Create app.py

app.py

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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 = "New York"
+
+# 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": ["Country A", "Country B", "Country C"],
+        "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)