app.py

# import streamlit as st
# import pandas as pd
# import plotly.express as px
# from app_backend import fetch_weather, generate_synthetic_data, optimize_load

# # Constants
# API_KEY = "84e26811a314599e940f343b4d5894a7"
# LOCATION = "Pakistan"

# # Sidebar
# st.sidebar.title("Smart Grid Dashboard")
# location = st.sidebar.text_input("Enter Location", LOCATION)

# # Fetch and display weather data
# weather = fetch_weather(API_KEY, location)
# if weather:
#     st.sidebar.write(f"Temperature: {weather['temperature']} °C")
#     st.sidebar.write(f"Wind Speed: {weather['wind_speed']} m/s")
#     st.sidebar.write(f"Weather: {weather['weather']}")

# # Main dashboard with tabs
# tabs = st.tabs(["Home", "Electricity Storage", "Electricity Trading"])

# with tabs[0]:
#     st.title("Real-Time Smart Grid Dashboard")

#     # Generate synthetic data
#     data = generate_synthetic_data()

#     # Plot total consumption, grid generation, and storage usage
#     fig = px.line(data, x="timestamp", y=["total_consumption_kwh", "grid_generation_kwh", "storage_usage_kwh"],
#                   title="Energy Consumption, Generation, and Storage Usage Over Time",
#                   labels={"value": "Energy (kWh)", "variable": "Energy Source"})
#     st.plotly_chart(fig)

#     # Grid health overview
#     st.subheader("Grid Health Overview")
#     grid_health_counts = data["grid_health"].value_counts()
#     st.bar_chart(grid_health_counts)

#     # Optimization recommendations
#     current_demand = data["total_consumption_kwh"].iloc[-1]
#     current_solar = data["solar_output_kw"].iloc[-1]
#     current_wind = data["wind_output_kw"].iloc[-1]
#     recommendation = optimize_load(current_demand, current_solar, current_wind)

#     st.subheader("Recommendations")
#     st.write(f"Current Load Demand: {current_demand} kWh")
#     st.write(f"Solar Output: {current_solar} kW")
#     st.write(f"Wind Output: {current_wind} kW")
#     st.write(f"Recommendation: {recommendation}")

# with tabs[1]:
#     st.title("Energy Storage Overview")

#     # Total energy stored
#     total_storage = 500  # Example of total energy storage
#     st.subheader(f"Total Energy Stored: {total_storage} kWh")

#     # Energy storage contribution from different sources
#     st.subheader("Energy Storage Contributions")
#     energy_sources = pd.DataFrame({
#         "Source": ["Wind", "Solar", "Turbine"],
#         "Energy (kW/min)": [5, 7, 10]
#     })
#     st.bar_chart(energy_sources.set_index("Source"))

#     # Show energy storage status with a rounded circle
#     st.subheader("Energy Storage Circle")
#     st.markdown("Energy storage is a combination of contributions from different renewable sources.")

#     # Visualization of energy storage circle using Plotly
#     storage_data = {
#         "Source": ["Wind", "Solar", "Turbine"],
#         "Energy": [5, 7, 10],
#     }
#     storage_df = pd.DataFrame(storage_data)
#     fig = px.pie(storage_df, names="Source", values="Energy", title="Energy Storage Sources")
#     st.plotly_chart(fig)

# with tabs[2]:
#     st.title("Energy Trading Overview")

#     # Energy cubes
#     st.subheader("Energy Cubes Stored")
#     energy_cubes = pd.DataFrame({
#         "Country": ["China", "Sri Lanka", "Bangladesh"],
#         "Energy (kWh)": [100, 200, 300],
#         "Shareable": [True, True, False]
#     })

#     # Displaying the energy cubes in a grid
#     st.write("Stored energy can be shared with other countries.")
#     st.dataframe(energy_cubes)

#     # Visualization of energy that can be shared
#     st.subheader("Energy Trading Visualization")
#     st.markdown("The following energy amounts are available for sharing with different countries.")
#     trading_fig = px.bar(energy_cubes, x="Country", y="Energy (kWh)", color="Shareable", title="Energy Trading")
#     st.plotly_chart(trading_fig)


# code 2


# import streamlit as st
# import pandas as pd
# import plotly.graph_objects as go
# from app_backend import fetch_weather, generate_synthetic_data, generate_storage_data

# # Constants
# API_KEY = "84e26811a314599e940f343b4d5894a7"
# DEFAULT_LOCATION = "Pakistan"

# # Sidebar for location and weather data
# st.sidebar.title("Smart Grid Dashboard")
# location = st.sidebar.text_input("Enter Location", DEFAULT_LOCATION)
# weather = fetch_weather(API_KEY, location)

# if weather:
#     st.sidebar.write(f"Temperature: {weather['temperature']} °C")
#     st.sidebar.write(f"Wind Speed: {weather['wind_speed']} m/s")
#     st.sidebar.write(f"Weather: {weather['weather']}")

# # Main interface
# st.title("Real-Time Smart Grid Dashboard")

# # Tabs
# tabs = st.tabs(["Home", "Power Storage", "Electricity Trade Management"])

# # Home Tab
# with tabs[0]:
#     st.header("Overview: Power and Energy Usage")
    
#     # Fetch synthetic data
#     data = generate_synthetic_data()

#     # Line Graph for Power Consumption, Generation, and Storage
#     fig = go.Figure()
#     fig.add_trace(go.Scatter(
#         x=data["timestamp"],
#         y=data["total_power_consumption_mw"],
#         mode='lines',
#         name="Total Power Consumption (MW)",
#         line=dict(color="red")
#     ))
#     fig.add_trace(go.Scatter(
#         x=data["timestamp"],
#         y=data["grid_generation_mw"],
#         mode='lines',
#         name="Grid Generation (MW)",
#         line=dict(color="green")
#     ))
#     fig.add_trace(go.Scatter(
#         x=data["timestamp"],
#         y=data["storage_utilization_mw"],
#         mode='lines',
#         name="Storage Utilization (MW)",
#         line=dict(color="blue")
#     ))
#     fig.update_layout(title="Power and Energy Trends", xaxis_title="Time", yaxis_title="Power (MW)")
#     st.plotly_chart(fig)

# # Storage Tab
# with tabs[1]:
#     st.header("Energy Storage Overview")
#     storage_data = generate_storage_data()

#     st.write(f"**Total Energy Stored:** {storage_data['total_stored_kwh']} kWh")
    
#     # Circular storage breakdown
#     sources = ["Wind", "Solar", "Turbine"]
#     values = [storage_data["wind"], storage_data["solar"], storage_data["turbine"]]
    
#     fig = go.Figure(data=[go.Pie(labels=sources, values=values, hole=.4)])
#     fig.update_layout(title="Energy Storage Breakdown")
#     st.plotly_chart(fig)

# # Electricity Trade Management Tab
# with tabs[2]:
#     st.header("Electricity Trade Management")

#     # Sample trade data
#     trade_data = {
#         "Country": ["Srilanka", "China", "Bangladesh"],
#         "Energy Exported (MW)": [50, 30, 70],
#         "Energy Imported (MW)": [20, 40, 10],
#     }
#     trade_df = pd.DataFrame(trade_data)

#     st.subheader("Trade Details")
#     st.write(trade_df)

#     # Visualization
#     fig = go.Figure()
#     fig.add_trace(go.Bar(x=trade_df["Country"], y=trade_df["Energy Exported (MW)"], name="Exported", marker_color='purple'))
#     fig.add_trace(go.Bar(x=trade_df["Country"], y=trade_df["Energy Imported (MW)"], name="Imported", marker_color='orange'))
#     fig.update_layout(title="Energy Trade", barmode='group')
#     st.plotly_chart(fig)


# code 3


# import streamlit as st
# import pandas as pd
# import plotly.graph_objects as go
# from app_backend import fetch_weather, generate_synthetic_data, generate_storage_data

# # Constants
# API_KEY = "84e26811a314599e940f343b4d5894a7"
# DEFAULT_LOCATION = "pakistan"

# # Sidebar for location and weather data
# st.sidebar.title("Smart Grid Dashboard")
# location = st.sidebar.text_input("Enter Location", DEFAULT_LOCATION)
# weather = fetch_weather(API_KEY, location)

# if weather:
#     st.sidebar.write(f"Temperature: {weather['temperature']} °C")
#     st.sidebar.write(f"Wind Speed: {weather['wind_speed']} m/s")
#     st.sidebar.write(f"Weather: {weather['weather']}")

