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
    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)