new updates

app.py

@@ -3,8 +3,8 @@ import streamlit as st
 import authentication 
 import streamlit as st
 from pag import add_field, edit, monitor
- 
-# from pages import add_field, edit, moniter
+
+
 def authenticate_user():
     st.title("Welcome to :orange[Field Monitoring App]")
     st.markdown("""
@@ -28,22 +28,22 @@ def authenticate_user():
 def main():
     
     if "authenticated" not in st.session_state:
-        st.session_state.authenticated = False
+        st.session_state.authenticated = False 
 
     if st.session_state.authenticated:
-        st.sidebar.title(":blue[Field Management Options]")
+        st.sidebar.title("Navigation")
         options = st.sidebar.radio("Choose an option:", 
-                                   ("Add Field", "Edit Fields/Add Field Info", "Monitor"))
+                                   ("Add Field", "Manage Fields", "Monitor Fields"))
         
         if options == "Add Field":
             st.title("Welcome to :orange[Field Monitoring App]")
 
             add_field.add_drawing()
             
-        elif options == "Edit Fields/Add Field Info":
+        elif options == "Manage Fields":
             st.title("Welcome to :orange[Field Monitoring App]")
             edit.edit_fields()
-        elif options == "Monitor":
+        elif options == "Monitor Fields":
             st.title("Welcome to :orange[Field Monitoring App]")
             monitor.monitor_fields()
     else:

main.py

@@ -2,9 +2,9 @@
 import streamlit as st
 import authentication 
 import streamlit as st
-from pag import add_field, edit, moniter
- 
-# from pages import add_field, edit, moniter
+from pag import add_field, edit, monitor
+
+
 def authenticate_user():
     st.title("Welcome to :orange[Field Monitoring App]")
     st.markdown("""
@@ -12,6 +12,7 @@ def authenticate_user():
             .stSelectbox > div > div {cursor: pointer;}
             </style>
             """, unsafe_allow_html=True)
+
     if not st.session_state.authenticated:
         choice = st.selectbox("Interested? Sign up or log in if you have an account",options=["Home","Login","SignUp"])
 
@@ -28,24 +29,24 @@ def authenticate_user():
 def main():
     
     if "authenticated" not in st.session_state:
-        st.session_state.authenticated = False
+        st.session_state.authenticated = False 
 
     if st.session_state.authenticated:
         st.sidebar.title("Navigation")
         options = st.sidebar.radio("Choose an option:", 
-                                   ("Add Field", "Edit", "Monitor"))
+                                   ("Add Field", "Manage Fields", "Monitor Fields"))
         
         if options == "Add Field":
             st.title("Welcome to :orange[Field Monitoring App]")
 
             add_field.add_drawing()
             
-        elif options == "Edit":
+        elif options == "Manage Fields":
             st.title("Welcome to :orange[Field Monitoring App]")
             edit.edit_fields()
-        elif options == "Monitor":
+        elif options == "Monitor Fields":
             st.title("Welcome to :orange[Field Monitoring App]")
-            moniter.monitor_fields()
+            monitor.monitor_fields()
     else:
         authenticate_user()
 if __name__ == "__main__":

pag/add_field.py

@@ -54,7 +54,7 @@ def display_map_and_drawing_controls(m, center_start):
     zoom_start = 13
     if st.session_state['active_drawing'] is None:
         st.info("IMPORTANT: Click on the drawing to confirm the drawn field", icon="🚨")
-        sat_basemap = utils.basemaps['Google Satellite']
+        sat_basemap = utils.basemaps['Google Satellite Hybrid']  # Change this line to use 'Google Satellite Hybrid'
         sat_basemap.add_to(m)
         folium.LayerControl().add_to(m)
         output = st_folium(m, center=center_start, zoom=zoom_start, key="new", width=800)

pag/edit.py

@@ -5,10 +5,9 @@ import geopandas as gpd
 from streamlit_folium import st_folium, folium_static
 from authentication import greeting, check_password
 import folium
+import json
 import time
-def check_authentication():
-    if not check_password():
-        st.stop()
+import pandas as pd
 
 
 def add_properties(df, col_name, value, field_name):
@@ -37,17 +36,24 @@ def read_custom_property():
     return custom_property_name, custom_property_value
 
 
-
-
-
 def edit_fields():
-    current_user = greeting("Changed your mind? Edit , Add or Delete Fields easily")
-    file_path = f"fields_{current_user}.parquet"
-    if os.path.exists(file_path):
-        gdf = gpd.read_parquet(file_path)
+    current_user = greeting("Manage your fields")
+    fields_file_path = f"fields_{current_user}.parquet"
+    history_file_path = f"history_{current_user}.csv"
+    
+    # Load or initialize the GeoDataFrame for fields
+    if os.path.exists(fields_file_path):
+        gdf = gpd.read_parquet(fields_file_path)
     else:
-        st.info("No Fields Added Yet!")
+        st.info("No fields added yet!")
         return
+    
+    # Load or initialize the DataFrame for field usage history
+    if os.path.exists(history_file_path):
+        history_df = pd.read_csv(history_file_path)
+    else:
+        history_df = pd.DataFrame(columns=['field_name', 'start_date', 'end_date', 'crop', 'irrigation_method'])
+
     st.info("Hover over the field to show the properties or check the Existing Fields List below")
     fields_map = gdf.explore()
     sat_basemap = utils.basemaps['Google Satellite']
@@ -56,32 +62,27 @@ def edit_fields():
     folium_static(fields_map, height=300, width=600)
     
     with st.expander("Existing Fields List", expanded=False):
+        # lis = [(f"Name:{gdf.iloc[i]['name']}",f"location: {gdf.iloc[i]['geometry']}" )for i in range(len(gdf))]
         st.write(gdf)
 
     field_name = select_field(gdf)
     if field_name == "Select Field":
         st.info("No Field Selected Yet!")
-    
     else:
-        delete_edit = st.radio("Delete or Edit Field?", options=["View", "Edit", "Delete"], key="delete_edit", help="Select the operation to perform")
-        if delete_edit == "View":
+        st.subheader(field_name)
+        option_menu = st.radio(f"Please add your {field_name} field information, historical data will help train our AI model", options=["View Field Info", "Add Field Information","Add Field Cultivation History", "Delete"], key="option_menu", help="Select the operation to perform")
+        if option_menu == "View Field Info":
             field = gdf[gdf['name'] == field_name]
             st.write(field)
-        elif delete_edit == "Delete":
-            delete = st.button("Delete Field", key="delete", help="Click to Delete Field", type="primary", use_container_width=True)
-            if delete:
-                if len(gdf) == 1 and (gdf['name'] == field_name).all():  # Check if this is the only field left
-                    os.remove(file_path)  # Delete the .parquet file if it's the last field
-                    st.success("All fields deleted. The data file has been removed.")
-                    time.sleep(0.3)
-                    st.rerun()
-                else:
-                    gdf = gdf[gdf['name'] != field_name]
-                    gdf.to_parquet(file_path)
-                    st.success("Field Deleted Successfully!")
-                    time.sleep(0.3)
-                    st.rerun()
-        else:
+            # Deserialize the usage history for display
+            if len(history_df)>0:
+                st.write("Previous cultivation History:", history_df)
+            else:
+                st.subheader("No cultivation history added for this field.")
+
+        elif option_menu == "Add Field Information":
+         
+
             no_input = True
             crop_type = read_crop_type()
             irrigation_type = read_irrigation_type()
@@ -103,8 +104,64 @@ def edit_fields():
                 st.success("Field Information Updated Successfully!")
                 st.info("Please Select View above to see the updated field information")
 
