app.py

import streamlit as st
import plotly.express as px
import pandas as pd
import streamlit_authenticator as stauth
import yaml
from yaml.loader import SafeLoader
import datetime

# from langchain.chat_models import ChatAnthropic
# from langchain.callbacks.base import BaseCallbackHandler
# from langchain.prompts import PromptTemplate
# from langchain.chains import LLMChain


# Function to fetch simulated fly situation data
def get_fly_situation(canteen):
    if canteen == "Deck":
        # Sample fly situation data
        fly_situation = {
            "temperature": 28,
            "humidity": 60,
            "fly_count": 9,
            "last_updated": "2023-11-10 12:00:00"
        }
        delta1 = '0.2'
        delta2 = '2'
        delta3 = '1'
    elif canteen == "Frontier":
        # Sample fly situation data
        fly_situation = {
            "temperature": 28.1,
            "humidity": 62,
            "fly_count": 21,
            "last_updated": "2023-11-10 12:00:00"
        }
        delta1 = '0.1' 
        delta2 = '1' 
        delta3 = '3' 
    return fly_situation, delta1, delta2, delta3

# Function to generate a sample fly situation dataset with time series
def get_fly_situation_history(canteen):
    if canteen == "Deck":
        # Sample fly situation time series data
        fly_situation_history = [
            {"timestamp": "2023-11-10 11:00:00", "fly_count": 2, "sensor":1},
            {"timestamp": "2023-11-10 11:05:00", "fly_count": 1, "sensor": 1},
            {"timestamp": "2023-11-10 11:10:00", "fly_count": 2, "sensor": 1},
            {"timestamp": "2023-11-10 11:15:00", "fly_count": 2, "sensor": 1},
            {"timestamp": "2023-11-10 11:20:00", "fly_count": 3, "sensor": 1},
            {"timestamp": "2023-11-10 11:25:00", "fly_count": 1, "sensor": 1},
            {"timestamp": "2023-11-10 11:30:00", "fly_count": 2, "sensor": 1},
            {"timestamp": "2023-11-10 11:35:00", "fly_count": 1, "sensor": 1},
            {"timestamp": "2023-11-10 11:40:00", "fly_count": 3, "sensor": 1},
            {"timestamp": "2023-11-10 11:45:00", "fly_count": 1, "sensor": 1},
            {"timestamp": "2023-11-10 11:50:00", "fly_count": 2, "sensor": 1},
            {"timestamp": "2023-11-10 11:55:00", "fly_count": 3, "sensor": 1},
            {"timestamp": "2023-11-10 12:00:00", "fly_count": 1, "sensor": 1},
            {"timestamp": "2023-11-10 11:00:00", "fly_count": 1, "sensor": 2},
            {"timestamp": "2023-11-10 11:05:00", "fly_count": 2, "sensor": 2},
            {"timestamp": "2023-11-10 11:10:00", "fly_count": 3, "sensor": 2},
            {"timestamp": "2023-11-10 11:15:00", "fly_count": 1, "sensor": 2},
            {"timestamp": "2023-11-10 11:20:00", "fly_count": 2, "sensor": 2},
            {"timestamp": "2023-11-10 11:25:00", "fly_count": 2, "sensor": 2},
            {"timestamp": "2023-11-10 11:30:00", "fly_count": 1, "sensor": 2},
            {"timestamp": "2023-11-10 11:35:00", "fly_count": 3, "sensor": 2},
            {"timestamp": "2023-11-10 11:40:00", "fly_count": 2, "sensor": 2},
            {"timestamp": "2023-11-10 11:45:00", "fly_count": 1, "sensor": 2},
            {"timestamp": "2023-11-10 11:50:00", "fly_count": 3, "sensor": 2},
            {"timestamp": "2023-11-10 11:55:00", "fly_count": 2, "sensor": 2},
            {"timestamp": "2023-11-10 12:00:00", "fly_count": 2, "sensor": 2},
            {"timestamp": "2023-11-10 11:00:00", "fly_count": 3, "sensor": 3},
            {"timestamp": "2023-11-10 11:05:00", "fly_count": 1, "sensor": 3},
            {"timestamp": "2023-11-10 11:10:00", "fly_count": 2, "sensor": 3},
            {"timestamp": "2023-11-10 11:15:00", "fly_count": 2, "sensor": 3},
            {"timestamp": "2023-11-10 11:20:00", "fly_count": 1, "sensor": 3},
