Add Legend and Histogram

app.py

@@ -77,7 +77,7 @@ def get_gdf_from_url(url):
     """Downloads and reads a KML/GeoJSON from a URL."""
     if not url or not url.strip():
         return None
-    
+
     # Handle Google Drive URLs
     if "drive.google.com" in url:
         if "/file/d/" in url:
@@ -174,32 +174,32 @@ def get_wayback_data():
         url = "https://wayback.maptiles.arcgis.com/arcgis/rest/services/World_Imagery/MapServer/WMTS/1.0.0/WMTSCapabilities.xml"
         response = requests.get(url)
         response.raise_for_status()  # Ensure request was successful
-    
+
         # Parse XML
         root = ET.fromstring(response.content)
-    
+
         ns = {
-            "wmts": "https://www.opengis.net/wmts/1.0",
-            "ows": "https://www.opengis.net/ows/1.1",
-            "xlink": "https://www.w3.org/1999/xlink",
+            "wmts": "http://www.opengis.net/wmts/1.0",
+            "ows": "http://www.opengis.net/ows/1.1",
+            "xlink": "http://www.w3.org/1999/xlink",
         }
-    
+
         # Use a robust XPath to find all 'Layer' elements anywhere in the document.
         # This is less brittle than specifying the full path.
         layers = root.findall(".//wmts:Contents/wmts:Layer", ns)
-    
+
         layer_data = []
         for layer in layers:
             title = layer.find("ows:Title", ns)
             identifier = layer.find("ows:Identifier", ns)
             resource = layer.find("wmts:ResourceURL", ns)  # Tile URL template
-    
+
             title_text = title.text if title is not None else "N/A"
             identifier_text = identifier.text if identifier is not None else "N/A"
             url_template = resource.get("template") if resource is not None else "N/A"
-    
+
             layer_data.append({"Title": title_text, "ResourceURL_Template": url_template})
-    
+
         wayback_df = pd.DataFrame(layer_data)
         wayback_df["date"] = pd.to_datetime(wayback_df["Title"].str.extract(r"(\d{4}-\d{2}-\d{2})").squeeze(), errors="coerce")
         wayback_df.set_index("date", inplace=True)
@@ -344,7 +344,7 @@ def process_and_display(file_obj, url_str, buffer_m, progress=gr.Progress()):
             .replace("{TileCol}", "{x}")
         )
         folium.TileLayer(tiles=wayback_url, attr="Esri", name=wayback_title).add_to(m)
-            
+
     m = add_geometry_to_map(m, geometry_gdf, buffer_geometry_gdf, opacity=0.3)
     m.add_child(folium.LayerControl())
     bounds = geometry_gdf.to_crs(epsg=4326).total_bounds
@@ -426,7 +426,7 @@ def calculate_indices(
         for veg_index in veg_indices:
             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:
@@ -442,42 +442,60 @@ def calculate_indices(
         traceback.print_exc()
         return f"An error occurred during calculation: {e}", None, None, None
 
+# NEW/MODIFIED FUNCTIONS START HERE
+
+def get_histogram(index_name, image, geometry, bins):
+    """Calculates the histogram for an image within a given geometry using GEE."""
+    try:
+        # Request histogram data from Earth Engine
+        hist_info = image.reduceRegion(
+            reducer=ee.Reducer.fixedHistogram(min=bins[0], max=bins[-1], steps=len(bins)-1),
+            geometry=geometry,
+            scale=10,  # Scale in meters appropriate for Sentinel-2
+            maxPixels=1e9
+        ).get(index_name).getInfo()
+
+        # Extract histogram counts
+        if hist_info:
+            histogram = [item[1] for item in hist_info]
+            return np.array(histogram), bins
+        else:
+            # Return empty histogram if no data
+            return np.array([0] * (len(bins) - 1)), bins
+    except Exception as e:
+        print(f"Could not compute histogram for {index_name}: {e}")
+        return np.array([0] * (len(bins) - 1)), bins
+
 def generate_comparison_maps(geometry_json, selected_index, selected_years, evi_vars, date_start_str, date_end_str, progress=gr.Progress()):
-    """Generates side-by-side maps for a selected index and two selected years."""
+    """Generates side-by-side maps for a selected index and two selected years with a classified legend."""
     if not geometry_json or not selected_index or not selected_years:
         return "Please process a file and select an index and years first.", "", ""
     if len(selected_years) != 2:
         return "Please select exactly two years to compare.", "", ""
-    
+
     one_time_setup()
     geometry_gdf = gpd.read_file(geometry_json).to_crs(4326)
     ee_geometry = ee.Geometry(json.loads(geometry_gdf.to_json())['features'][0]['geometry'])
     bounds = geometry_gdf.total_bounds
     map_bounds = [[bounds[1], bounds[0]], [bounds[3], bounds[2]]]
-    
+
     start_month, start_day = map(int, date_start_str.split('-'))
     end_month, end_day = map(int, date_end_str.split('-'))
 