+        elif option_menu == "Add Field Cultivation History":
+            with st.form(key='history_form', clear_on_submit=True):
+                start_date = st.date_input("Cultivation Start Date", key=f'start_date')
+                end_date = st.date_input("Cultivation End Date", key=f'end_date')
+                crop_planted = st.selectbox("Type of Crop Planted", [' ', 'Wheat', 'Corn', 'Rice',"other"], index=0)
+               
+                irrigation_method = st.selectbox("Irrigation Method Used", ['Rainfed', 'Irrigated', " "], index=2)
+                submit_history = st.form_submit_button("Submit Crop Cycle")
+                if submit_history:
+                    # Check that the start date is before the end date
+                    if start_date < end_date:
+                        # Append new usage entry
+                        new_history = {
+                            'field_name': field_name,
+                            'start_date': str(start_date),
+                            'end_date': str(end_date),
+                            'crop': crop_planted,
+                            'irrigation_method': irrigation_method
+                        }
+                        # Use concat instead of append
+                        history_df = pd.concat([history_df, pd.DataFrame([new_history])], ignore_index=True)
+                        history_df.to_csv(history_file_path, index=False)
+                        st.success("Field usage history updated successfully!, fill the form again to add another cultivation history" )
+
+                    else:
+                        st.write("check the entered dates")
+
+        elif option_menu == "Delete":
+            option = st.selectbox("What do you want to delete", options=[f'Delete {field_name} Field', 'Delete a historical entry from the field'])
+        
+            if option == f"Delete {field_name} Field" :
+                delete = st.button("Delete Entire Field", key="delete_field", help="Click to Delete Field", type="primary", use_container_width=True)
+                if delete:
+                    if len(gdf) == 1 and (gdf['name'] == field_name).all():  # Check if this is the only field left
+                        os.remove(fields_file_path)  # Delete the .parquet file if it's the last field
+                        os.remove(history_file_path)
+                        st.success("All fields deleted. The data file has been removed.")
+                        time.sleep(0.3)
+                        st.rerun()
+                    else:
+                        gdf = gdf[gdf['name'] != field_name]
+                        history_df = history_df[history_df["field_name"] != field_name ]
+                        gdf.to_parquet(fields_file_path)
+                        history_df.to_csv(history_file_path, index=False)
+                        st.success("Field Deleted Successfully!")
+                        time.sleep(0.3)
+                        st.rerun()
+            elif option == "Delete a historical entry from the field":
+            # Allow the user to select which historical entry to delete
+                idx_history_to_delete = st.selectbox("Select a history to delete, select the index of the entry that you want to delete", options=history_df[history_df['field_name'] == field_name].index)
+                if st.button("Confirm Delete Historical Entry", key="delete_history", help="Click to Delete Entry", type="primary", use_container_width=True):
+                    history_df.drop(labels=0, axis=0, index=None, columns=None, level=None, inplace=True, errors='raise')
+                    history_df.to_csv(history_file_path, index=False)
+
+                    st.success("Entry Deleted Successfully!")
+                    time.sleep(0.3)
+                    st.rerun()
 
 if __name__ == '__main__':
-    check_authentication()
 
     edit_fields()