            {"timestamp": "2023-11-10 11:25:00", "fly_count": 3, "sensor": 3},
            {"timestamp": "2023-11-10 11:30:00", "fly_count": 2, "sensor": 3},
            {"timestamp": "2023-11-10 11:35:00", "fly_count": 1, "sensor": 3},
            {"timestamp": "2023-11-10 11:40:00", "fly_count": 1, "sensor": 3},
            {"timestamp": "2023-11-10 11:45:00", "fly_count": 2, "sensor": 3},
            {"timestamp": "2023-11-10 11:50:00", "fly_count": 2, "sensor": 3},
            {"timestamp": "2023-11-10 11:55:00", "fly_count": 3, "sensor": 3},
            {"timestamp": "2023-11-10 12:00:00", "fly_count": 6, "sensor": 3},
        ]
    elif canteen == "Frontier":
        # Sample fly situation time series data
        fly_situation_history = [
            {"timestamp": "2023-11-10 11:00:00", "fly_count": 2, "sensor":1},
            {"timestamp": "2023-11-10 11:05:00", "fly_count": 5, "sensor": 1},
            {"timestamp": "2023-11-10 11:10:00", "fly_count": 6, "sensor": 1},
            {"timestamp": "2023-11-10 11:15:00", "fly_count": 4, "sensor": 1},
            {"timestamp": "2023-11-10 11:20:00", "fly_count": 5, "sensor": 1},
            {"timestamp": "2023-11-10 11:25:00", "fly_count": 2, "sensor": 1},
            {"timestamp": "2023-11-10 11:30:00", "fly_count": 5, "sensor": 1},
            {"timestamp": "2023-11-10 11:35:00", "fly_count": 6, "sensor": 1},
            {"timestamp": "2023-11-10 11:40:00", "fly_count": 7, "sensor": 1},
            {"timestamp": "2023-11-10 11:45:00", "fly_count": 8, "sensor": 1},
            {"timestamp": "2023-11-10 11:50:00", "fly_count": 10, "sensor": 1},
            {"timestamp": "2023-11-10 11:55:00", "fly_count": 9, "sensor": 1},
            {"timestamp": "2023-11-10 12:00:00", "fly_count": 8, "sensor": 1},
            {"timestamp": "2023-11-10 11:00:00", "fly_count": 1, "sensor": 2},
            {"timestamp": "2023-11-10 11:05:00", "fly_count": 2, "sensor": 2},
            {"timestamp": "2023-11-10 11:10:00", "fly_count": 3, "sensor": 2},
            {"timestamp": "2023-11-10 11:15:00", "fly_count": 2, "sensor": 2},
            {"timestamp": "2023-11-10 11:20:00", "fly_count": 3, "sensor": 2},
            {"timestamp": "2023-11-10 11:25:00", "fly_count": 4, "sensor": 2},
            {"timestamp": "2023-11-10 11:30:00", "fly_count": 6, "sensor": 2},
            {"timestamp": "2023-11-10 11:35:00", "fly_count": 7, "sensor": 2},
            {"timestamp": "2023-11-10 11:40:00", "fly_count": 8, "sensor": 2},
            {"timestamp": "2023-11-10 11:45:00", "fly_count": 10, "sensor": 2},
            {"timestamp": "2023-11-10 11:50:00", "fly_count": 9, "sensor": 2},
            {"timestamp": "2023-11-10 11:55:00", "fly_count": 8, "sensor": 2},
            {"timestamp": "2023-11-10 12:00:00", "fly_count": 6, "sensor": 2},
            {"timestamp": "2023-11-10 11:00:00", "fly_count": 3, "sensor": 3},
            {"timestamp": "2023-11-10 11:05:00", "fly_count": 2, "sensor": 3},
            {"timestamp": "2023-11-10 11:10:00", "fly_count": 2, "sensor": 3},
            {"timestamp": "2023-11-10 11:15:00", "fly_count": 2, "sensor": 3},
            {"timestamp": "2023-11-10 11:20:00", "fly_count": 1, "sensor": 3},
            {"timestamp": "2023-11-10 11:25:00", "fly_count": 3, "sensor": 3},
            {"timestamp": "2023-11-10 11:30:00", "fly_count": 5, "sensor": 3},
            {"timestamp": "2023-11-10 11:35:00", "fly_count": 7, "sensor": 3},
            {"timestamp": "2023-11-10 11:40:00", "fly_count": 6, "sensor": 3},
            {"timestamp": "2023-11-10 11:45:00", "fly_count": 3, "sensor": 3},
            {"timestamp": "2023-11-10 11:50:00", "fly_count": 2, "sensor": 3},
            {"timestamp": "2023-11-10 11:55:00", "fly_count": 1, "sensor": 3},
            {"timestamp": "2023-11-10 12:00:00", "fly_count": 7, "sensor": 3},
        ]