-    vis_params = {
-        "min": 0.0, "max": 1.0,
-        "palette": ['FFFFFF', 'CE7E45', 'DF923D', 'F1B555', 'FCD163', '99B718', '74A901', '66A000', '529400', '3E8601', '207401', '056201', '004C00', '023B01', '012E01', '011D01', '011301']
-    }
-
     maps_html = []
     for i, year in enumerate(selected_years):
         progress((i + 1) / 2, desc=f"Generating map for {year}")
         start_date = f"{year}-{start_month:02d}-{start_day:02d}"
         end_date = f"{year}-{end_month:02d}-{end_day:02d}"
-        
+
+        # --- Wayback Basemap Logic (unchanged) ---
         wayback_url = None
         wayback_title = "Default Satellite"
         if not WAYBACK_DF.empty:
             try:
                 target_date = datetime(int(year), start_month, 15)
-                # Find the index of the closest date in the wayback dataframe
                 nearest_idx = WAYBACK_DF.index.get_indexer([target_date], method='nearest')[0]
                 wayback_item = WAYBACK_DF.iloc[nearest_idx]
-                
                 wayback_title = f"Esri Wayback ({wayback_item.name.strftime('%Y-%m-%d')})"
                 wayback_url = (
                     wayback_item["ResourceURL_Template"]
@@ -488,9 +506,9 @@ def generate_comparison_maps(geometry_json, selected_index, selected_years, evi_
                 )
             except Exception as e:
                 print(f"Could not find a suitable Wayback basemap for {year}: {e}")
-                # Fallback to default values if any error occurs
                 wayback_url = None
                 wayback_title = "Default Satellite"
+        # --- End Wayback Logic ---
 
         collection = (
             ee.ImageCollection("COPERNICUS/S2_SR_HARMONIZED")
@@ -508,17 +526,60 @@ def generate_comparison_maps(geometry_json, selected_index, selected_years, evi_
             continue
 
         mosaic = collection.qualityMosaic(selected_index)
-        clipped_image = mosaic.select(selected_index).clip(ee_geometry)
 
-        # Create map and add the year-specific basemap
+        # Create the base map
         m = gee_folium.Map(zoom_start=14)
         if wayback_url:
             m.add_tile_layer(wayback_url, name=wayback_title, attribution="Esri")
         else:
             m.add_basemap("SATELLITE")
 
-        m.addLayer(clipped_image, vis_params, f"{selected_index} {year}")
-        m.add_colorbar(vis_params=vis_params, label=f"{selected_index} Value")
+        # --- START: New Legend and Layer Logic ---
+        if selected_index in ["NDVI", "RandomForest", "GujVDI", "CI", "EVI", "EVI2"]:
+            # Define bins for classification
+            bins = [0, 0.2, 0.4, 0.6, 0.8, 1]
+            histogram, bin_edges = get_histogram(selected_index, mosaic.select(selected_index), ee_geometry, bins)
+            
+            total_pix = np.sum(histogram)
+            # Format histogram values as percentages, handle division by zero
+            if total_pix > 0:
+                formatted_histogram = [f"{h*100/total_pix:.2f}" for h in histogram]
+            else:
+                formatted_histogram = ["0.00"] * len(histogram)
+
+            # Add the classified legend to the map
+            m.add_legend(
+                title=f"{selected_index} Classification ({year})",
+                legend_dict={
+                    "0-0.2: Open/Sparse Vegetation ({}%)".format(formatted_histogram[0]): "#FF0000",
+                    "0.2-0.4: Low Vegetation ({}%)".format(formatted_histogram[1]): "#FFFF00",
+                    "0.4-0.6: Moderate Vegetation ({}%)".format(formatted_histogram[2]): "#FFA500",
+                    "0.6-0.8: Dense Vegetation ({}%)".format(formatted_histogram[3]): "#00FE00",
+                    "0.8-1: Very Dense Vegetation ({}%)".format(formatted_histogram[4]): "#00A400",
+                },
+                position="bottomright",
+                draggable=False,
+            )
+            
+            # Define visualization parameters for the classified layer
+            ind_vis_params = {
+                "min": 0,
+                "max": 1,
+                "palette": ["#FF0000", "#FFFF00", "#FFA500", "#00FE00", "#00A400"],
+            }
+            # Add the classified raster layer to the map
+            m.addLayer(mosaic.select(selected_index).clip(ee_geometry), ind_vis_params, f"{selected_index} Classified Layer ({year})")
+        
+        else:  # Fallback for indices without a classified legend (original behavior)
+            vis_params = {
+                "min": 0.0, "max": 1.0,
+                "palette": ['FFFFFF', 'CE7E45', 'DF923D', 'F1B555', 'FCD163', '99B718', '74A901', '66A000', '529400', '3E8601', '207401', '056201', '004C00', '023B01', '012E01', '011D01', '011301']
+            }
+            clipped_image = mosaic.select(selected_index).clip(ee_geometry)
+            m.addLayer(clipped_image, vis_params, f"{selected_index} {year}")
+            m.add_colorbar(vis_params=vis_params, label=f"{selected_index} Value")
+        # --- END: New Legend and Layer Logic ---
+
         folium.GeoJson(geometry_gdf, name="Geometry", style_function=lambda x: {"color": "yellow", "fillOpacity": 0, "weight": 2.5}).add_to(m)
         m.fit_bounds(map_bounds, padding=(10, 10))
         m.addLayerControl()
@@ -529,7 +590,7 @@ def generate_comparison_maps(geometry_json, selected_index, selected_years, evi_
 
