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Update app.py
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app.py
CHANGED
@@ -1,3 +1,114 @@
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import streamlit as st
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import pandas as pd
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import plotly.express as px
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st.sidebar.write(f"Wind Speed: {weather['wind_speed']} m/s")
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st.sidebar.write(f"Weather: {weather['weather']}")
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# Main dashboard
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})
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st.bar_chart(energy_sources.set_index("Source"))
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# Show energy storage status with a rounded circle
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st.subheader("Energy Storage Circle")
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st.markdown("Energy storage is a combination of contributions from different renewable sources.")
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# Visualization of energy storage circle using Plotly
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storage_data = {
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"Source": ["Wind", "Solar", "Turbine"],
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"Energy": [5, 7, 10],
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}
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storage_df = pd.DataFrame(storage_data)
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fig = px.pie(storage_df, names="Source", values="Energy", title="Energy Storage Sources")
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st.plotly_chart(fig)
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with tabs[2]:
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st.title("Energy Trading Overview")
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# Energy cubes
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st.subheader("Energy Cubes Stored")
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energy_cubes = pd.DataFrame({
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"Country": ["China", "Sri Lanka", "Bangladesh"],
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"Energy (kWh)": [100, 200, 300],
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"Shareable": [True, True, False]
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})
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# Displaying the energy cubes in a grid
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st.write("Stored energy can be shared with other countries.")
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st.dataframe(energy_cubes)
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# Visualization of energy that can be shared
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st.subheader("Energy Trading Visualization")
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st.markdown("The following energy amounts are available for sharing with different countries.")
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trading_fig = px.bar(energy_cubes, x="Country", y="Energy (kWh)", color="Shareable", title="Energy Trading")
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st.plotly_chart(trading_fig)
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# import streamlit as st
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# import pandas as pd
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# import plotly.express as px
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# from app_backend import fetch_weather, generate_synthetic_data, optimize_load
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# # Constants
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# API_KEY = "84e26811a314599e940f343b4d5894a7"
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# LOCATION = "Pakistan"
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# # Sidebar
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# st.sidebar.title("Smart Grid Dashboard")
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# location = st.sidebar.text_input("Enter Location", LOCATION)
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# # Fetch and display weather data
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# weather = fetch_weather(API_KEY, location)
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# if weather:
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# st.sidebar.write(f"Temperature: {weather['temperature']} °C")
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# st.sidebar.write(f"Wind Speed: {weather['wind_speed']} m/s")
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# st.sidebar.write(f"Weather: {weather['weather']}")
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# # Main dashboard with tabs
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# tabs = st.tabs(["Home", "Storage", "Trading"])
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# with tabs[0]:
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# st.title("Real-Time Smart Grid Dashboard")
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# # Generate synthetic data
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# data = generate_synthetic_data()
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# # Plot total consumption, grid generation, and storage usage
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# fig = px.line(data, x="timestamp", y=["total_consumption_kwh", "grid_generation_kwh", "storage_usage_kwh"],
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# title="Energy Consumption, Generation, and Storage Usage Over Time",
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# labels={"value": "Energy (kWh)", "variable": "Energy Source"})
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# st.plotly_chart(fig)
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# # Grid health overview
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# st.subheader("Grid Health Overview")
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# grid_health_counts = data["grid_health"].value_counts()
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# st.bar_chart(grid_health_counts)
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# # Optimization recommendations
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# current_demand = data["total_consumption_kwh"].iloc[-1]
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# current_solar = data["solar_output_kw"].iloc[-1]
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# current_wind = data["wind_output_kw"].iloc[-1]
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# recommendation = optimize_load(current_demand, current_solar, current_wind)
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# st.subheader("Recommendations")
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# st.write(f"Current Load Demand: {current_demand} kWh")
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# st.write(f"Solar Output: {current_solar} kW")
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# st.write(f"Wind Output: {current_wind} kW")
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# st.write(f"Recommendation: {recommendation}")
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# with tabs[1]:
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# st.title("Energy Storage Overview")
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# # Total energy stored
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# total_storage = 500 # Example of total energy storage
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# st.subheader(f"Total Energy Stored: {total_storage} kWh")
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# # Energy storage contribution from different sources
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# st.subheader("Energy Storage Contributions")
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# energy_sources = pd.DataFrame({
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# "Source": ["Wind", "Solar", "Turbine"],
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# "Energy (kW/min)": [5, 7, 10]
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# })
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# st.bar_chart(energy_sources.set_index("Source"))
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# # Show energy storage status with a rounded circle
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# st.subheader("Energy Storage Circle")
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# st.markdown("Energy storage is a combination of contributions from different renewable sources.")