pag/moniter.py

@@ -1,561 +0,0 @@
-import os
-import utils
-import streamlit as st
-import geopandas as gpd
-from streamlit_folium import st_folium, folium_static
-from authentication import greeting, check_password
-import folium
-from senHub import SenHub
-from datetime import datetime
-from sentinelhub import  SHConfig, MimeType
-import requests
-import process
-import joblib
-from zipfile import ZipFile
-import matplotlib.pyplot as plt
-from plotly.subplots import make_subplots
-import plotly.graph_objects as go
-
-def check_authentication():
-    if not check_password():
-        st.stop()
-
-
-
-config = SHConfig()
-config.instance_id       = '6c220beb-90c4-4131-b658-10cddd8d97b9'
-config.sh_client_id      = '17e7c154-7f2d-4139-b1af-cef762385079'
-config.sh_client_secret  = 'KvbQMKZB85ZWEgWuxqiWIVEvTAQEfoF9'
-
-
-def select_field(gdf):
-    names = gdf['name'].tolist()
-    names.append("Select Field")
-    field_name = st.selectbox("Select Field", options=names, key="field_name_monitor", help="Select the field to edit", index=len(names)-1)
-    return field_name
-
-
-def calculate_bbox(df, field):
-    bbox = df.loc[df['name'] == field].bounds
-    r = bbox.iloc[0]
-    return [r.minx, r.miny, r.maxx, r.maxy]
-
-def get_available_dates_for_field(df, field, year, start_date='', end_date=''):
-    bbox = calculate_bbox(df, field)
-    token = SenHub(config).token
-    headers = utils.get_bearer_token_headers(token)
-    if start_date == '' or end_date == '':
-        start_date = f'{year}-01-01'
-        end_date = f'{year}-12-31'
-    data = f'{{ "collections": [ "sentinel-2-l2a" ], "datetime": "{start_date}T00:00:00Z/{end_date}T23:59:59Z", "bbox": {bbox}, "limit": 100, "distinct": "date" }}'
-    response = requests.post('https://services.sentinel-hub.com/api/v1/catalog/search', headers=headers, data=data)
-    try:
-        features = response.json()['features']
-    except:
-        print(response.json())
-        features = []
-    return features
-
-@st.cache_data
-def get_and_cache_available_dates(_df, field, year, start_date, end_date):
-    dates = get_available_dates_for_field(_df, field, year, start_date, end_date)
-    print(f'Caching Dates for {field}')
-    return dates
-
-
-
-
-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):
-    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 track(metric, field_name, src_df, client_name):
-
-    dates = []
-    date = -1
-    if 'dates' not in st.session_state:
-        st.session_state['dates'] = dates
-    else:
-        dates = st.session_state['dates']
-    if 'date' not in st.session_state:
-        st.session_state['date'] = date
-    else:
-        date = st.session_state['date']
-
-    # Give the user the option to select year, start date and end date
-    # with st.expander('Select Year, Start Date and End Date'):
-    #     # Get the year
-    #     years = [f'20{i}' for i in range(22, 25)]
-    #     year = st.selectbox('Select Year: ', years, index=len(years)-2, key=f'Select Year Dropdown Menu - {metric}')
-        
-    #     # Set the min, max and default values for start and end dates
-    #     min_val = f'{year}-01-01'
-    #     max_val = f'{year}-12-31'
-    #     default_val = f'{year}-11-01'
-    #     min_val = datetime.strptime(min_val, '%Y-%m-%d')
-    #     max_val = datetime.strptime(max_val, '%Y-%m-%d')
-    #     default_val = datetime.strptime(default_val, '%Y-%m-%d')
-
-    #     # Get the start and end dates
-    #     start_date = st.date_input('Start Date', value=default_val, min_value=min_val, max_value=max_val, key=f'Start Date - {metric}')
-    #     end_date = st.date_input('End Date', value=max_val, min_value=min_val, max_value=max_val, key=f'End Date - {metric}')
-
-
-    # Get the dates with available data for that field when the user clicks the button
-    # get_dates_button = st.button(f'Get Dates for Field {field_name} (Field ID: {field_name}) in {year} (from {start_date} to {end_date})',
-    #                                 key=f'Get Dates Button - {metric}',
-    #                                 help='Click to get the dates with available data for the selected field',
-    #                                 use_container_width=True, type='primary')
-    # if get_dates_button:
-    if True:
-        start_date = '2024-01-01'
-        today = datetime.today()
-        end_date = today.strftime('%Y-%m-%d')
-        year = '2024'
-
-        dates = get_and_cache_available_dates(src_df, field_name, year, start_date, end_date)
-        # Add None to the end of the list to be used as a default value
-        # dates.append(-1)
-        #sort the dates from earliest to today
-        dates = sorted(dates)
-
-        #Add the dates to the session state
-        st.session_state['dates'] = dates
-
-    # Display the dropdown menu
-    if len(dates) > 0:
-        date = st.selectbox('Select Observation Date: ', dates, index=len(dates)-1, key=f'Select Date Dropdown Menu - {metric}')
-        if date != -1:
-            st.write('You selected:', date)
-            #Add the date to the session state
-            st.session_state['date'] = date
-        else:
-            st.write('Please Select A Date')
-    else:
-        st.info('No dates available for the selected field and dates range, select a different range or click the button to fetch the dates again')
-
-
-    st.markdown('---')
-    st.header('Show Field Data')
-
-    # If a field and a date are selected, display the field data
-    if date != -1:   
-
-        # 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)
-            
-            #Merge the metric and cloud cover data on the geometry column
-            field_data = metric_data.merge(cloud_cover_data, on='geometry')
-
-        # Display the field data
-        st.write(f'Field Data for {field_name} (Field ID: {field_name}) on {date}')
-        st.write(field_data.head(2))
-
-        #Get Avarage Cloud Cover
-        avg_clp = field_data[f'CLP_{date}'].mean() *100
-
-        # If the avarage cloud cover is greater than 80%, display a warning message
-        if avg_clp > 80:
-            st.warning(f'⚠️ The Avarage Cloud Cover is {avg_clp}%')
-            st.info('Please Select A Different Date')
-
-        ## Generate the field data Map ##
-
-        #Title, Colormap and Legend
-        title = f'{metric} for selected field {field_name} (Field ID: {field_name}) in {date}'
-        cmap = 'RdYlGn'
-
-        # Create a map of the field data
-        field_data_map  = field_data.explore(
-            column=f'{metric}_{date}',
-            cmap=cmap,
-            legend=True,
-            vmin=0,
-            vmax=1,
-            marker_type='circle', marker_kwds={'radius':5.3, 'fill':True})
-        
-        # Add Google Satellite as a base map
-        google_map = utils.basemaps['Google Satellite']
-        google_map.add_to(field_data_map)
-
-        # Display the map
-        st_folium(field_data_map, width = 725, key=f'Field Data Map - {metric}')
-
-
-        #Dwonload Links
-
-        # If the field data is not empty, display the download links
-        if len(field_data) > 0:
-            # Create two columns for the download links
-            download_as_shp_col, download_as_tiff_col = st.columns(2)
-
-            # Create a shapefile of the field data and add a download link
-            with download_as_shp_col:
-
-                #Set the shapefile name and path based on the field id, metric and date
-                extension = 'shp'
-                shapefilename = f"{field_name}_{metric}_{date}.{extension}"
-                path = f'./shapefiles/{field_name}/{metric}/{extension}'
-
-                # Create the target directory if it doesn't exist
-                os.makedirs(path, exist_ok=True)
-                
-                # Save the field data as a shapefile
-                field_data.to_file(f'{path}/{shapefilename}')
-
-                # Create a zip file of the shapefile
-                files = []
-                for i in os.listdir(path):
-                    if os.path.isfile(os.path.join(path,i)):
-                        if i[0:len(shapefilename)] == shapefilename:
-                            files.append(os.path.join(path,i))
-                zipFileName = f'{path}/{field_name}_{metric}_{date}.zip'
-                zipObj = ZipFile(zipFileName, 'w')
-                for file in files:
-                    zipObj.write(file)
-                zipObj.close()
-
-                # Add a download link for the zip file
-                with open(zipFileName, 'rb') as f:
