Update app.py

app.py

@@ -1,107 +1,200 @@
+# 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)
+
+
+
 import streamlit as st
 import pandas as pd
-import plotly.express as px
-from app_backend import fetch_weather, generate_synthetic_data, optimize_load
+import plotly.graph_objects as go
+from app_backend import fetch_weather, generate_synthetic_data, generate_storage_data
 
 # Constants
 API_KEY = "84e26811a314599e940f343b4d5894a7"
-LOCATION = "Pakistan"
+DEFAULT_LOCATION = "Pakistan"
 
-# Sidebar
+# Sidebar for location and weather data
 st.sidebar.title("Smart Grid Dashboard")
-location = st.sidebar.text_input("Enter Location", LOCATION)
-
-# Fetch and display weather data
+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 dashboard with tabs
-tabs = st.tabs(["Home", "Electricity Storage", "Electricity Trading"])
+# Main interface
+st.title("Real-Time Smart Grid Dashboard")
 
-with tabs[0]:
-    st.title("Real-Time Smart Grid Dashboard")
+# Tabs
+tabs = st.tabs(["Home", "Storage", "Electricity Trade Management"])
 
-    # Generate synthetic data
+# Home Tab
+with tabs[0]:
+    st.header("Overview: Power and Energy Usage")
+    
+    # Fetch 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"})
+    # 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 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}")
-
+# Storage Tab
 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.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.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)
-
+    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)