^^

src/pages/1_Historical_Prices.py

@@ -16,19 +16,14 @@ AWS_SECRET_ACCESS_KEY = os.getenv("AWS_SECRET_ACCESS_KEY")
 ROOT_DIR = "./data/"
 MISSING_VALUE_PLACEHOLDER = -1
 
-
 # --- Asynchronous Data Loading Functions ---
 async def async_load_file_s3(object_key: str) -> pd.DataFrame:
     if not AWS_S3_BUCKET or not AWS_ACCESS_KEY_ID or not AWS_SECRET_ACCESS_KEY:
-        raise ValueError(
-            "AWS credentials or bucket name not set in environment variables."
-        )
+        raise ValueError("AWS credentials or bucket name not set in environment variables.")
 
     session = get_session()
     async with session.create_client(
-        "s3",
-        aws_access_key_id=AWS_ACCESS_KEY_ID,
-        aws_secret_access_key=AWS_SECRET_ACCESS_KEY,
+        "s3", aws_access_key_id=AWS_ACCESS_KEY_ID, aws_secret_access_key=AWS_SECRET_ACCESS_KEY
     ) as s3_client:
         response = await s3_client.get_object(Bucket=AWS_S3_BUCKET, Key=object_key)
         status = response.get("ResponseMetadata", {}).get("HTTPStatusCode")
@@ -39,75 +34,54 @@ async def async_load_file_s3(object_key: str) -> pd.DataFrame:
             return pd.read_csv(io.StringIO(content.decode("utf-8")))
         raise ValueError(f"Unsuccessful S3 get_object response. Status - {status}")
 
-
-@st.cache_resource
 async def async_load_geojson_from_s3(object_key: str) -> dict:
     local_file_path = f"{ROOT_DIR}{object_key}"
     if os.path.exists(local_file_path):
         with open(local_file_path, "r") as f:
             return json.load(f)
 
-
-@st.cache_resource
-async def load_all_data_async():
-    departements_df_task = async_load_file_s3(
-        "processed/housing/dataset_departements_housing_prices.csv"
-    )
-    departements_geojson_task = async_load_geojson_from_s3(
-        "processed/referentiel/departements.geojson"
-    )
-    communes_df_task = async_load_file_s3(
-        "processed/housing/dataset_housing_prices.csv"
-    )
-    communes_geojson_task = async_load_geojson_from_s3(
-        "processed/referentiel/communes.geojson"
-    )
+# Internal async function to gather all data loading tasks
+async def _load_all_data_async_internal():
+    departements_df_task = async_load_file_s3("processed/housing/dataset_departements_housing_prices.csv")
+    departements_geojson_task = async_load_geojson_from_s3("processed/referentiel/departements.geojson")
+    communes_df_task = async_load_file_s3("processed/housing/dataset_housing_prices.csv")
+    communes_geojson_task = async_load_geojson_from_s3("processed/referentiel/communes.geojson")
 
     return await asyncio.gather(
         departements_df_task,
         departements_geojson_task,
         communes_df_task,
-        communes_geojson_task,
+        communes_geojson_task
     )
 
+@st.cache_resource
+def load_all_data_sync_wrapper():
+    # This synchronous wrapper runs the async data loading
+    return asyncio.run(_load_all_data_async_internal())
 
 # --- Helper for Color Scale ---
 def _generate_custom_colorscale(min_val, max_val):
     custom_colorscale = [[0.0, "lightgrey"]]
     reversed_rdylgn_colors = pcolors.diverging.RdYlGn[::-1]
 
-    normalized_min_actual = (min_val - MISSING_VALUE_PLACEHOLDER) / (
-        max_val - MISSING_VALUE_PLACEHOLDER
-    )
-    normalized_max_actual = (max_val - MISSING_VALUE_PLACEHOLDER) / (
-        max_val - MISSING_VALUE_PLACEHOLDER
-    )
+    normalized_min_actual = (min_val - MISSING_VALUE_PLACEHOLDER) / (max_val - MISSING_VALUE_PLACEHOLDER)
+    normalized_max_actual = (max_val - MISSING_VALUE_PLACEHOLDER) / (max_val - MISSING_VALUE_PLACEHOLDER)
 
     num_steps = len(reversed_rdylgn_colors)
     for i, color in enumerate(reversed_rdylgn_colors):
-        normalized_point = normalized_min_actual + (
-            normalized_max_actual - normalized_min_actual
-        ) * (i / (num_steps - 1))
+        normalized_point = normalized_min_actual + (normalized_max_actual - normalized_min_actual) * (i / (num_steps - 1))
         if normalized_point > 0.0:
             custom_colorscale.append([normalized_point, color])
     return sorted(custom_colorscale, key=lambda x: x[0])
 
-
 # --- Plotly Figure Generation Functions ---
 @st.cache_data
 def display_choropleth_map(df: pd.DataFrame, geojson_data: dict):
-    df_agg = (
-        df[df["prixm2moyen"] > 0]
-        .groupby(["code_departement", "annee"])["prixm2moyen"]
-        .mean()
-        .reset_index()
-    )
+    df_agg = df[df["prixm2moyen"] > 0].groupby(["code_departement", "annee"])["prixm2moyen"].mean().reset_index()
     min_actual_prixm2moyen = df_agg["prixm2moyen"].min()
     max_actual_prixm2moyen = df_agg["prixm2moyen"].max()
 