-                    st.download_button('Download as ShapeFile', f,file_name=zipFileName)
-
-            # Get the tiff file path and create a download link
-            with download_as_tiff_col:
-                #get the tiff file path
-                tiff_path = utils.get_masked_location_img_path(client_name, metric, date, field_name)
-                # Add a download link for the tiff file
-                donwnload_filename = f'{metric}_{field_name}_{date}.tiff'
-                with open(tiff_path, 'rb') as f:
-                    st.download_button('Download as Tiff File', f,file_name=donwnload_filename)
-
-    else:
-        st.info('Please Select A Field and A Date')
-    
-
-    # st.markdown('---')
-    # st.header('Show Historic Averages')
-
-
-    # #Let the user select the year, start date and end date
-    # with st.expander('Select Year, Start Date and End Date'):
-    #     # Get the year
-    #     years = [f'20{i}' for i in range(22, 25)]
-    #     year = st.selectbox('Select Year: ', years, index=len(years)-2, key=f'Select Year Dropdown Menu - {metric}- Historic Averages')
-        
-    #     # Set the start and end dates to the first and last dates of the year
-    #     start_date = f'{year}-01-01'
-    #     end_date = f'{year}-12-31'
-
-    # # Get the dates for historic averages
-    # historic_avarages_dates_for_field = get_and_cache_available_dates(src_df, field_name, year, start_date, end_date)
-
-    # # Convert the dates to datetime objects and sort them ascendingly then convert them back to strings
-    # historic_avarages_dates_for_field = [datetime.strptime(date, '%Y-%m-%d') for date in historic_avarages_dates_for_field]
-    # historic_avarages_dates_for_field.sort()
-    # historic_avarages_dates_for_field = [datetime.strftime(date, '%Y-%m-%d') for date in historic_avarages_dates_for_field]
-
-    # # Get the number of dates
-    # num_historic_dates = len(historic_avarages_dates_for_field)
-    # st.write(f' Found {num_historic_dates} dates for field {field_name} in {year} (from {start_date} to {end_date})')
-
-    # # Display the historic averages when the user clicks the button
-    # display_historic_avgs_button = st.button(f'Display Historic Averages for Field {field_name} (Field ID: {field_name}) in {year} (from {start_date} to {end_date})',
-    #                                             key=f'Display Historic Averages Button - {metric}',
-    #                                             help='Click to display the historic averages for the selected field',
-    #                                             use_container_width=True, type='primary')
-    
-    # # If the button is clicked, display the historic averages
-    # if display_historic_avgs_button:
-
-    #     #Initlize the historic averages cache dir and file path
-    #     historic_avarages_cache_dir = './historic_avarages_cache'
-    #     historic_avarages_cache_path = f'{historic_avarages_cache_dir}/historic_avarages_cache.joblib'
-    #     historic_avarages_cache_clp_path = f'{historic_avarages_cache_dir}/historic_avarages_cache_clp.joblib'
-
-    #     # Load the historic averages cache if it exists, else create it
-    #     if os.path.exists(historic_avarages_cache_path):
-    #         historic_avarages_cache = joblib.load(historic_avarages_cache_path)
-    #     else:
-    #         os.makedirs(historic_avarages_cache_dir, exist_ok=True)
-    #         joblib.dump({}, historic_avarages_cache_path)
-    #         historic_avarages_cache = joblib.load(historic_avarages_cache_path)
-    #     if os.path.exists(historic_avarages_cache_clp_path):
-    #         historic_avarages_cache_clp = joblib.load(historic_avarages_cache_clp_path)
-    #     else:
-    #         os.makedirs(historic_avarages_cache_dir, exist_ok=True)
-    #         joblib.dump({}, historic_avarages_cache_clp_path)
-    #         historic_avarages_cache_clp = joblib.load(historic_avarages_cache_clp_path)
-
-    #     found_in_cache = False
-    #     if client_name not in historic_avarages_cache:
-    #         historic_avarages_cache[client_name] = {}
-    #     if metric not in historic_avarages_cache[client_name]:
-    #         historic_avarages_cache[client_name][metric] = {}
-    #     if field_name not in historic_avarages_cache[client_name][metric]:
-    #         historic_avarages_cache[client_name][metric][field_name] = {}
-    #     if year not in historic_avarages_cache[client_name][metric][field_name]:
-    #         historic_avarages_cache[client_name][metric][field_name][year] = {}
-    #     if len(historic_avarages_cache[client_name][metric][field_name][year]) > 0:
-    #         found_in_cache = True
-
-
-    #     #Check if the field and year are in the cache_clp for the current metric and client
-    #     found_in_cache_clp = False
-    #     if client_name not in historic_avarages_cache_clp:
-    #         historic_avarages_cache_clp[client_name] = {}
-    #     if 'CLP' not in historic_avarages_cache_clp[client_name]:
-    #         historic_avarages_cache_clp[client_name]['CLP'] = {}
-    #     if field_name not in historic_avarages_cache_clp[client_name]['CLP']:
-    #         historic_avarages_cache_clp[client_name]['CLP'][field_name] = {}
-    #     if year not in historic_avarages_cache_clp[client_name]['CLP'][field_name]:
-    #         historic_avarages_cache_clp[client_name]['CLP'][field_name][year] = {}
-    #     if len(historic_avarages_cache_clp[client_name]['CLP'][field_name][year]) > 0:
-    #         found_in_cache_clp = True
-
-
-    #     # If Found in cache, get the historic averages from the cache
-    #     if found_in_cache and found_in_cache_clp:
-    #         st.info('Found Historic Averages in Cache')
-    #         historic_avarages = historic_avarages_cache[client_name][metric][field_name][year]['historic_avarages']
-    #         historic_avarages_dates = historic_avarages_cache[client_name][metric][field_name][year]['historic_avarages_dates']
-    #         historic_avarages_clp = historic_avarages_cache_clp[client_name]['CLP'][field_name][year]['historic_avarages_clp']
-
-    #     # Else, calculate the historic averages and add them to the cache
-    #     else:
-    #         st.info('Calculating Historic Averages...')
-
-
-    #         #Empty lists for the historic averages , dates and cloud cover
-    #         historic_avarages = []
-    #         historic_avarages_dates = []
-    #         historic_avarages_clp = []
-
-    #         # Get the historic averages
-    #         dates_for_field_bar = st.progress(0)
-    #         with st.spinner('Calculating Historic Averages...'):
-    #             with st.empty():
-    #                 for i in range(num_historic_dates):
-    #                     # Get the historic average for the current date
-    #                     current_date = historic_avarages_dates_for_field[i]
-    #                     current_df = get_cuarted_df_for_field(src_df, field_name, current_date, metric, client_name)
-    #                     current_df_clp = get_cuarted_df_for_field(src_df, field_name, current_date, 'CLP', client_name)
-    #                     current_avg = current_df[f'{metric}_{current_date}'].mean()
-    #                     current_avg_clp = current_df_clp[f'CLP_{current_date}'].mean()
-    #                     # Add the historic average and date to the lists
-    #                     historic_avarages.append(current_avg)
-    #                     historic_avarages_dates.append(current_date)
-    #                     historic_avarages_clp.append(current_avg_clp)
-    #                     # Update the progress bar
-    #                     dates_for_field_bar.progress((i + 1)/(num_historic_dates))
-
-    #                     # Create a plot of the historic averages with the cloud cover as dashed line and dates as x axis (rotated 90 degrees when needed)
-    #                     fig, ax = plt.subplots(figsize=(5, 3))
-
-    #                     # Set the x axis ticks and labels
-    #                     x = historic_avarages_dates
-    #                     x_ticks = [i for i in range(len(x))]
-    #                     ax.set_xticks(x_ticks)
-                        
-    #                     #Set rotation to 90 degrees if the number of dates is greater than 10
-    #                     rot = 0 if len(x) < 10 else 90
-    #                     ax.set_xticklabels(x, rotation=rot)
-
-    #                     # Set the y axis ticks and labels
-    #                     y1 = historic_avarages