    return fly_situation_history

# Function to get dataframe of camera locations
def get_camera_locations(canteen):
    if canteen == 'Frontier':
        camera_locations = pd.DataFrame({
            "latitude": [1.2963134225592299, 1.2965099487866827, 1.296561127489237],
            "longitude": [103.78033553238319, 103.78067954132742, 103.7807614482189],
            "size": [1 for i in range(3)]
            })
    elif canteen == 'Deck':
        camera_locations = pd.DataFrame({
            "latitude": [1.2948580016451805, 1.2947091254796532, 1.2944617283028779],
            "longitude": [103.77238596429575, 103.77266955821814, 103.77246151634456],
            "size": [1 for i in range(3)]
            })
        
    return camera_locations

def get_pheremone_levels(sensor):
    pheremone_levels_history = [
        {"timestamp": "2023-11-10 11:00:00", "pheremone_level": 75, "sensor":1},
        {"timestamp": "2023-11-10 11:05:00", "pheremone_level": 75, "sensor": 1},
        {"timestamp": "2023-11-10 11:10:00", "pheremone_level": 74, "sensor": 1},
        {"timestamp": "2023-11-10 11:15:00", "pheremone_level": 74, "sensor": 1},
        {"timestamp": "2023-11-10 11:20:00", "pheremone_level": 74, "sensor": 1},
        {"timestamp": "2023-11-10 11:25:00", "pheremone_level": 74, "sensor": 1},
        {"timestamp": "2023-11-10 11:30:00", "pheremone_level": 73, "sensor": 1},
        {"timestamp": "2023-11-10 11:35:00", "pheremone_level": 72, "sensor": 1},
        {"timestamp": "2023-11-10 11:40:00", "pheremone_level": 71, "sensor": 1},
        {"timestamp": "2023-11-10 11:45:00", "pheremone_level": 65, "sensor": 1},
        {"timestamp": "2023-11-10 11:50:00", "pheremone_level": 63, "sensor": 1},
        {"timestamp": "2023-11-10 11:55:00", "pheremone_level": 62, "sensor": 1},
        {"timestamp": "2023-11-10 12:00:00", "pheremone_level": 58, "sensor": 1},
        {"timestamp": "2023-11-10 11:00:00", "pheremone_level": 95, "sensor": 2},
        {"timestamp": "2023-11-10 11:05:00", "pheremone_level": 91, "sensor": 2},
        {"timestamp": "2023-11-10 11:10:00", "pheremone_level": 91, "sensor": 2},
        {"timestamp": "2023-11-10 11:15:00", "pheremone_level": 90, "sensor": 2},
        {"timestamp": "2023-11-10 11:20:00", "pheremone_level": 90, "sensor": 2},
        {"timestamp": "2023-11-10 11:25:00", "pheremone_level": 90, "sensor": 2},
        {"timestamp": "2023-11-10 11:30:00", "pheremone_level": 90, "sensor": 2},
        {"timestamp": "2023-11-10 11:35:00", "pheremone_level": 90, "sensor": 2},
        {"timestamp": "2023-11-10 11:40:00", "pheremone_level": 87, "sensor": 2},
        {"timestamp": "2023-11-10 11:45:00", "pheremone_level": 84, "sensor": 2},
        {"timestamp": "2023-11-10 11:50:00", "pheremone_level": 80, "sensor": 2},
        {"timestamp": "2023-11-10 11:55:00", "pheremone_level": 73, "sensor": 2},
        {"timestamp": "2023-11-10 12:00:00", "pheremone_level": 72, "sensor": 2},
        {"timestamp": "2023-11-10 11:00:00", "pheremone_level": 41, "sensor": 3},
        {"timestamp": "2023-11-10 11:05:00", "pheremone_level": 41, "sensor": 3},
        {"timestamp": "2023-11-10 11:10:00", "pheremone_level": 40, "sensor": 3},
        {"timestamp": "2023-11-10 11:15:00", "pheremone_level": 40, "sensor": 3},
        {"timestamp": "2023-11-10 11:20:00", "pheremone_level": 39, "sensor": 3},
        {"timestamp": "2023-11-10 11:25:00", "pheremone_level": 38, "sensor": 3},
        {"timestamp": "2023-11-10 11:30:00", "pheremone_level": 38, "sensor": 3},
        {"timestamp": "2023-11-10 11:35:00", "pheremone_level": 35, "sensor": 3},
        {"timestamp": "2023-11-10 11:40:00", "pheremone_level": 34, "sensor": 3},
        {"timestamp": "2023-11-10 11:45:00", "pheremone_level": 33, "sensor": 3},
        {"timestamp": "2023-11-10 11:50:00", "pheremone_level": 33, "sensor": 3},
        {"timestamp": "2023-11-10 11:55:00", "pheremone_level": 30, "sensor": 3},
        {"timestamp": "2023-11-10 12:00:00", "pheremone_level": 26, "sensor": 3},
    ]
    return pheremone_levels_history

# # Streaming LLM output class
# class StreamHandler(BaseCallbackHandler):
#     # Referenced from: https://discuss.streamlit.io/t/langchain-stream/43782
#     def __init__(self, container, initial_text='', display_method='markdown'):
#         self.container = container
#         self.text = initial_text
#         self.display_method = display_method

#     def on_llm_new_token(self, token: str, **kwargs) -> None:
#         self.text += token
#         display_function = getattr(self.container, self.display_method, None)
#         if display_function is not None:
#             display_function(self.text)
#         else:
#             raise ValueError(f'Invalid display_method: {self.display_method}')


# Start of Streamlit Apps
st.set_page_config(layout="centered")
hide_streamlit_style = '''
            <style>
            #MainMenu {visibility: show;}
            footer {visibility: hidden;}
            </style>
            '''
st.markdown(hide_streamlit_style, unsafe_allow_html=True) 

# Import configuration file for user authentication
with open('credentials.yaml') as file:
    config = yaml.load(file, Loader=SafeLoader)
    
# Create an authentication object.
authenticator = stauth.Authenticate(
    config['credentials'],
    config['cookie']['name'],
    config['cookie']['key'],
    config['cookie']['expiry_days']
    )
# List of advanced users
advanced_users = ['advanced']

# Landing page if user not logged in
if st.session_state['authentication_status'] is None:
    # Landing page copy and banner
    st.markdown('<h1 style="text-align: left;">Fly Situation Monitoring App 🪰</h1>', unsafe_allow_html=True)
    st.markdown('<h4 style="text-align: left;">Keeping You Informed, Keeping Flies at Bay</h4>', unsafe_allow_html=True)
    st.write('\n')
    st.write('\n')
    
    # Loging log-in details
    name, authentication_status, username = authenticator.login('', 'main')
    
    # If log-in failed
    if st.session_state['authentication_status'] is False:
        st.error('Username/password is incorrect.')
    st.write('\n')
    st.write('\n')

    
# App if user is logged in and authenticated
if st.session_state['authentication_status']:
    
    # Streamlit app start
    st.title("Fly Situation Monitoring App 🪰")
    st.markdown("Keeping You Informed, Keeping Flies at Bay")
    st.write('\n')
    
    # User selects a canteen
    canteen = st.selectbox("Select a Canteen:", options=["Deck", "Frontier"])
    st.write('\n')
    
    # If user is a student, show basic app layout
    if not st.session_state['username'] in advanced_users:
        
        # Tabs
        tab1, tab2 = st.tabs(["Current", "History"])
        
        # Tab 1: Fly Situation
        with tab1:
            st.header("Current Fly Situation")
            
            # Get data
            fly_situation, delta1, delta2, delta3 = get_fly_situation(canteen)
            
            # Display key information using cards
            col1_fly_curr, col2_fly_curr, col3_fly_curr = st.columns(3)
            col1_fly_curr.metric("Temperature", str(fly_situation["temperature"]) + " °C", delta=delta1)
            col2_fly_curr.metric("Humidty", str(fly_situation["humidity"]) + " %", delta=delta2)
            col3_fly_curr.metric("Fly Count", str(fly_situation["fly_count"]), delta=delta3, delta_color="inverse")
            st.caption("Last updated at " + fly_situation["last_updated"] + " (5 min intervals)")
            
            # Alert level
            if fly_situation["fly_count"] > 20:
                alert_level = "High 🔴"
                alert_colour = "red"
            elif fly_situation["fly_count"] > 10:
                alert_level = "Moderate 🟠"
                alert_colour = "orange"
            else: 
                alert_level = "Low 🟢"
                alert_colour = "green"
            st.markdown(f"<h2 style='color:{alert_colour}; text-align: left'>Alert Level: {alert_level}</h3>", unsafe_allow_html=True)
            st.markdown('---')
            
            # Camera locations
            st.header("Smart Sensor Locations")
            camera_locations = get_camera_locations(canteen)
            st.map(camera_locations, size='size', zoom=18)
            st.markdown('---')
            
            # Feedback
            st.header("Feedback")
            # Gather feedback
            feedback_col1, feedback_col2 = st.columns(2)
            with feedback_col1:
                user_feedback = st.text_area("Provide Feedback on the Fly Situation:")
            with feedback_col2:
                uploaded_files = st.file_uploader("Upload a Photo", accept_multiple_files=True, type=['jpg', 'png'])
                for uploaded_file in uploaded_files:
                    st.image(uploaded_file)
            if st.button("Submit Feedback"):
                st.success("Feedback submitted successfully!")
            st.write('\n')
            st.write('\n')
            st.write('\n')
                
        # Tab 2: History
        with tab2:
            st.subheader("Fly Count Over Time")
            
            # Get history data
            fly_situation_history = get_fly_situation_history(canteen)
            
            # Create a DataFrame for the time series data
            df = pd.DataFrame(fly_situation_history)
            sum_by_timestamp = df.groupby('timestamp')['fly_count'].sum().reset_index()
            sum_by_timestamp["timestamp"] = pd.to_datetime(sum_by_timestamp["timestamp"])
            
