Update app.py

app.py

@@ -100,6 +100,107 @@
 #     st.plotly_chart(trading_fig)
 
 
+# code 2
+
+
+# import streamlit as st
+# import pandas as pd
+# import plotly.graph_objects as go
+# from app_backend import fetch_weather, generate_synthetic_data, generate_storage_data
+
+# # Constants
+# API_KEY = "84e26811a314599e940f343b4d5894a7"
+# DEFAULT_LOCATION = "Pakistan"
+
+# # Sidebar for location and weather data
+# st.sidebar.title("Smart Grid Dashboard")
+# 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 interface
+# st.title("Real-Time Smart Grid Dashboard")
+
+# # Tabs
+# tabs = st.tabs(["Home", "Power Storage", "Electricity Trade Management"])
+
+# # Home Tab
+# with tabs[0]:
+#     st.header("Overview: Power and Energy Usage")
+    
+#     # Fetch synthetic data
+#     data = generate_synthetic_data()
+
+#     # 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)
+
+# # Storage Tab
+# with tabs[1]:
+#     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.header("Electricity Trade Management")
+
+#     # Sample trade data
+#     trade_data = {
+#         "Country": ["Srilanka", "China", "Bangladesh"],
+#         "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)
+
+
+# code 3
+
 
 import streamlit as st
 import pandas as pd
@@ -108,7 +209,7 @@ from app_backend import fetch_weather, generate_synthetic_data, generate_storage
 
 # Constants
 API_KEY = "84e26811a314599e940f343b4d5894a7"
-DEFAULT_LOCATION = "Pakistan"
+DEFAULT_LOCATION = "pakistan"
 
 # Sidebar for location and weather data
 st.sidebar.title("Smart Grid Dashboard")
@@ -124,7 +225,7 @@ if weather:
 st.title("Real-Time Smart Grid Dashboard")
 
 # Tabs
-tabs = st.tabs(["Home", "Power Storage", "Electricity Trade Management"])
+tabs = st.tabs(["Home", "Storage", "Electricity Trade Management"])
 
 # Home Tab
 with tabs[0]:
@@ -159,28 +260,71 @@ with tabs[0]:
     fig.update_layout(title="Power and Energy Trends", xaxis_title="Time", yaxis_title="Power (MW)")
     st.plotly_chart(fig)
 
+    # Grid Health Indicator
+    st.subheader("Grid Health Status")
+    grid_health = "Stable" if data["grid_generation_mw"].mean() >= data["total_power_consumption_mw"].mean() else "Critical"
+    st.write(f"**Grid Health:** {grid_health}")
+
+    # AI Recommendations
+    st.subheader("AI Recommendations")
+    recommendations = [
+        "Increase solar panel efficiency by 10% for peak hours.",
+        "Optimize wind turbine alignment based on real-time wind data.",
+        "Store excess energy during low-demand periods for future use.",
+        "Improve grid stability by distributing load dynamically across sectors.",
+    ]
+    for rec in recommendations:
+        st.write(f"- {rec}")
+
 # Storage Tab
 with tabs[1]:
     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")
+    # Individual Circles for Wind, Solar, and Turbine
+    st.subheader("Energy Contributions")
+    col1, col2, col3 = st.columns(3)
+    with col1:
+        st.metric("Wind Energy", f"{storage_data['wind']} MW/min")
+    with col2:
+        st.metric("Solar Energy", f"{storage_data['solar']} MW/min")
+    with col3:
+        st.metric("Turbine Energy", f"{storage_data['turbine']} MW/min")
+
+    # Central Grid Storage Visualization
+    st.subheader("Total Energy Stored in Grid")
+    fig = go.Figure()
+    fig.add_trace(go.Scatter(x=[0], y=[0], mode='markers+text', text=["Grid"], marker=dict(size=70, color="blue")))
+    fig.add_trace(go.Scatter(
+        x=[-1, 1, 0],
+        y=[1, 1, -1],
+        mode='markers+text',
+        text=["Wind", "Solar", "Turbine"],
+        marker=dict(size=50, color=["green", "yellow", "orange"])
+    ))
+    fig.add_trace(go.Scatter(
+        x=[-0.5, 0.5, 0],
+        y=[0.5, 0.5, -0.5],
+        mode="lines",
+        line=dict(width=3, color="gray"),
+    ))
+    fig.update_layout(
+        title="Energy Storage Visualization",
+        xaxis=dict(visible=False),
+        yaxis=dict(visible=False),
+        showlegend=False
+    )
     st.plotly_chart(fig)
 
+    st.write(f"**Total Energy Stored:** {storage_data['total_stored_kwh']} kWh")
+
 # Electricity Trade Management Tab
 with tabs[2]:
     st.header("Electricity Trade Management")
 
     # Sample trade data
     trade_data = {
-        "Country": ["Srilanka", "China", "Bangladesh"],
+        "Country": ["Country A", "Country B", "Country C"],
         "Energy Exported (MW)": [50, 30, 70],
         "Energy Imported (MW)": [20, 40, 10],
     }
@@ -198,5 +342,3 @@ with tabs[2]:
 
 
 
-
-