataframe
         if 'latitude' not in locations_df.columns or 'longitude' not in locations_df.columns:
             st.error("Uploaded file is missing required 'latitude' or 'longitude' columns.")
         else:
-            # Display a preview of the points data
             st.write("Preview of the uploaded points data:")
             st.dataframe(locations_df.head())
 
-            # 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 using a loop
             for _, row in locations_df.iterrows():
                 latitude = row['latitude']
                 longitude = row['longitude']
                 
-                # Check if latitude or longitude are NaN and skip if they are
                 if pd.isna(latitude) or pd.isna(longitude):
-                    continue  # Skip this row and move to the next one
+                    continue
                 
                 m.add_marker(location=[latitude, 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
 
-        # 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}")
 
-            # 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
+                continue
 
-            # Define the region of interest (ROI)
             roi = ee.Geometry.Point([longitude, latitude])
 
-            # 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)
@@ -311,8 +275,6 @@ if file_upload:
                 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(image, roi)
@@ -326,22 +288,17 @@ if file_upload:
                 elif index_choice.lower() == 'custom formula' and custom_formula:
                     result = process_custom_formula(image, roi, custom_formula)
 
-                # Validate result before using getInfo
                 if result is not None:
-                    calculated_value = None  # Initialize the calculated_value as None
+                    calculated_value = None
 
-                # Check if the result is a dictionary
                     if isinstance(result, dict):
-                        # Extract the value using the appropriate key (adjust the key name as needed)
-                        calculated_value = result.get('CustomResult', None)  # Replace 'CustomResult' if using NDVI, NDWI, etc.
+                        calculated_value = result.get('CustomResult', None)
                     else:
                         try:
-                            # If it's an Earth Engine object, get the value using getInfo
                             calculated_value = result.getInfo()
                         except Exception as e:
                             st.error(f"Error getting result info: {e}")
-    
-                    # If a valid calculated_value is found, append the result to session_state
+
                     if calculated_value is not None:
                         st.session_state.results.append({
                             'Location Name': location_name,
@@ -353,9 +310,7 @@ if file_upload:
                         st.warning(f"No value calculated for {location_name}.")
                 else:
                     st.warning(f"No value calculated for {location_name}.")
-                    
 
- # Check if polygons_df is populated for polygons
 if polygons_df is not None:
     st.write("Preview of the uploaded polygons data:")
     st.dataframe(polygons_df.head())
@@ -364,9 +319,9 @@ if polygons_df is not None:
 
     for _, row in polygons_df.iterrows():
         polygon = row['geometry']
-            if polygon.is_valid:
-                gdf = gpd.GeoDataFrame([row], geometry=[polygon], crs=polygons_df.crs)
-                m.add_gdf(gdf=gdf, layer_name=row.get('name', 'Unnamed Polygon'))
+        if polygon.is_valid:
+            gdf = gpd.GeoDataFrame([row], geometry=[polygon], crs=polygons_df.crs)
+            m.add_gdf(gdf=gdf, layer_name=row.get('name', 'Unnamed Polygon'))
 
     st.write("Map of Uploaded Polygons:")
     m.to_streamlit()
@@ -395,7 +350,8 @@ if polygons_df is not None:
             result = None
             if index_choice.lower() == 'ndvi':
                 result = calculate_ndvi(image, roi)
-            elif index_choice.lower() == 'ndwi':
+            elif index_choice.lower() ```python
+                == 'ndwi':
                 result = calculate_ndwi(image, roi)
             elif index_choice.lower() == 'average no₂':
                 if 'NO2' in image.bandNames().getInfo():
@@ -406,22 +362,18 @@ if polygons_df is not None:
                 result = process_custom_formula(image, roi, custom_formula)
 
             if result is not None:
-                # Initialize the calculated_value as None
                 calculated_value = None
-    
-            # Check if the result is a dictionary (e.g., custom formula result)
-            if isinstance(result, dict) and 'CustomResult' in result:
-                calculated_value = result['CustomResult']  # Extract the numeric value from the dictionary
-            # If the result is a numeric value (e.g., NDVI, NDWI, or NO2)
-            elif isinstance(result, (int, float)):
-                calculated_value = result
-    
-            # If a valid calculated_value is found, append the result to session_state
-            if calculated_value is not None:
-                st.session_state.results.append({
-                    'Location Name': location_name,
-                    'Calculated Value': calculated_value
-                })
+
+                if isinstance(result, dict) and 'CustomResult' in result:
+                    calculated_value = result['CustomResult']
+                elif isinstance(result, (int, float)):
+                    calculated_value = result
+
+                if calculated_value is not None:
+                    st.session_state.results.append({
+                        'Location Name': location_name,
+                        'Calculated Value': calculated_value
+                    })
 
 # After processing, show the results
 if st.session_state.results:
@@ -441,4 +393,4 @@ if st.session_state.results:
         data=result_df.to_csv(index=False).encode('utf-8'),
         file_name=filename,
         mime='text/csv'
-    )
+    )