            # Plot the time series using Plotly Express
            fig = px.line(sum_by_timestamp, x="timestamp", y="fly_count", labels={"fly_count": "Fly Count", "timestamp": "Timestamp"})
            st.plotly_chart(fig)

            # Question-and-Answer
            with st.form("form"):
                prompt = st.text_input("Ask a Question:")
                submit = st.form_submit_button("Submit")
                if prompt:
                    with st.spinner("Generating..."):
                        pass       
                               
        # Logout
        logout_col1, logout_col2 = st.columns([6,1])
        with logout_col2:    
            st.write('\n')
            st.write('\n')
            st.write('\n')
            authenticator.logout('Logout', 'main')
            
        # Footer Credits
        st.markdown('##')
        st.markdown("---")
        st.markdown("Created with ❤️ by HS2912 W4 Group 2")
    
    else:
        # Tabs
        tab1, tab2, tab3 = st.tabs(["Current", "History", "Control System"])
        
        # Tab 1: Fly Situation
        with tab1:
            st.header("Current Fly Situation")
            # Get current data
            fly_situation, delta1, delta2, delta3 = get_fly_situation(canteen)
            # Display key information using cards
            col1_fly_curr, col2_fly_curr, col3_fly_curr = st.columns(3)
            col1_fly_curr.metric("Temperature", str(fly_situation["temperature"]) + " °C", delta=delta1)
            col2_fly_curr.metric("Humidty", str(fly_situation["humidity"]) + " %", delta=delta2)
            col3_fly_curr.metric("Fly Count", str(fly_situation["fly_count"]), delta=delta3, delta_color="inverse")
            st.caption("Last updated at " + fly_situation["last_updated"] + " (5 min intervals)")
            
            # Alert 
            if fly_situation["fly_count"] > 20:
                alert_level = "High 🔴"
                alert_colour = "red"
            elif fly_situation["fly_count"] > 10:
                alert_level = "Moderate 🟠"
                alert_colour = "orange"
            else: 
                alert_level = "Low 🟢"
                alert_colour = "green"
            st.markdown(f"<h2 style='color:{alert_colour}; text-align: left'>Alert Level: {alert_level}</h3>", unsafe_allow_html=True)
            st.markdown('---')
            
            # Camera locations
            st.header("Smart Sensor Locations")
            camera_locations = get_camera_locations(canteen)
            st.map(camera_locations, size='size', zoom=18)
            st.markdown('---')
            
            # Feedback
            st.header("Feedback")
            # Gather feedback
            feedback_col1, feedback_col2 = st.columns(2)
            with feedback_col1:
                user_feedback = st.text_area("Provide Feedback on the Fly Situation:")
            with feedback_col2:
                uploaded_files = st.file_uploader("Upload a Photo", accept_multiple_files=True, type=['jpg', 'png'])
                for uploaded_file in uploaded_files:
                    st.image(uploaded_file)
            if st.button("Submit Feedback"):
                st.success("Feedback submitted successfully!")
            st.write('\n')
            st.write('\n')
            st.write('\n')
                

        # Tab 2: History
        with tab2:
            # Fly count over time 
            st.subheader("Fly Count Over Time")
            # Select sensor
            selected_sensor = st.selectbox("Select Sensor:", ["All", "Sensor 1", "Sensor 2", "Sensor 3"])
            # Get history data
            fly_situation_history = get_fly_situation_history(canteen)
            # Create a DataFrame for the time series data
            df = pd.DataFrame(fly_situation_history)
            if selected_sensor != "All":
                df = df[df["sensor"]==int(selected_sensor[-1])]
            sum_by_timestamp = df.groupby('timestamp')['fly_count'].sum().reset_index()
            sum_by_timestamp["timestamp"] = pd.to_datetime(sum_by_timestamp["timestamp"])
            # Plot the time series using Plotly Express
            fig = px.line(sum_by_timestamp, x="timestamp", y="fly_count", labels={"fly_count": "Fly Count", "timestamp": "Timestamp"})
            st.plotly_chart(fig)
            
