downloading data starting from 1/1/2024 in a new thread

pag/monitor.py

@@ -11,6 +11,7 @@ import requests
 import process
 from zipfile import ZipFile
 import plotly.express as px
+import threading
 
 def check_authentication():
     if not check_password():
@@ -65,18 +66,45 @@ def get_and_cache_available_dates(_df, field, year, start_date, end_date):
 
 
 
-def get_cuarted_df_for_field(df, field, date, metric, clientName):
+# def get_cuarted_df_for_field(df, field, date, metric, clientName):
+#     curated_date_path =  utils.get_curated_location_img_path(clientName, metric, date, field)
+#     if curated_date_path is not None:
+#         curated_df = gpd.read_file(curated_date_path)
+#     else:
+#         process.Download_image_in_given_date(clientName, metric, df, field, date)
+#         process.mask_downladed_image(clientName, metric, df, field, date)
+#         process.convert_maske_image_to_geodataframe(clientName, metric, df, field, date, df.crs)
+#         curated_date_path =  utils.get_curated_location_img_path(clientName, metric, date, field)
+#         curated_df = gpd.read_file(curated_date_path)
+#     return curated_df
+
+def get_cuarted_df_for_field(df, field, date, metric, clientName, dates):
     curated_date_path =  utils.get_curated_location_img_path(clientName, metric, date, field)
     if curated_date_path is not None:
         curated_df = gpd.read_file(curated_date_path)
+
     else:
-        process.Download_image_in_given_date(clientName, metric, df, field, date)
-        process.mask_downladed_image(clientName, metric, df, field, date)
-        process.convert_maske_image_to_geodataframe(clientName, metric, df, field, date, df.crs)
+        download_date_date(df, field, [date], metric, clientName,)
+        curated_date_path =  utils.get_curated_location_img_path(clientName, metric, date, field)
+        print("curr selected date processed")
+
+
+        dates.remove(date)
+        download_thread = threading.Thread(target=download_date_date, name="Downloader", args=(df, field, dates, metric, clientName))
+        download_thread.start()
+
         curated_date_path =  utils.get_curated_location_img_path(clientName, metric, date, field)
         curated_df = gpd.read_file(curated_date_path)
     return curated_df
 
+def download_date_date(df, field, dates, metric, clientName,):
+    for date in dates:
+        process.Download_image_in_given_date(clientName, metric, df, field, date)
+        process.mask_downladed_image(clientName, metric, df, field, date)
+        process.convert_maske_image_to_geodataframe(clientName, metric, df, field, date, df.crs)
+    print(f"finished downloading prev dates data")
+    return 
+
 
 def track(metric, field_name, src_df, client_name):
     st.title(":green[Select Date and Start Monitoring]")
@@ -132,8 +160,8 @@ def track(metric, field_name, src_df, client_name):
         # Get the field data at the selected date
         with st.spinner('Loading Field Data...'):
             # Get the metric data and cloud cover data for the selected field and date
-            metric_data = get_cuarted_df_for_field(src_df, field_name, date, metric, client_name)
-            cloud_cover_data = get_cuarted_df_for_field(src_df, field_name, date, 'CLP', client_name)
+            metric_data = get_cuarted_df_for_field(src_df, field_name, date, metric, client_name, dates)
+            cloud_cover_data = get_cuarted_df_for_field(src_df, field_name, date, 'CLP', client_name, dates)
             
             #Merge the metric and cloud cover data on the geometry column
             field_data = metric_data.merge(cloud_cover_data, on='geometry')