     return f"Comparison generated for {selected_years[0]} and {selected_years[1]}.", maps_html[0], maps_html[1]
 
-# --- Gradio Interface ---
+# --- Gradio Interface (unchanged) ---
 theme = gr.themes.Soft(primary_hue="teal", secondary_hue="orange").set(
     background_fill_primary="white"
 )
@@ -556,7 +617,7 @@ with gr.Blocks(theme=theme, title="Kamlan: KML Analyzer") as demo:
             url_input = gr.Textbox(label="Or Provide File URL", placeholder="e.g., https://.../my_file.kml")
             buffer_input = gr.Number(label="Buffer (meters)", value=50)
             process_button = gr.Button("Process Input", variant="primary")
-            
+
             with gr.Accordion("Advanced Settings", open=False):
                 gr.Markdown("### Select Vegetation Indices")
                 all_veg_indices = ["GujVDI", "NDVI", "EVI", "EVI2", "RandomForest", "CI"]
@@ -581,14 +642,14 @@ with gr.Blocks(theme=theme, title="Kamlan: KML Analyzer") as demo:
                     max_year_input = gr.Number(label="End Year", value=today.year, precision=0)
 
             calculate_button = gr.Button("Calculate Vegetation Indices", variant="primary")
-            
+
 
         with gr.Column(scale=2):
             gr.Markdown("## 2. Results")
             info_box = gr.Textbox(label="Status", interactive=False, placeholder="Status messages will appear here...")
             map_output = gr.HTML(label="Map View")
             stats_output = gr.DataFrame(label="Geometry Metrics")
-            
+
             gr.Markdown("### Digital Elevation Model (DEM) and Slope")
             with gr.Row():
                 dem_map_output = gr.HTML(label="DEM Map")
@@ -605,9 +666,9 @@ with gr.Blocks(theme=theme, title="Kamlan: KML Analyzer") as demo:
     with gr.Row():
         comparison_index_select = gr.Radio(all_veg_indices, label="Select Index for Comparison", value="NDVI")
         comparison_years_select = gr.CheckboxGroup(label="Select Two Years to Compare", choices=[])
-    
+
     compare_button = gr.Button("Generate Comparison Maps", variant="secondary")
-    
+
     with gr.Row():
         map_year_1_output = gr.HTML(label="Comparison Map 1")
         map_year_2_output = gr.HTML(label="Comparison Map 2")
@@ -640,15 +701,15 @@ with gr.Blocks(theme=theme, title="Kamlan: KML Analyzer") as demo:
                 geometry_json, buffer_json, veg_indices,
                 evi_vars, date_range, int(min_year), int(max_year), progress
             )
-            
+
             status_message = error_msg or success_msg
             first_plot = plots[0] if plots else None
             df_display = df.round(3) if df is not None else None
-            
+
             available_years = []
             if df is not None and 'Year' in df.columns:
                 available_years = sorted(df['Year'].unique().tolist())
-            
+
             return status_message, df_display, df, first_plot, gr.update(choices=available_years, value=[])
 
         except Exception as e: