Update app.py

app.py

@@ -476,105 +476,177 @@
 # code 5
 
 
+# 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']}")
+
+# # Tabs
+# tab_home, tab_storage, tab_trading = st.tabs(["Home", "Power Storage", "Electricity Trade Management"])
+
+# # Home Tab
+# with tab_home:
+#     st.title("Real-Time Smart Grid Dashboard")
+
+#     # Generate synthetic data
+#     data = generate_synthetic_data()
+
+#     # Grid Health
+#     # st.subheader("Grid Health Overview")
+#     # grid_health_counts = data["grid_health"].value_counts()
+#     # st.bar_chart(grid_health_counts)
+
+#     # Power Consumption, Generation & Storage Graph
+#     st.subheader("Power Consumption, Generation & Storage")
+#     fig = px.line(data, x="timestamp", y=["load_demand_kwh", "solar_output_kw", "wind_output_kw"], 
+#                   title="Power Consumption, Generation & Storage", labels={"value": "Power (MW)"})
+#     fig.update_traces(line=dict(width=2))
+#     st.plotly_chart(fig)
+
+#     # Grid Health
+#     st.subheader("Grid Health Overview")
+#     grid_health_counts = data["grid_health"].value_counts()
+#     st.bar_chart(grid_health_counts)
+
+    
+#     # Optimization Recommendations
+#     current_demand = data["load_demand_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} MW")
+#     st.write(f"Solar Output: {current_solar} MW")
+#     st.write(f"Wind Output: {current_wind} MW")
+#     st.write(f"Recommendation: {recommendation}")
+
+# # Storage Tab
+# with tab_storage:
+#     st.title("Energy Storage Overview")
+
+#     # Energy Contribution by Resources
+#     st.subheader("Energy Contribution Percentage by Resources")
+#     energy_data = {
+#         "Wind": 5,
+#         "Solar": 7,
+#         "Turbine": 10
+#     }
+#     energy_df = pd.DataFrame(list(energy_data.items()), columns=["Source", "Energy (MW)"])
+#     fig = px.pie(energy_df, values="Energy (MW)", names="Source", title="Energy Contribution by Resources")
+#     st.plotly_chart(fig)
+
+#     # Energy Storage Merge
+#     st.subheader("Total Energy Stored")
+#     st.write("Energy stored from all sources:")
+#     energy_stored = sum(energy_data.values())
+#     st.write(f"Total Energy Stored: {energy_stored} MW")
+#     st.write("Energy sources merged into total energy storage:")
+#     st.write(f"Total Energy Stored in Grid: {energy_stored} MW")
+
+# # Trading Tab
+# with tab_trading:
+#     st.title("Electricity Trade Management")
+
+#     # Simulating Electricity Trade (Energy cubes & trading)
+#     st.subheader("Energy Trade Overview")
+#     energy_trade = {
+#         "USA": 50,
+#         "Germany": 40,
+#         "India": 30
+#     }
+#     trade_df = pd.DataFrame(list(energy_trade.items()), columns=["Country", "Energy (MW)"])
+#     fig = px.bar(trade_df, x="Country", y="Energy (MW)", title="Energy Trading Overview")
+#     st.plotly_chart(fig)
+
+#     st.write("Energy cubes available for trading:")
+#     st.write("The system can trade energy with other countries.")
+
+# code 6
+
+
+
 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']}")
-
-# Tabs
-tab_home, tab_storage, tab_trading = st.tabs(["Home", "Power Storage", "Electricity Trade Management"])
-
-# Home Tab
-with tab_home:
-    st.title("Real-Time Smart Grid Dashboard")
-
-    # Generate synthetic data
-    data = generate_synthetic_data()
-
-    # Grid Health
-    # st.subheader("Grid Health Overview")
-    # grid_health_counts = data["grid_health"].value_counts()
-    # st.bar_chart(grid_health_counts)
-
-    # Power Consumption, Generation & Storage Graph
-    st.subheader("Power Consumption, Generation & Storage")
-    fig = px.line(data, x="timestamp", y=["load_demand_kwh", "solar_output_kw", "wind_output_kw"], 
-                  title="Power Consumption, Generation & Storage", labels={"value": "Power (MW)"})
-    fig.update_traces(line=dict(width=2))
-    st.plotly_chart(fig)
+import plotly.graph_objects as go
+from app_backend import fetch_data, generate_recommendations, grid_health_status, generate_trading_options
+
+# Dashboard layout
+st.title('Real-Time Smart Grid Application Dashboard')
+st.sidebar.title('Navigation')
+tabs = ['Home', 'Energy Storage', 'Electricity Trade Management']
+selected_tab = st.sidebar.radio('Choose Tab', tabs)
 
-    # Grid Health
-    st.subheader("Grid Health Overview")
-    grid_health_counts = data["grid_health"].value_counts()
-    st.bar_chart(grid_health_counts)
+# Fetch data and prepare visualizations
+data = fetch_data()
 