# # Main interface
# st.title("Real-Time Smart Grid Dashboard")

# # Tabs
# tabs = st.tabs(["Home", "Storage", "Electricity Trade Management"])

# # Home Tab
# with tabs[0]:
#     st.header("Overview: Power and Energy Usage")
    
#     # Fetch synthetic data
#     data = generate_synthetic_data()

#     # Line Graph for Power Consumption, Generation, and Storage
#     fig = go.Figure()
#     fig.add_trace(go.Scatter(
#         x=data["timestamp"],
#         y=data["total_power_consumption_mw"],
#         mode='lines',
#         name="Total Power Consumption (MW)",
#         line=dict(color="red")
#     ))
#     fig.add_trace(go.Scatter(
#         x=data["timestamp"],
#         y=data["grid_generation_mw"],
#         mode='lines',
#         name="Grid Generation (MW)",
#         line=dict(color="green")
#     ))
#     fig.add_trace(go.Scatter(
#         x=data["timestamp"],
#         y=data["storage_utilization_mw"],
#         mode='lines',
#         name="Storage Utilization (MW)",
#         line=dict(color="blue")
#     ))
#     fig.update_layout(title="Power and Energy Trends", xaxis_title="Time", yaxis_title="Power (MW)")
#     st.plotly_chart(fig)

#     # Grid Health Indicator
#     #     # Grid health overview
#     # st.subheader("Grid Health Overview")
#     # grid_health_counts = data["grid_health"].value_counts()
#     # st.bar_chart(grid_health_counts)


    
#     st.subheader("Grid Health Status")
#     grid_health = "Stable" if data["grid_generation_mw"].mean() >= data["total_power_consumption_mw"].mean() else "Critical"
#     st.write(f"**Grid Health:** {grid_health}")

#     # AI Recommendations
#     st.subheader("AI Recommendations")
#     recommendations = [
#         "Increase solar panel efficiency by 10% for peak hours.",
#         "Optimize wind turbine alignment based on real-time wind data.",
#         "Store excess energy during low-demand periods for future use.",
#         "Improve grid stability by distributing load dynamically across sectors.",
#     ]
#     for rec in recommendations:
#         st.write(f"- {rec}")

# # Storage Tab
# with tabs[1]:
#     st.header("Energy Storage Overview")
#     storage_data = generate_storage_data()

#     # Individual Circles for Wind, Solar, and Turbine
#     st.subheader("Energy Contributions")
#     col1, col2, col3 = st.columns(3)
#     with col1:
#         st.metric("Wind Energy", f"{storage_data['wind']} MW/min")
#     with col2:
#         st.metric("Solar Energy", f"{storage_data['solar']} MW/min")
#     with col3:
#         st.metric("Turbine Energy", f"{storage_data['turbine']} MW/min")

#     # Central Grid Storage Visualization
#     st.subheader("Total Energy Stored in Grid")
#     fig = go.Figure()
#     fig.add_trace(go.Scatter(x=[0], y=[0], mode='markers+text', text=["Grid"], marker=dict(size=70, color="blue")))
#     fig.add_trace(go.Scatter(
#         x=[-1, 1, 0],
#         y=[1, 1, -1],
#         mode='markers+text',
#         text=["Wind", "Solar", "Turbine"],
#         marker=dict(size=50, color=["green", "yellow", "orange"])
#     ))
#     fig.add_trace(go.Scatter(
#         x=[-0.5, 0.5, 0],
#         y=[0.5, 0.5, -0.5],
#         mode="lines",
#         line=dict(width=3, color="gray"),
#     ))
#     fig.update_layout(
#         title="Energy Storage Visualization",
#         xaxis=dict(visible=False),
#         yaxis=dict(visible=False),
#         showlegend=False
#     )
#     st.plotly_chart(fig)

#     st.write(f"**Total Energy Stored:** {storage_data['total_stored_kwh']} kWh")

# # Electricity Trade Management Tab
# with tabs[2]:
#     st.header("Electricity Trade Management")

#     # Sample trade data
#     trade_data = {
#         "Country": ["Country A", "Country B", "Country C"],
#         "Energy Exported (MW)": [50, 30, 70],
#         "Energy Imported (MW)": [20, 40, 10],
#     }
#     trade_df = pd.DataFrame(trade_data)

