Update app.py

app.py

@@ -916,9 +916,9 @@ def create_contour_map(geojson_file, chem_symbol, lat_min, lat_max, long_min, lo
         return {"message": f"An error occurred: {str(e)}"}
                     
 
-def plot_high_value_points(geojson_file, chem_symbol, lat_min, lat_max, long_min, long_max):
+def plot_high_value_points(geojson_file, chem_symbol, lat_min, lat_max, long_min, long_max, threshold):
     """
-    Plots points on a map for geochemical data that have values higher than the average value of the chemical.
+    Plots points on a map for geochemical data that have values higher than a specified threshold.
     Also calculates and includes statistics (mean, mode, median, std deviation, variance) of the chemical values.
 
     Parameters:
@@ -928,6 +928,7 @@ def plot_high_value_points(geojson_file, chem_symbol, lat_min, lat_max, long_min
     - lat_max (float): The maximum latitude value.
     - long_min (float): The minimum longitude value.
     - long_max (float): The maximum longitude value.
+    - threshold (float): The threshold value for chemical concentration in ppm.
 
     Returns:
     - dict: A dictionary containing the message and HTML content of the generated map.
@@ -936,16 +937,19 @@ def plot_high_value_points(geojson_file, chem_symbol, lat_min, lat_max, long_min
                 "html": str (optional)
             }
     """
+    # Define lat/long limits
     lat_limits = (lat_min, lat_max)
     long_limits = (long_min, long_max)
 
     try:
+        # Load GeoJSON data
         gdf = gpd.read_file(geojson_file)
         
         # Check if the chemical symbol exists in the data
         if chem_symbol not in gdf.columns:
             return {"message": f"Error: Chemical symbol '{chem_symbol}' not found in the data."}
         
+        # Filter data for the specified lat/long limits
         gdf = gdf[(gdf.geometry.y >= lat_limits[0]) & (gdf.geometry.y <= lat_limits[1]) & 
                   (gdf.geometry.x >= long_limits[0]) & (gdf.geometry.x <= long_limits[1])]
         
@@ -955,14 +959,15 @@ def plot_high_value_points(geojson_file, chem_symbol, lat_min, lat_max, long_min
         # Calculate statistics of the chemical values
         chem_values = gdf[chem_symbol].values
         mean_value = np.mean(chem_values)
-        mode_value = stats.mode(chem_values)  # Mode can have multiple values, taking the first one
+        mode_value = stats.mode(chem_values)  # Properly access the mode value
         median_value = np.median(chem_values)
         std_deviation = np.std(chem_values)
         variance = np.var(chem_values)
         
-        # Filter points with values higher than the average
-        high_value_points = gdf[gdf[chem_symbol] > mean_value]
+        # Filter points with values higher than the threshold
+        high_value_points = gdf[gdf[chem_symbol] > threshold]
         
+        # Base map centered on the midpoint of the given lat/long limits
         lat_center = (lat_limits[0] + lat_limits[1]) / 2
         long_center = (long_limits[0] + long_limits[1]) / 2
         m = folium.Map(location=[lat_center, long_center], zoom_start=10)
@@ -971,12 +976,13 @@ def plot_high_value_points(geojson_file, chem_symbol, lat_min, lat_max, long_min
         for idx, row in high_value_points.iterrows():
             folium.Marker(
                 location=[row.geometry.y, row.geometry.x],
-                tooltip=f'{chem_symbol}: {row[chem_symbol]}'
+                tooltip=f'{chem_symbol}: {row[chem_symbol]:.2f} ppm'
             ).add_to(m)
         
         # Add layer control to toggle markers
         folium.LayerControl().add_to(m)
         m.add_child(MeasureControl())
+
         north_arrow_svg = """
         <div style="position: fixed; 
                     bottom: 30px; left: 10px; width: 40px; height: 40px; 
@@ -991,14 +997,16 @@ def plot_high_value_points(geojson_file, chem_symbol, lat_min, lat_max, long_min
         </div>
         """
         m.get_root().html.add_child(folium.Element(north_arrow_svg))
+        # Generate the HTML content
         html_content = m.get_root().render()
         
-        message = f"Plotted points with '{chem_symbol}' values higher than mean ({mean_value:.2f})\n"
-        message += f"Mean: {mean_value:.2f}\n"
-        message += f"Mode: {mode_value}\n"
-        message += f"Median: {median_value:.2f}\n"
-        message += f"Standard Deviation: {std_deviation:.2f}\n"
-        message += f"Variance: {variance:.2f}"
+        # Construct the message with statistics
+        message = f"Plotted points with '{chem_symbol}' values higher than {threshold:.2f} ppm\n"
+        message += f"Mean: {mean_value:.2f} ppm\n"
+        message += f"Mode: {mode_value} ppm\n"
+        message += f"Median: {median_value:.2f} ppm\n"
+        message += f"Standard Deviation: {std_deviation:.2f} ppm\n"
+        message += f"Variance: {variance:.2f} ppm"
         
         return {
             "message": message,
@@ -1006,7 +1014,8 @@ def plot_high_value_points(geojson_file, chem_symbol, lat_min, lat_max, long_min
         }
     
     except Exception as e:
-        return {"message": f"An error occurred while generating the statistics: {str(e)}"}
+        return {"message": f"An error occurred: {str(e)}"}
+
     
 
 def Manager_agent(query, lat_min, lat_max, long_min, long_max):