+if selected_tab == 'Home':
+    st.header('Home - Real-Time Data Overview')
     
-    # Optimization Recommendations
-    current_demand = data["load_demand_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} MW")
-    st.write(f"Solar Output: {current_solar} MW")
-    st.write(f"Wind Output: {current_wind} MW")
-    st.write(f"Recommendation: {recommendation}")
-
-# Storage Tab
-with tab_storage:
-    st.title("Energy Storage Overview")
-
-    # Energy Contribution by Resources
-    st.subheader("Energy Contribution Percentage by Resources")
-    energy_data = {
-        "Wind": 5,
-        "Solar": 7,
-        "Turbine": 10
-    }
-    energy_df = pd.DataFrame(list(energy_data.items()), columns=["Source", "Energy (MW)"])
-    fig = px.pie(energy_df, values="Energy (MW)", names="Source", title="Energy Contribution by Resources")
+    # Current weather and grid information
+    st.subheader("Location Weather Data")
+    st.write(f"Temperature: {data['temperature']} °C")
+    st.write(f"Wind Speed: {data['wind_speed']} m/s")
+    st.write(f"Weather: {data['weather_condition']}")
+
+    # Real-Time Power Flow Visualization (with dynamic chart)
+    st.subheader("Real-Time Power Flow Visualization")
+    fig = go.Figure()
+    fig.add_trace(go.Scatter(x=data['timestamps'], y=data['total_consumption'], mode='lines', name='Total Consumption (MW)', line=dict(color='red')))
+    fig.add_trace(go.Scatter(x=data['timestamps'], y=data['grid_generation'], mode='lines', name='Grid Generation (MW)', line=dict(color='green')))
+    fig.add_trace(go.Scatter(x=data['timestamps'], y=data['storage_usage'], mode='lines', name='Storage Usage (MW)', line=dict(color='blue')))
+    fig.update_layout(title="Power Flow", xaxis_title="Time", yaxis_title="Power (MW)")
     st.plotly_chart(fig)
 
-    # Energy Storage Merge
-    st.subheader("Total Energy Stored")
-    st.write("Energy stored from all sources:")
-    energy_stored = sum(energy_data.values())
-    st.write(f"Total Energy Stored: {energy_stored} MW")
-    st.write("Energy sources merged into total energy storage:")
-    st.write(f"Total Energy Stored in Grid: {energy_stored} MW")
-
-# Trading Tab
-with tab_trading:
-    st.title("Electricity Trade Management")
-
-    # Simulating Electricity Trade (Energy cubes & trading)
-    st.subheader("Energy Trade Overview")
-    energy_trade = {
-        "USA": 50,
-        "Germany": 40,
-        "India": 30
-    }
-    trade_df = pd.DataFrame(list(energy_trade.items()), columns=["Country", "Energy (MW)"])
-    fig = px.bar(trade_df, x="Country", y="Energy (MW)", title="Energy Trading Overview")
-    st.plotly_chart(fig)
+    # Grid Health Status
+    st.subheader("Grid Health Indicators")
+    health_status = grid_health_status(data)
+    st.write(health_status)
 
-    st.write("Energy cubes available for trading:")
-    st.write("The system can trade energy with other countries.")
+    # AI Recommendations
+    st.subheader("AI Recommendations")
+    recommendations = generate_recommendations(data)
+    for rec in recommendations:
+        st.write(rec)
 
+elif selected_tab == 'Energy Storage':
+    st.header('Energy Storage Overview')
+    
+    # Storage Contribution Chart
+    st.subheader("Energy Storage Contribution by Resources")
+    fig = go.Figure()
+    fig.add_trace(go.Pie(labels=['Solar', 'Wind', 'Hydro'], values=[data['solar_storage'], data['wind_storage'], data['hydro_storage']], hole=0.3))
+    fig.update_layout(title="Energy Storage Contribution")
+    st.plotly_chart(fig)
+    
+    # Total Energy Storage with Circle Representation
+    st.subheader("Total Energy Storage Representation")
+    fig = go.Figure()
+    fig.add_trace(go.Scatterpolar(r=[data['solar_storage'], data['wind_storage'], data['hydro_storage']], theta=['Solar', 'Wind', 'Hydro'], fill='toself', name='Energy Sources'))
+    fig.add_trace(go.Scatterpolar(r=[data['total_storage']], theta=['Total Storage'], fill='none', mode='text+markers', text=['Total Storage'], textposition='center', marker=dict(size=15, color='purple')))
+    fig.update_layout(title="Energy Storage and Total", polar=dict(radialaxis=dict(visible=True)), showlegend=False)
+    st.plotly_chart(fig)
+
+elif selected_tab == 'Electricity Trade Management':
+    st.header('Electricity Trade Management')
+    
+    # Display available energy for trade
+    st.subheader("Energy Trade Availability")
+    trading_options = generate_trading_options(data)
+    st.write(trading_options)