-    #                     y2 = historic_avarages_clp
-    #                     y_ticks = [i/10 for i in range(11)]
-    #                     ax.set_yticks(y_ticks)
-    #                     ax.set_yticklabels(y_ticks)
-
-    #                     # Plot the historic averages and cloud cover
-    #                     ax.plot(x_ticks, y1, label=f'{metric} Historic Averages')
-    #                     ax.plot(x_ticks, y2, '--', label='Cloud Cover')
-    #                     ax.legend()
-
-    #                     # Set the title and axis labels
-    #                     ax.set_title(f'{metric} Historic Averages for {field_name} (Field ID: {field_name}) in {year}')
-    #                     ax.set_xlabel('Date')
-    #                     ax.set_ylabel(f'{metric} Historic Averages')
-
-    #                     # Display the plot
-    #                     st.pyplot(fig, use_container_width=True)
-
-    #         # Add the historic averages to the cache
-    #         historic_avarages_cache[client_name][metric][field_name][year]['historic_avarages'] = historic_avarages
-    #         historic_avarages_cache[client_name][metric][field_name][year]['historic_avarages_dates'] = historic_avarages_dates
-    #         historic_avarages_cache_clp[client_name]['CLP'][field_name][year]['historic_avarages_clp'] = historic_avarages_clp
-    #         # Save the cache
-    #         joblib.dump(historic_avarages_cache, historic_avarages_cache_path)
-    #         joblib.dump(historic_avarages_cache_clp, historic_avarages_cache_clp_path)
-    #         # Tell the user that the historic averages are saved in the cache
-    #         st.info('Historic Averages Saved in Cache')
-    #         st.write(f'Cache Path: {historic_avarages_cache_path}')
-    #         st.write(f'Cache CLP Path: {historic_avarages_cache_clp_path}')
-
-
-    #     # Display the historic averages in nice plotly plot
-    #     fig = make_subplots(specs=[[{"secondary_y": True}]])
-
-    #     # Add the historic averages to the plot
-    #     fig.add_trace(
-    #         go.Scatter(x=historic_avarages_dates, y=historic_avarages, name=f'{metric} Historic Averages'),
-    #         secondary_y=False,
-    #     )
-
-    #     # Add the cloud cover to the plot
-    #     fig.add_trace(
-    #         go.Scatter(x=historic_avarages_dates, y=historic_avarages_clp, name='Cloud Cover'),
-    #         secondary_y=True,
-    #     )
-
-    #     # Set the title and axis labels
-    #     fig.update_layout(title_text=f'{metric} Historic Averages for {field_name} (Field ID: {field_name}) in {year}')
-    #     fig.update_xaxes(title_text='Date')
-    #     fig.update_yaxes(title_text=f'{metric} Historic Averages', secondary_y=False)
-    #     fig.update_yaxes(title_text='Cloud Cover', secondary_y=True)
-
-    #     # Display the plot
-    #     st.plotly_chart(fig)            
-        
-
-    # st.markdown('---')
-    # st.header('Show Historic GIF')
-
-
-    # #Let the user select the year, start date and end date of the GIF
-    # with st.expander('Select Year, Start Date and End Date of the GIF'):
-    #     # Get the year
-    #     years = [f'20{i}' for i in range(16, 23)]
-    #     year = st.selectbox('Select Year: ', years, index=len(years)-2, key=f'Select Year Dropdown Menu - {metric}- Historic Averages GIF')
-        
-    #     # Set the start and end dates to the first and last dates of the year
-    #     start_date = f'{year}-01-01'
-    #     end_date = f'{year}-12-31'
-
-    # # Get the dates for historic GIF
-    # historic_avarages_dates_for_field = get_and_cache_available_dates(src_df, field_name, year, start_date, end_date)
-
-    # # Convert the dates to datetime objects and sort them ascendingly then convert them back to strings
-    # historic_avarages_dates_for_field = [datetime.strptime(date, '%Y-%m-%d') for date in historic_avarages_dates_for_field]
-    # historic_avarages_dates_for_field.sort()
-    # historic_avarages_dates_for_field = [datetime.strftime(date, '%Y-%m-%d') for date in historic_avarages_dates_for_field]
-
-    # # Get the number of dates
-    # num_historic_dates = len(historic_avarages_dates_for_field)
-    # st.write(f' Found {num_historic_dates} dates for field {field_name} in {year} (from {start_date} to {end_date})')
-
-    # # Display the historic GIF when the user clicks the button
-    # display_historic_GIF_button = st.button(f'Display Historic GIF  for Field {field_name} (Field ID: {field_name}) in {year} (from {start_date} to {end_date})',
-    #                                             key=f'Display Historic GIF Button - {metric}',
-    #                                             help='Click to display the historic GIF for the selected field',
-    #                                             use_container_width=True, type='primary')
-    
-    # # If the button is clicked, display the historic GIF
-    # if display_historic_GIF_button:
-
-    #     #Initlize the historic GIF imgs and dates
-    #     st.info('Generating Historic GIF...')
-    #     historic_imgs = []
-    #     historic_imgs_dates = []
-
-    #     # Gen the historic GIF
-    #     dates_for_field_bar = st.progress(0)
-    #     with st.spinner('Generating Historic GIF...'):
-    #         with st.empty():
-    #             for i in range(num_historic_dates):
-    #                 current_date = historic_avarages_dates_for_field[i]
-    #                 current_df = get_cuarted_df_for_field(src_df, field_name, current_date, metric, client_name)
-    #                 historic_imgs.append(current_df)
-    #                 historic_imgs_dates.append(current_date)
-    #                 dates_for_field_bar.progress((i + 1)/(num_historic_dates))
-
-    #                 # Create a fig of the historic Img 
-    #                 fig, ax = plt.subplots(figsize=(10, 5))
-
-    #                 # Get the current img
-    #                 current_df_lat_lon = utils.add_lat_lon_to_gdf_from_geometry(current_df)
-    #                 current_img = utils.gdf_column_to_one_band_array(current_df_lat_lon, f'{metric}_{current_date}')
-
-    #                 # Plot the historic Img
-    #                 title = f'{metric} for selected field {field_name} (Field ID: {field_name}) in {current_date}'
-    #                 ax.imshow(current_img)
-    #                 ax.set_title(title)
-
-    #                 # Display the plot
-    #                 st.pyplot(fig)
-
-    #     # Create the historic GIF
-    #     historic_GIF_name = f'{metric}_{field_name}_{year}.gif'
-    #     st.write('Creating Historic GIF...', historic_GIF_name)
-       
-
-def monitor_fields():
-    current_user = greeting("Let's take a look how these fields are doing")
-    if os.path.exists(f"fields_{current_user}.parquet"):
-        gdf = gpd.read_parquet(f"fields_{current_user}.parquet")
-    else:
-        st.info("No Fields Added Yet!")
-        return
-    # st.info("Hover over the field to show the properties or check the Existing Fields List below")
-    # fields_map = gdf.explore()
-    # sat_basemap = utils.basemaps['Google Satellite']
-    # sat_basemap.add_to(fields_map)
-    # folium.LayerControl().add_to(fields_map)
-    # # output = st_folium(fields_map, key="edit_map", height=300, width=600)
-    # folium_static(fields_map, height=300, width=600)
-    
-    with st.expander("Existing Fields List", expanded=False):
-        st.write(gdf)
-
-    field_name = select_field(gdf)
-    if field_name == "Select Field":
-        st.info("No Field Selected Yet!")
-    
-    else:
-        with st.expander("Metrics Explanation", expanded=False):
-            st.write("NDVI: Normalized Difference Vegetation Index, Mainly used to monitor the health of vegetation")
-            st.write("LAI: Leaf Area Index, Mainly used to monitor the productivity of vegetation")
-            st.write("CAB: Chlorophyll Absorption in the Blue band, Mainly used to monitor the chlorophyll content in vegetation")
-            st.write("NDMI: Normalized Difference Moisture Index, Mainly used to monitor the moisture content in vegetation")
-        st.success("More metrics and analysis features will be added soon")
-        metric = st.radio("Select Metric to Monitor", ["NDVI", "LAI", "CAB", "NDMI"], key="metric", index=0, help="Select the metric to monitor")
-        st.write(f"Monitoring {metric} for {field_name}")
-
-        track(metric, field_name, gdf, current_user)
-
-        
-
-
-if __name__ == '__main__':
-    check_authentication()
-    monitor_fields()

