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", "Storage", "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)











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

# Generate synthetic data
data = generate_synthetic_data()

# Plot total power consumption (load demand) in MW
fig = px.line(data, x="timestamp", y="load_demand_mw", title="Power Consumption (MW) Over Time")
st.plotly_chart(fig)

# Plot renewable energy generation in MW (solar + wind) on the graph
fig = px.bar(
    data,
    x="timestamp",
    y=["solar_output_mw", "wind_output_mw"],
    title="Renewable Energy Generation (MW)",
    labels={"value": "Power (MW)", "variable": "Energy Source"}
)
st.plotly_chart(fig)

# Show battery storage in kWh
fig = px.line(data, x="timestamp", y="battery_storage_kwh", title="Battery Storage (kWh) Over Time")
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_mw"].iloc[-1]  # Load demand in MW
current_solar = data["solar_output_mw"].iloc[-1]  # Solar output in MW
current_wind = data["wind_output_mw"].iloc[-1]  # Wind output in MW
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}")

# Electricity Trade Management Tab
st.subheader("Electricity Trade Management")
st.write("Manage energy trading here.")