-    custom_colorscale = _generate_custom_colorscale(
-        min_actual_prixm2moyen, max_actual_prixm2moyen
-    )
+    custom_colorscale = _generate_custom_colorscale(min_actual_prixm2moyen, max_actual_prixm2moyen)
 
     fig = px.choropleth_map(
         df_agg,
@@ -139,27 +113,20 @@ def display_choropleth_map(df: pd.DataFrame, geojson_data: dict):
         st.warning("No animation updatemenus found.")
     return fig
 
-
 @st.cache_data
 def display_choropleth_map_for_department(
-    df: pd.DataFrame,
-    department_code: str,
-    geojson_data: dict,
-    min_global_prixm2moyen: float,
-    max_global_prixm2moyen: float,
+    df: pd.DataFrame, department_code: str, geojson_data: dict,
+    min_global_prixm2moyen: float, max_global_prixm2moyen: float
 ):
     df_filtered = df[df["code_departement"] == department_code].copy()
 
     filtered_features = [
-        feature
-        for feature in geojson_data["features"]
+        feature for feature in geojson_data["features"]
         if feature["properties"]["code"].startswith(department_code)
     ]
     filtered_geojson = {"type": "FeatureCollection", "features": filtered_features}
 
-    custom_colorscale = _generate_custom_colorscale(
-        min_global_prixm2moyen, max_global_prixm2moyen
-    )
+    custom_colorscale = _generate_custom_colorscale(min_global_prixm2moyen, max_global_prixm2moyen)
 
     center_lat, center_lon, zoom_level = 46.603354, 1.888334, 6
     if filtered_features:
@@ -170,12 +137,9 @@ def display_choropleth_map_for_department(
             center_lat, center_lon = department_centroid.y, department_centroid.x
             minx, miny, maxx, maxy = dissolved_department.total_bounds
             width, height = maxx - minx, maxy - miny
-            if width < 0.2 and height < 0.2:
-                zoom_level = 10
-            elif width < 0.5 and height < 0.5:
-                zoom_level = 9
-            else:
-                zoom_level = 8
+            if width < 0.2 and height < 0.2: zoom_level = 10
+            elif width < 0.5 and height < 0.5: zoom_level = 9
+            else: zoom_level = 8
 
     fig = px.choropleth_map(
         df_filtered,
@@ -207,29 +171,19 @@ def display_choropleth_map_for_department(
         st.warning("No animation updatemenus found.")
     return fig
 
-
 # --- Streamlit App Layout ---
 st.set_page_config(page_title="Oasis", page_icon=":house:", layout="wide")
 
 st.header("Historical Price - France")
 st.subheader("An overview of real estate prices in France from 2015 to 2024")
 
-st.write(
-    "This map shows the average price per square meter in French departments over the years, with a focus on climatic events."
-)
+st.write("This map shows the average price per square meter in French departments over the years, with a focus on climatic events.")
 
 with st.spinner("Loading data and preparing maps..."):
-    # Call the async function directly without asyncio.run()
-    (
-        dataset_departements_housing_prices,
-        departements_geojson,
-        dataset_housing_prices,
-        communes_geojson,
-    ) = load_all_data_async()
-
-    min_global_commune_prixm2moyen = dataset_housing_prices[
-        dataset_housing_prices["prixm2moyen"] > 0
-    ]["prixm2moyen"].min()
+    # Call the synchronous wrapper function
+    dataset_departements_housing_prices, departements_geojson, dataset_housing_prices, communes_geojson = load_all_data_sync_wrapper()
+    
+    min_global_commune_prixm2moyen = dataset_housing_prices[dataset_housing_prices["prixm2moyen"] > 0]["prixm2moyen"].min()
     max_global_commune_prixm2moyen = dataset_housing_prices["prixm2moyen"].max()
 
 fig_france = display_choropleth_map(
@@ -238,36 +192,4 @@ fig_france = display_choropleth_map(
 )
 st.plotly_chart(fig_france, use_container_width=True)
 st.write("Hover over the map to see detailed information for each department and year.")
-st.write(
-    "Missing values are represented in light grey, while actual data is shown in a gradient from red (high prices) to green (low prices)."
-)
-st.write(
-    "Note: The color scale is customized to highlight missing values in light grey, while the actual data is represented using a reversed RdYlGn color scale, where red indicates higher prices and green indicates lower prices."
-)
-
-st.subheader("Select a Department to View Commune Prices")
-selected_departement = st.selectbox(
-    "Select a Department",
-    options=dataset_housing_prices["code_departement"].unique(),
-)
-
-fig_department = display_choropleth_map_for_department(
-    dataset_housing_prices,
-    selected_departement,
-    communes_geojson,
-    min_global_commune_prixm2moyen,
-    max_global_commune_prixm2moyen,
-)
-
-st.subheader("Average Price per Square Meter in French Communes (2015-2024)")
-st.plotly_chart(fig_department, use_container_width=True)
-st.write("Hover over the map to see detailed information for each commune and year.")
-st.write(
-    "Missing values are represented in light grey, while actual data is shown in a gradient from red (high prices) to green (low prices)."
-)
-st.write(
-    "Note: The color scale is customized to highlight missing values in light grey, while the actual data is represented using a reversed RdYlGn color scale, where red indicates higher prices and green indicates lower prices."
-)
-st.write(
-    "The map is animated by year, allowing you to see how the average price per square meter changes over time."
-)
+st.write("Missing values are represented in light grey.")