pag/monitor.py

@@ -78,12 +78,6 @@ def get_cuarted_df_for_field(df, field, date, metric, clientName):
     return curated_df
 
 
-
-
-
-
-
-
 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:
@@ -109,31 +103,6 @@ def track(metric, field_name, src_df, client_name):
     else:
         date = st.session_state['date']
 
-    # Give the user the option to select year, start date and end date
-    # with st.expander('Select Year, Start Date and End Date'):
-    #     # Get the year
-    #     years = [f'20{i}' for i in range(22, 25)]
-    #     year = st.selectbox('Select Year: ', years, index=len(years)-2, key=f'Select Year Dropdown Menu - {metric}')
-        
-    #     # Set the min, max and default values for start and end dates
-    #     min_val = f'{year}-01-01'
-    #     max_val = f'{year}-12-31'
-    #     default_val = f'{year}-11-01'
-    #     min_val = datetime.strptime(min_val, '%Y-%m-%d')
-    #     max_val = datetime.strptime(max_val, '%Y-%m-%d')
-    #     default_val = datetime.strptime(default_val, '%Y-%m-%d')
-
-    #     # Get the start and end dates
-    #     start_date = st.date_input('Start Date', value=default_val, min_value=min_val, max_value=max_val, key=f'Start Date - {metric}')
-    #     end_date = st.date_input('End Date', value=max_val, min_value=min_val, max_value=max_val, key=f'End Date - {metric}')
-
-
-    # Get the dates with available data for that field when the user clicks the button
-    # get_dates_button = st.button(f'Get Dates for Field {field_name} (Field ID: {field_name}) in {year} (from {start_date} to {end_date})',
-    #                                 key=f'Get Dates Button - {metric}',
-    #                                 help='Click to get the dates with available data for the selected field',
-    #                                 use_container_width=True, type='primary')
-    # if get_dates_button:
     if True:
         start_date = '2024-01-01'
         today = datetime.today()
@@ -142,7 +111,6 @@ def track(metric, field_name, src_df, client_name):
 
         dates = get_and_cache_available_dates(src_df, field_name, year, start_date, end_date)
         # Add None to the end of the list to be used as a default value
-        # dates.append(-1)
         #sort the dates from earliest to today
         dates = sorted(dates)
 
@@ -260,264 +228,7 @@ def track(metric, field_name, src_df, client_name):
 
