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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) |