#     st.subheader("Trade Details")
#     st.write(trade_df)

#     # Visualization
#     fig = go.Figure()
#     fig.add_trace(go.Bar(x=trade_df["Country"], y=trade_df["Energy Exported (MW)"], name="Exported", marker_color='purple'))
#     fig.add_trace(go.Bar(x=trade_df["Country"], y=trade_df["Energy Imported (MW)"], name="Imported", marker_color='orange'))
#     fig.update_layout(title="Energy Trade", barmode='group')
#     st.plotly_chart(fig)


# code 4



# import streamlit as st
# import plotly.express as px
# from app_backend import fetch_weather, generate_synthetic_data, optimize_load
# import pandas as pd

# # Constants
# API_KEY = "84e26811a314599e940f343b4d5894a7"  # Replace with your OpenWeather API key
# LOCATION = "Pakistan"

# # Sidebar
# st.sidebar.title("Smart Grid Dashboard")
# location = st.sidebar.text_input("Enter Location", LOCATION)

# # Fetch and display weather data
# weather = fetch_weather(API_KEY, location)
# st.sidebar.subheader("Weather Information")
# if weather:
#     st.sidebar.write(f"**Temperature**: {weather['temperature']} °C")
#     st.sidebar.write(f"**Wind Speed**: {weather['wind_speed']} m/s")
#     st.sidebar.write(f"**Weather**: {weather['weather']}")

# # Main tabs
# tab1, tab2, tab3 = st.tabs(["Home", "Power Storage", "Power Trading management"])

# # Home Tab
# with tab1:
#     st.title("Real-Time Smart Grid Dashboard")
    
#     # Generate synthetic data
#     data = generate_synthetic_data()
    
#     # Show weather at top
#     if weather:
#         st.write(f"### Location: {location}")
#         st.write(f"Temperature: {weather['temperature']} °C | Wind Speed: {weather['wind_speed']} m/s | Weather: {weather['weather']}")
    
#     # Power consumption graph
#     fig = px.line(
#         data,
#         x="timestamp",
#         y=["power_consumption_mw", "generation_mw", "storage_usage_mw"],
#         labels={"value": "Power (MW)", "variable": "Metric"},
#         title="Power Flow Over Time"
#     )
#     fig.update_traces(mode="lines+markers")
#     st.plotly_chart(fig)
    
#     # Grid health as bar chart
#     st.subheader("Grid Health Overview")
#     grid_health_counts = data["grid_health"].value_counts()
#     fig_health = px.bar(
#         grid_health_counts,
#         x=grid_health_counts.index,
#         y=grid_health_counts.values,
#         labels={"x": "Grid Status", "y": "Count"},
#         title="Grid Health Status"
#     )
#     st.plotly_chart(fig_health)
    
#     # AI recommendations
#     st.subheader("AI Recommendations")
#     current_demand = data["power_consumption_mw"].iloc[-1]
#     current_solar = data["solar_output_mw"].iloc[-1]
#     current_wind = data["wind_output_mw"].iloc[-1]
#     recommendation = optimize_load(current_demand, current_solar, current_wind)
#     st.write(f"**Current Load Demand**: {current_demand} MW")
#     st.write(f"**Solar Output**: {current_solar} MW")
#     st.write(f"**Wind Output**: {current_wind} MW")
#     st.write(f"**Recommendation**: {recommendation}")

# # Storage Tab
# with tab2:
#     st.title("Energy Storage Status")
    
#     # Pie chart of energy percentage contribution
#     storage_data = {
#         "Wind": data["wind_output_mw"].mean(),
#         "Solar": data["solar_output_mw"].mean(),
#         "Turbine": data["turbine_output_mw"].mean()
#     }
#     fig_pie = px.pie(
#         names=storage_data.keys(),
#         values=storage_data.values(),
#         title="Energy Contribution by Resource"
#     )
#     st.plotly_chart(fig_pie)
    
#     # Circle visualization for storage
#     st.subheader("Total Energy Stored")
#     total_storage = sum(storage_data.values())
#     st.write(f"**Total Energy Stored**: {total_storage:.2f} MW")
    
