feat: add fauna statistics

src/app.py

@@ -7,7 +7,7 @@ import streamlit as st
 from image_preprocessing import get_image_caption, get_images, resize_image
 import plotly.express as px
 
-from statistics import get_plot_from_most_common_elements
+from park_statistics import get_plot_from_most_common_elements, aggregate_fauna_elements
 from data_models.sql_connection import get_db_connection
 from data_models.park_manager import ParkManager
 from data_models.image_manager import ImageManager
@@ -94,6 +94,12 @@ def display_stats() -> None:
     df = pd.DataFrame(predictions)
     st.markdown("## Most common elements")
     st.plotly_chart(get_plot_from_most_common_elements(df, "built_elements", "elements"))
+
+    st.markdown("## Fauna identification")
+    fauna_elements = aggregate_fauna_elements(df)
+    fauna_elements = pd.DataFrame(fauna_elements.items(), columns=["fauna", "count"])
+    st.plotly_chart(px.pie(fauna_elements, names="fauna", values="count", labels={"count": "# Animals", "fauna": "Fauna"}))
+
     st.markdown("## Vegetation detection")
     st.plotly_chart(get_plot_from_most_common_elements(df, "vegetation_detection", "vegetation"))
 

src/{statistics.py → park_statistics.py}

@@ -36,3 +36,15 @@ def get_plot_from_most_common_elements(df: pd.DataFrame, column: str, key: str =
         y="count",
         labels={"count": "# Objects", column: "Built Elements"},
     )
+
+def aggregate_fauna_elements(df: pd.DataFrame) -> dict:
+    fauna_elements = {}
+    fauna = df["fauna_identification"].apply(lambda x: ast.literal_eval(x)["fauna"])
+
+    for i in range(len(fauna)):
+        for element in fauna[i]:
+            if element["type"] in fauna_elements:
+                fauna_elements[element["type"]] += element["count"]
+            else:
+                fauna_elements[element["type"]] = element["count"]
+    return fauna_elements