     else:
         st.info('Please Select A Field and A Date')
-    
-
-    # st.markdown('---')
-    # st.header('Show Historic Averages')
-
-
-    # #Let the user select the year, start date and end date
-    # with st.expander('Select Year, Start Date and End Date'):
-    #     # Get the year
-    #     years = [f'20{i}' for i in range(22, 25)]
-    #     year = st.selectbox('Select Year: ', years, index=len(years)-2, key=f'Select Year Dropdown Menu - {metric}- Historic Averages')
-        
-    #     # Set the start and end dates to the first and last dates of the year
-    #     start_date = f'{year}-01-01'
-    #     end_date = f'{year}-12-31'
-
-    # # Get the dates for historic averages
-    # historic_avarages_dates_for_field = get_and_cache_available_dates(src_df, field_name, year, start_date, end_date)
-
-    # # Convert the dates to datetime objects and sort them ascendingly then convert them back to strings
-    # historic_avarages_dates_for_field = [datetime.strptime(date, '%Y-%m-%d') for date in historic_avarages_dates_for_field]
-    # historic_avarages_dates_for_field.sort()
-    # historic_avarages_dates_for_field = [datetime.strftime(date, '%Y-%m-%d') for date in historic_avarages_dates_for_field]
-
-    # # Get the number of dates
-    # num_historic_dates = len(historic_avarages_dates_for_field)
-    # st.write(f' Found {num_historic_dates} dates for field {field_name} in {year} (from {start_date} to {end_date})')
-
-    # # Display the historic averages when the user clicks the button
-    # display_historic_avgs_button = st.button(f'Display Historic Averages for Field {field_name} (Field ID: {field_name}) in {year} (from {start_date} to {end_date})',
-    #                                             key=f'Display Historic Averages Button - {metric}',
-    #                                             help='Click to display the historic averages for the selected field',
-    #                                             use_container_width=True, type='primary')
-    
-    # # If the button is clicked, display the historic averages
-    # if display_historic_avgs_button:
-
-    #     #Initlize the historic averages cache dir and file path
-    #     historic_avarages_cache_dir = './historic_avarages_cache'
-    #     historic_avarages_cache_path = f'{historic_avarages_cache_dir}/historic_avarages_cache.joblib'
-    #     historic_avarages_cache_clp_path = f'{historic_avarages_cache_dir}/historic_avarages_cache_clp.joblib'
-
-    #     # Load the historic averages cache if it exists, else create it
-    #     if os.path.exists(historic_avarages_cache_path):
-    #         historic_avarages_cache = joblib.load(historic_avarages_cache_path)
-    #     else:
-    #         os.makedirs(historic_avarages_cache_dir, exist_ok=True)
-    #         joblib.dump({}, historic_avarages_cache_path)
-    #         historic_avarages_cache = joblib.load(historic_avarages_cache_path)
-    #     if os.path.exists(historic_avarages_cache_clp_path):
-    #         historic_avarages_cache_clp = joblib.load(historic_avarages_cache_clp_path)
-    #     else:
-    #         os.makedirs(historic_avarages_cache_dir, exist_ok=True)
-    #         joblib.dump({}, historic_avarages_cache_clp_path)
-    #         historic_avarages_cache_clp = joblib.load(historic_avarages_cache_clp_path)
-
-    #     found_in_cache = False
-    #     if client_name not in historic_avarages_cache:
-    #         historic_avarages_cache[client_name] = {}
-    #     if metric not in historic_avarages_cache[client_name]:
-    #         historic_avarages_cache[client_name][metric] = {}
-    #     if field_name not in historic_avarages_cache[client_name][metric]:
-    #         historic_avarages_cache[client_name][metric][field_name] = {}
-    #     if year not in historic_avarages_cache[client_name][metric][field_name]:
-    #         historic_avarages_cache[client_name][metric][field_name][year] = {}
-    #     if len(historic_avarages_cache[client_name][metric][field_name][year]) > 0:
-    #         found_in_cache = True
-
-
-    #     #Check if the field and year are in the cache_clp for the current metric and client
-    #     found_in_cache_clp = False
-    #     if client_name not in historic_avarages_cache_clp:
-    #         historic_avarages_cache_clp[client_name] = {}
-    #     if 'CLP' not in historic_avarages_cache_clp[client_name]:
-    #         historic_avarages_cache_clp[client_name]['CLP'] = {}
-    #     if field_name not in historic_avarages_cache_clp[client_name]['CLP']:
-    #         historic_avarages_cache_clp[client_name]['CLP'][field_name] = {}
-    #     if year not in historic_avarages_cache_clp[client_name]['CLP'][field_name]:
-    #         historic_avarages_cache_clp[client_name]['CLP'][field_name][year] = {}
-    #     if len(historic_avarages_cache_clp[client_name]['CLP'][field_name][year]) > 0:
-    #         found_in_cache_clp = True
-
-
-    #     # If Found in cache, get the historic averages from the cache
-    #     if found_in_cache and found_in_cache_clp:
-    #         st.info('Found Historic Averages in Cache')
-    #         historic_avarages = historic_avarages_cache[client_name][metric][field_name][year]['historic_avarages']
-    #         historic_avarages_dates = historic_avarages_cache[client_name][metric][field_name][year]['historic_avarages_dates']
-    #         historic_avarages_clp = historic_avarages_cache_clp[client_name]['CLP'][field_name][year]['historic_avarages_clp']
-
-    #     # Else, calculate the historic averages and add them to the cache
-    #     else:
-    #         st.info('Calculating Historic Averages...')
-
-
-    #         #Empty lists for the historic averages , dates and cloud cover
-    #         historic_avarages = []
-    #         historic_avarages_dates = []
-    #         historic_avarages_clp = []
-
-    #         # Get the historic averages
-    #         dates_for_field_bar = st.progress(0)
-    #         with st.spinner('Calculating Historic Averages...'):
-    #             with st.empty():
-    #                 for i in range(num_historic_dates):
-    #                     # Get the historic average for the current date
-    #                     current_date = historic_avarages_dates_for_field[i]
-    #                     current_df = get_cuarted_df_for_field(src_df, field_name, current_date, metric, client_name)
-    #                     current_df_clp = get_cuarted_df_for_field(src_df, field_name, current_date, 'CLP', client_name)
-    #                     current_avg = current_df[f'{metric}_{current_date}'].mean()
-    #                     current_avg_clp = current_df_clp[f'CLP_{current_date}'].mean()
-    #                     # Add the historic average and date to the lists
-    #                     historic_avarages.append(current_avg)
-    #                     historic_avarages_dates.append(current_date)
-    #                     historic_avarages_clp.append(current_avg_clp)
-    #                     # Update the progress bar
-    #                     dates_for_field_bar.progress((i + 1)/(num_historic_dates))
-
-    #                     # Create a plot of the historic averages with the cloud cover as dashed line and dates as x axis (rotated 90 degrees when needed)
-    #                     fig, ax = plt.subplots(figsize=(5, 3))
-
-    #                     # Set the x axis ticks and labels
-    #                     x = historic_avarages_dates
-    #                     x_ticks = [i for i in range(len(x))]
-    #                     ax.set_xticks(x_ticks)
-                        
-    #                     #Set rotation to 90 degrees if the number of dates is greater than 10
-    #                     rot = 0 if len(x) < 10 else 90
-    #                     ax.set_xticklabels(x, rotation=rot)
-
-    #                     # Set the y axis ticks and labels
-    #                     y1 = historic_avarages
-    #                     y2 = historic_avarages_clp
-    #                     y_ticks = [i/10 for i in range(11)]
-    #                     ax.set_yticks(y_ticks)
-    #                     ax.set_yticklabels(y_ticks)
-
-    #                     # Plot the historic averages and cloud cover
-    #                     ax.plot(x_ticks, y1, label=f'{metric} Historic Averages')
-    #                     ax.plot(x_ticks, y2, '--', label='Cloud Cover')
-    #                     ax.legend()
-
-    #                     # Set the title and axis labels
-    #                     ax.set_title(f'{metric} Historic Averages for {field_name} (Field ID: {field_name}) in {year}')
-    #                     ax.set_xlabel('Date')
-    #                     ax.set_ylabel(f'{metric} Historic Averages')
-
-    #                     # Display the plot
-    #                     st.pyplot(fig, use_container_width=True)
-
-    #         # Add the historic averages to the cache
-    #         historic_avarages_cache[client_name][metric][field_name][year]['historic_avarages'] = historic_avarages
-    #         historic_avarages_cache[client_name][metric][field_name][year]['historic_avarages_dates'] = historic_avarages_dates
-    #         historic_avarages_cache_clp[client_name]['CLP'][field_name][year]['historic_avarages_clp'] = historic_avarages_clp
-    #         # Save the cache
-    #         joblib.dump(historic_avarages_cache, historic_avarages_cache_path)
-    #         joblib.dump(historic_avarages_cache_clp, historic_avarages_cache_clp_path)
-    #         # Tell the user that the historic averages are saved in the cache
-    #         st.info('Historic Averages Saved in Cache')
-    #         st.write(f'Cache Path: {historic_avarages_cache_path}')
-    #         st.write(f'Cache CLP Path: {historic_avarages_cache_clp_path}')
-
-
-    #     # Display the historic averages in nice plotly plot
-    #     fig = make_subplots(specs=[[{"secondary_y": True}]])
-
-    #     # Add the historic averages to the plot
-    #     fig.add_trace(
-    #         go.Scatter(x=historic_avarages_dates, y=historic_avarages, name=f'{metric} Historic Averages'),
-    #         secondary_y=False,
-    #     )
-
-    #     # Add the cloud cover to the plot
-    #     fig.add_trace(
-    #         go.Scatter(x=historic_avarages_dates, y=historic_avarages_clp, name='Cloud Cover'),
-    #         secondary_y=True,
-    #     )
-
-    #     # Set the title and axis labels
-    #     fig.update_layout(title_text=f'{metric} Historic Averages for {field_name} (Field ID: {field_name}) in {year}')
-    #     fig.update_xaxes(title_text='Date')
-    #     fig.update_yaxes(title_text=f'{metric} Historic Averages', secondary_y=False)
-    #     fig.update_yaxes(title_text='Cloud Cover', secondary_y=True)
-
-    #     # Display the plot
-    #     st.plotly_chart(fig)            
-        
-
-    # st.markdown('---')
-    # st.header('Show Historic GIF')
-
-
-    # #Let the user select the year, start date and end date of the GIF
-    # with st.expander('Select Year, Start Date and End Date of the GIF'):
-    #     # Get the year
-    #     years = [f'20{i}' for i in range(16, 23)]
-    #     year = st.selectbox('Select Year: ', years, index=len(years)-2, key=f'Select Year Dropdown Menu - {metric}- Historic Averages GIF')
-        
-    #     # Set the start and end dates to the first and last dates of the year
-    #     start_date = f'{year}-01-01'
-    #     end_date = f'{year}-12-31'
-
-    # # Get the dates for historic GIF
-    # historic_avarages_dates_for_field = get_and_cache_available_dates(src_df, field_name, year, start_date, end_date)
-
-    # # Convert the dates to datetime objects and sort them ascendingly then convert them back to strings
-    # historic_avarages_dates_for_field = [datetime.strptime(date, '%Y-%m-%d') for date in historic_avarages_dates_for_field]
-    # historic_avarages_dates_for_field.sort()
-    # historic_avarages_dates_for_field = [datetime.strftime(date, '%Y-%m-%d') for date in historic_avarages_dates_for_field]
-
-    # # Get the number of dates
-    # num_historic_dates = len(historic_avarages_dates_for_field)
-    # st.write(f' Found {num_historic_dates} dates for field {field_name} in {year} (from {start_date} to {end_date})')
-
-    # # Display the historic GIF when the user clicks the button
-    # display_historic_GIF_button = st.button(f'Display Historic GIF  for Field {field_name} (Field ID: {field_name}) in {year} (from {start_date} to {end_date})',
-    #                                             key=f'Display Historic GIF Button - {metric}',
-    #                                             help='Click to display the historic GIF for the selected field',
-    #                                             use_container_width=True, type='primary')
-    
-    # # If the button is clicked, display the historic GIF
-    # if display_historic_GIF_button:
-
-    #     #Initlize the historic GIF imgs and dates
-    #     st.info('Generating Historic GIF...')
-    #     historic_imgs = []
-    #     historic_imgs_dates = []
-
-    #     # Gen the historic GIF
-    #     dates_for_field_bar = st.progress(0)
-    #     with st.spinner('Generating Historic GIF...'):
-    #         with st.empty():
-    #             for i in range(num_historic_dates):
-    #                 current_date = historic_avarages_dates_for_field[i]
-    #                 current_df = get_cuarted_df_for_field(src_df, field_name, current_date, metric, client_name)
-    #                 historic_imgs.append(current_df)
-    #                 historic_imgs_dates.append(current_date)
-    #                 dates_for_field_bar.progress((i + 1)/(num_historic_dates))
-
-    #                 # Create a fig of the historic Img 
-    #                 fig, ax = plt.subplots(figsize=(10, 5))
-
-    #                 # Get the current img
-    #                 current_df_lat_lon = utils.add_lat_lon_to_gdf_from_geometry(current_df)
-    #                 current_img = utils.gdf_column_to_one_band_array(current_df_lat_lon, f'{metric}_{current_date}')
-
-    #                 # Plot the historic Img
-    #                 title = f'{metric} for selected field {field_name} (Field ID: {field_name}) in {current_date}'
-    #                 ax.imshow(current_img)
-    #                 ax.set_title(title)
-
-    #                 # Display the plot
-    #                 st.pyplot(fig)
-
-    #     # Create the historic GIF
-    #     historic_GIF_name = f'{metric}_{field_name}_{year}.gif'
-    #     st.write('Creating Historic GIF...', historic_GIF_name)
-       
+           
 