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# # Visualization of energy storage circle using Plotly
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# storage_data = {
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# "Source": ["Wind", "Solar", "Turbine"],
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# "Energy": [5, 7, 10],
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# }
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# storage_df = pd.DataFrame(storage_data)
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# fig = px.pie(storage_df, names="Source", values="Energy", title="Energy Storage Sources")
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# st.plotly_chart(fig)
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# with tabs[2]:
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# st.title("Energy Trading Overview")
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# # Energy cubes
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# st.subheader("Energy Cubes Stored")
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# energy_cubes = pd.DataFrame({
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# "Country": ["China", "Sri Lanka", "Bangladesh"],
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# "Energy (kWh)": [100, 200, 300],
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# "Shareable": [True, True, False]
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# })
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# # Displaying the energy cubes in a grid
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# st.write("Stored energy can be shared with other countries.")
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# st.dataframe(energy_cubes)
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# # Visualization of energy that can be shared
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# st.subheader("Energy Trading Visualization")
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# st.markdown("The following energy amounts are available for sharing with different countries.")
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# trading_fig = px.bar(energy_cubes, x="Country", y="Energy (kWh)", color="Shareable", title="Energy Trading")
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# st.plotly_chart(trading_fig)
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import streamlit as st
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import pandas as pd
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import plotly.express as px
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st.sidebar.write(f"Wind Speed: {weather['wind_speed']} m/s")
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st.sidebar.write(f"Weather: {weather['weather']}")
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# Main dashboard
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st.title("Real-Time Smart Grid Dashboard")
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# Generate synthetic data
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data = generate_synthetic_data()
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# Plot total power consumption (load demand) in MW
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fig = px.line(data, x="timestamp", y="load_demand_mw", title="Power Consumption (MW) Over Time")
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st.plotly_chart(fig)
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# Plot renewable energy generation in MW (solar + wind) on the graph
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fig = px.bar(
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data,
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x="timestamp",
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y=["solar_output_mw", "wind_output_mw"],
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title="Renewable Energy Generation (MW)",
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labels={"value": "Power (MW)", "variable": "Energy Source"}
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)
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st.plotly_chart(fig)
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# Show battery storage in kWh
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fig = px.line(data, x="timestamp", y="battery_storage_kwh", title="Battery Storage (kWh) Over Time")
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st.plotly_chart(fig)
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# Grid health
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st.subheader("Grid Health Overview")
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grid_health_counts = data["grid_health"].value_counts()
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st.bar_chart(grid_health_counts)
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# Optimization recommendations
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current_demand = data["load_demand_mw"].iloc[-1] # Load demand in MW
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current_solar = data["solar_output_mw"].iloc[-1] # Solar output in MW
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current_wind = data["wind_output_mw"].iloc[-1] # Wind output in MW
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recommendation = optimize_load(current_demand, current_solar, current_wind)
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st.subheader("Recommendations")
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st.write(f"Current Load Demand: {current_demand} MW")
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st.write(f"Solar Output: {current_solar} MW")
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st.write(f"Wind Output: {current_wind} MW")
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st.write(f"Recommendation: {recommendation}")
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# Electricity Trade Management Tab
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st.subheader("Electricity Trade Management")
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st.write("Manage energy trading here.")
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