Plot age brackets sidebar

app/app.py

@@ -7,6 +7,7 @@ import geopandas as gpd
 from datetime import datetime
 
 import seaborn as sns
+import matplotlib.pyplot as plt
 
 from pandas.core.frame import functools
 # Load data and compute static values
@@ -80,13 +81,39 @@ with ui.layout_columns(col_widths=[8, 4]):
                 print((y, x))
                 score = initBackend.get_station_score((y, x), radius=input.rad())
                 return f"Score: {score}"
-            @render.plot(alt="A Seaborn histogram on penguin body mass in grams.")  
-            def plot():  
-                ax = sns.histplot(data=[1,2,3,4,5,6,7,8,9], x="body_mass_g", bins=input.n())  
-                ax.set_title("Palmer Penguins")
-                ax.set_xlabel("Mass (g)")
-                ax.set_ylabel("Count")
-                return ax  
+            @render.plot(alt="A bar chart of age bracket data.")
+            def plot():
+                print("Generating age bracket bar chart")
+                
+                # Dummy coords
+                # station_coord = (-21.910388, 64.144947)
+                
+                x,y = stop.get()
+                station_coord = (y,x)
+
+                # Fetch age bracket data from the Data_provider instance
+                age_data = initBackend.get_station_score(station_coord)['age_data']  # Assume this returns a dictionary or DataFrame
+                    
+                # Example structure: {'0-4 ára': 120, '5-9 ára': 140, ...}
+                age_brackets = list(age_data.keys())
+                populations = list(age_data.values())
+                
+                # Create a Matplotlib figure
+                fig, ax = plt.subplots(figsize=(8, 4))
+                
+                # Create the bar chart
+                ax.bar(age_brackets, populations, color='skyblue')
+                
+                # Customize the plot
+                ax.set_title("Population by Age Bracket")
+                ax.set_xlabel("Age Bracket")
+                ax.set_ylabel("Population")
+                ax.set_xticks(range(len(age_brackets)))
+                ax.set_xticklabels(age_brackets, rotation=45, ha="right")
+                
+                # Return the figure for rendering in Shiny
+                return fig
+
                         
 
         

app/data_processing/point_scoring.py

@@ -54,7 +54,7 @@ def score_current(station_coord, df_features, cov_smsv, w_density, w_income, w_a
 
         density_score = smsv_info["density"].iloc[0] * w_density
         total_score += (age_score + income_score + density_score) * smsv["coverage_percentage"] # TODO: Area of the cricle * percent covered / total area of the small area
-    return {"total_score": total_score, "income_score": income_score, "age_score": age_score, "density_score": density_score}
+    return {"total_score": total_score, "income_score": income_score, "age_score": age_score, "density_score": density_score, "age_data": age_dist}
 
 def get_age_score(age_distribution):
     """