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']}")

# # 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)