            # Pheremones level
            st.subheader("Pheremone Level Over Time")
            selected_sensor_level = st.selectbox("Select Sensor:", ["Sensor 1", "Sensor 2", "Sensor 3"])
            # Get history data
            sensor_pheremone_history = get_pheremone_levels(selected_sensor_level)
            pheremone_df = pd.DataFrame(sensor_pheremone_history)
            pheremone_df = pheremone_df[pheremone_df["sensor"] == int(selected_sensor_level[-1])]
            pheremone_df['timestamp'] = pd.to_datetime(pheremone_df['timestamp'])
            # Plot the time series using Plotly Express
            fig = px.line(pheremone_df, x="timestamp", y="pheremone_level", labels={"pheremone_level": "Pheremone Level", "timestamp": "Timestamp"})
            st.plotly_chart(fig)
            

            # Question-and-Answer
            with st.form("form"):
                prompt = st.text_input("Ask a Question:")
                submit = st.form_submit_button("Submit")
                if prompt:
                    with st.spinner("Generating..."):
                        pass
        
        # Tab 3: Control System
        with tab3:
            # Enable/disable automatic pest control system
            st.header("System Settings")
            automatic_control_enabled = st.toggle("Enable Automatic Pest Control", value=True)
            st.write('\n')
            st.write('\n')
            if not automatic_control_enabled:
                disabled = False
            else:
                disabled = True
            
            # Camera
            st.subheader("Smart Camera/Sensors")
            sensor1 = st.toggle("Enable Sensor 1", value=True, disabled=disabled, key='deck_sensor_1')
            sensor2 = st.toggle("Enable Sensor 2", value=True, disabled=disabled, key='deck_sensor_2')
            sensor3 = st.toggle("Enable Sensor 3", value=True, disabled=disabled, key='deck_sensor_3')
            st.write('\n')
            
            # Audio
            st.subheader("Audio")
            # Accoustic 
            accoustic = st.selectbox("Accoustic Audio", ["Audio 1", "Audio 2", "Audio 3"], disabled=disabled)
            st.write('\n')
            
            # Pheremones
            # Time interval for pheremones discharge in minutes)
            st.subheader("Pheremones")
            pheremones_interval = st.slider("Pheremones Discharge Interval (minutes)", min_value=5, max_value=60, value=15, step=5, disabled=disabled)
            st.write('\n')

            # Alerts
            st.subheader('Alerts')
            # Pest activity threshold for alerts
            pest_activity_threshold = st.slider("Fly Count Threshold to Send Out Alerts", min_value=0, max_value=100, value=30, step=5, disabled=disabled)
            st.write('\n')
            # Instant alerts for pest sightings or unusual activity
            st.markdown('<h5>Instant alert</h3>', unsafe_allow_html=True)
            if st.button("Send Pest Alert", disabled=disabled):
                st.success("Pest alert sent!")
            st.write('\n')
            # Notifications for upcoming preventive measures or scheduled treatments
            st.markdown('<h5>Schedule notification for upcoming treatment day</h3>', unsafe_allow_html=True)
            upcoming_event_date = st.date_input("Schedule Date", disabled=disabled)
            upcoming_event_time = st.time_input("Set time for alert", disabled=disabled)
            if st.button("Schedule Notification", disabled=disabled):
                st.success(f"Notification scheduled for {upcoming_event_date} {upcoming_event_time}")

        # Logout
        logout_col1, logout_col2 = st.columns([6,1])
        with logout_col2:    
            st.write('\n')
            st.write('\n')
            st.write('\n')
            authenticator.logout('Logout', 'main')
            
        # Footer Credits
        st.markdown('##')
        st.markdown("---")
        st.markdown("Created with ❤️ by HS2912 W4 Group 2")