#     st.markdown(
#         """
#         <div style="display: flex; justify-content: center; align-items: center; flex-direction: column;">
#             <div style="width: 150px; height: 150px; border-radius: 50%; background-color: #FFDD00; display: flex; justify-content: center; align-items: center; font-size: 24px; font-weight: bold; margin-bottom: 20px;">
#                 {total_storage:.2f} MW
#             </div>
#             <div style="display: flex; gap: 50px;">
#                 <div style="width: 100px; height: 100px; border-radius: 50%; background-color: #0073FF; display: flex; justify-content: center; align-items: center; font-size: 16px; font-weight: bold;">
#                     Wind<br>{storage_data["Wind"]:.2f} MW
#                 </div>
#                 <div style="width: 100px; height: 100px; border-radius: 50%; background-color: #FF5733; display: flex; justify-content: center; align-items: center; font-size: 16px; font-weight: bold;">
#                     Solar<br>{storage_data["Solar"]:.2f} MW
#                 </div>
#                 <div style="width: 100px; height: 100px; border-radius: 50%; background-color: #28B463; display: flex; justify-content: center; align-items: center; font-size: 16px; font-weight: bold;">
#                     Turbine<br>{storage_data["Turbine"]:.2f} MW
#                 </div>
#             </div>
#         </div>
#         """,
#         unsafe_allow_html=True
#     )

# # Trading Tab
# with tab3:
#     st.title("Electricity Trade Management")
#     st.write("Under development...")



# code 5


import streamlit as st
import pandas as pd
import plotly.express as px
from app_backend import fetch_weather, generate_synthetic_data, optimize_load

# Constants
API_KEY = "84e26811a314599e940f343b4d5894a7"
LOCATION = "Pakistan"

# Sidebar
st.sidebar.title("Smart Grid Dashboard")
location = st.sidebar.text_input("Enter Location", LOCATION)

# Fetch and display weather data
weather = fetch_weather(API_KEY, location)
if weather:
    st.sidebar.write(f"Temperature: {weather['temperature']} °C")
    st.sidebar.write(f"Wind Speed: {weather['wind_speed']} m/s")
    st.sidebar.write(f"Weather: {weather['weather']}")
    user input 
	Voltage (KV) ()
	Frequency (Hz) for stability monitoring.
	Power Flow (MW) 
	Renewable Generation category user will select (solar, wind, hydro outputs).
	Battery Storage Capacity check its unit (State of Charge).


# Tabs
tab_home, tab_storage, tab_trading = st.tabs(["Home", "Power Storage", "Electricity Trade Management"])

# Home Tab
with tab_home:
    st.title("Real-Time Smart Grid Dashboard")

    # Generate synthetic data
    data = generate_synthetic_data()

    # Grid Health
    # st.subheader("Grid Health Overview")
    # grid_health_counts = data["grid_health"].value_counts()
    # st.bar_chart(grid_health_counts)

    # Power Consumption, Generation & Storage Graph
    st.subheader("Power Consumption, Generation & Storage")
    fig = px.line(data, x="timestamp", y=["load_demand_kwh", "solar_output_kw", "wind_output_kw"], 
                  title="Power Consumption, Generation & Storage", labels={"value": "Power (MW)"})
    fig.update_traces(line=dict(width=2))
    st.plotly_chart(fig)

    # Grid Health
    st.subheader("Grid Health Overview")
    grid_health_counts = data["grid_health"].value_counts()
    st.bar_chart(grid_health_counts)

    
    # Optimization Recommendations
    current_demand = data["load_demand_kwh"].iloc[-1]
    current_solar = data["solar_output_kw"].iloc[-1]
    current_wind = data["wind_output_kw"].iloc[-1]
    recommendation = optimize_load(current_demand, current_solar, current_wind)

    st.subheader("Recommendations")
    st.write(f"Current Load Demand: {current_demand} MW")
    st.write(f"Solar Output: {current_solar} MW")
    st.write(f"Wind Output: {current_wind} MW")
    st.write(f"Recommendation: {recommendation}")

# Storage Tab
with tab_storage:
    st.title("Energy Storage Overview")

    # Energy Contribution by Resources
    st.subheader("Energy Contribution Percentage by Resources")
    energy_data = {
        "Wind": 5,
        "Solar": 7,
        "Turbine": 10
    }
    energy_df = pd.DataFrame(list(energy_data.items()), columns=["Source", "Energy (MW)"])
    fig = px.pie(energy_df, values="Energy (MW)", names="Source", title="Energy Contribution by Resources")
    st.plotly_chart(fig)

    # Energy Storage Merge
    st.subheader("Total Energy Stored")
    st.write("Energy stored from all sources:")
    energy_stored = sum(energy_data.values())
    st.write(f"Total Energy Stored: {energy_stored} MW")
    st.write("Energy sources merged into total energy storage:")
    st.write(f"Total Energy Stored in Grid: {energy_stored} MW")