 def monitor_fields():
     current_user = greeting("Let's take a look how these fields are doing")
@@ -526,13 +237,7 @@ def monitor_fields():
     else:
         st.info("No Fields Added Yet!")
         return
-    # st.info("Hover over the field to show the properties or check the Existing Fields List below")
-    # fields_map = gdf.explore()
-    # sat_basemap = utils.basemaps['Google Satellite']
-    # sat_basemap.add_to(fields_map)
-    # folium.LayerControl().add_to(fields_map)
-    # # output = st_folium(fields_map, key="edit_map", height=300, width=600)
-    # folium_static(fields_map, height=300, width=600)
+
     
     with st.expander("Existing Fields List", expanded=False):
         st.write(gdf)

test.py

@@ -1,43 +0,0 @@
-import streamlit as st
-
-# Set page configuration
-st.set_page_config(
-    page_title="Your App Title",
-    page_icon=":shark:",
-    layout="wide",  # Use "wide" for expanded layout
-    initial_sidebar_state="expanded",
-)
-
-# def local_css(file_name):
-#     with open(file_name) "r") as f:
-#         st.markdown(f'<style>{f.read()}</style>', unsafe_allow_html=True)
-
-# Write CSS to apply styles
-def custom_css():
-    st.markdown("""
-        <style>
-            html, body, [data-testid="stAppViewContainer"] {
-                background-color: #000000;  /* Black background */
-                color: #FFFFFF;  /* White text color */
-            }
-            .stTextInput > label, .stSelectbox > label, .stRadio > label, .stCheckbox > label {
-                color: #CCCCCC;  /* Lighter text for better contrast */
-            }
-            /* Additional styling can be added here */
-        </style>
-        """, unsafe_allow_html=True)
-
-# Load CSS file (if you have a CSS file you prefer to use)
-# local_css("styles.css")
-
-# Apply custom CSS
-custom_css()
-
-# Your app code
-st.title("Your Streamlit App")
-st.write("This is a sample app with a black background.")
-
-# Example of other components
-st.text_input("Enter some text")
-st.selectbox("Choose an option", ["Option 1", "Option 2", "Option 3"])
-st.checkbox("Check me out")