# Trading Tab
with tab_trading:
    st.title("Electricity Trade Management")

    # Simulating Electricity Trade (Energy cubes & trading)
    st.subheader("Energy Trade Overview")
    energy_trade = {
        "USA": 50,
        "Germany": 40,
        "India": 30
    }
    trade_df = pd.DataFrame(list(energy_trade.items()), columns=["Country", "Energy (MW)"])
    fig = px.bar(trade_df, x="Country", y="Energy (MW)", title="Energy Trading Overview")
    st.plotly_chart(fig)

    st.write("Energy cubes available for trading:")
    st.write("The system can trade energy with other countries.")

# code 6



# import streamlit as st
# import pandas as pd
# import plotly.graph_objects as go
# from app_backend import fetch_data, generate_recommendations, grid_health_status, generate_trading_options

# # Dashboard layout
# st.title('Real-Time Smart Grid Application Dashboard')
# st.sidebar.title('Navigation')
# tabs = ['Home', 'Energy Storage', 'Electricity Trade Management']
# selected_tab = st.sidebar.radio('Choose Tab', tabs)

# # Fetch data and prepare visualizations
# data = fetch_data()

# if selected_tab == 'Home':
#     st.header('Home - Real-Time Data Overview')
    
#     # Current weather and grid information
#     st.subheader("Location Weather Data")
#     st.write(f"Temperature: {data['temperature']} °C")
#     st.write(f"Wind Speed: {data['wind_speed']} m/s")
#     st.write(f"Weather: {data['weather_condition']}")

#     # Real-Time Power Flow Visualization (with dynamic chart)
#     st.subheader("Real-Time Power Flow Visualization")
#     fig = go.Figure()
#     fig.add_trace(go.Scatter(x=data['timestamps'], y=data['total_consumption'], mode='lines', name='Total Consumption (MW)', line=dict(color='red')))
#     fig.add_trace(go.Scatter(x=data['timestamps'], y=data['grid_generation'], mode='lines', name='Grid Generation (MW)', line=dict(color='green')))
#     fig.add_trace(go.Scatter(x=data['timestamps'], y=data['storage_usage'], mode='lines', name='Storage Usage (MW)', line=dict(color='blue')))
#     fig.update_layout(title="Power Flow", xaxis_title="Time", yaxis_title="Power (MW)")
#     st.plotly_chart(fig)

#     # Grid Health Status
#     st.subheader("Grid Health Indicators")
#     health_status = grid_health_status(data)
#     st.write(health_status)

#     # AI Recommendations
#     st.subheader("AI Recommendations")
#     recommendations = generate_recommendations(data)
#     for rec in recommendations:
#         st.write(rec)

# elif selected_tab == 'Energy Storage':
#     st.header('Energy Storage Overview')
    
#     # Storage Contribution Chart
#     st.subheader("Energy Storage Contribution by Resources")
#     fig = go.Figure()
#     fig.add_trace(go.Pie(labels=['Solar', 'Wind', 'Hydro'], values=[data['solar_storage'], data['wind_storage'], data['hydro_storage']], hole=0.3))
#     fig.update_layout(title="Energy Storage Contribution")
#     st.plotly_chart(fig)
    
#     # Total Energy Storage with Circle Representation
#     st.subheader("Total Energy Storage Representation")
#     fig = go.Figure()
#     fig.add_trace(go.Scatterpolar(r=[data['solar_storage'], data['wind_storage'], data['hydro_storage']], theta=['Solar', 'Wind', 'Hydro'], fill='toself', name='Energy Sources'))
#     fig.add_trace(go.Scatterpolar(r=[data['total_storage']], theta=['Total Storage'], fill='none', mode='text+markers', text=['Total Storage'], textposition='center', marker=dict(size=15, color='purple')))
#     fig.update_layout(title="Energy Storage and Total", polar=dict(radialaxis=dict(visible=True)), showlegend=False)
#     st.plotly_chart(fig)

# elif selected_tab == 'Electricity Trade Management':
#     st.header('Electricity Trade Management')
    
#     # Display available energy for trade
#     st.subheader("Energy Trade Availability")
#     trading_options = generate_trading_options(data)
#     st.write(trading_options)