Update app.py

app.py

@@ -476,177 +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)
+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)
+    # 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")
+    # 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)
 
-#     # 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)
+    # 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.")
+    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.graph_objects as go
-from app_backend import fetch_data, generate_recommendations, grid_health_status, generate_trading_options
+# import streamlit as st
+# import pandas as pd
+# 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)
+# # 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)
 
-# Fetch data and prepare visualizations
-data = fetch_data()
+# # Fetch data and prepare visualizations
+# data = fetch_data()
 
-if selected_tab == 'Home':
-    st.header('Home - Real-Time Data Overview')
+# if selected_tab == 'Home':
+#     st.header('Home - Real-Time Data Overview')
     
-    # 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)
+#     # 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)
 
-    # Grid Health Status
-    st.subheader("Grid Health Indicators")
-    health_status = grid_health_status(data)
-    st.write(health_status)
+#     # Grid Health Status
+#     st.subheader("Grid Health Indicators")
+#     health_status = grid_health_status(data)
+#     st.write(health_status)
 
-    # AI Recommendations
-    st.subheader("AI Recommendations")
-    recommendations = generate_recommendations(data)
-    for rec in recommendations:
-        st.write(rec)
+#     # 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')
+# 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)
+#     # 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)
+#     # 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')
+# 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)
+#     # Display available energy for trade
+#     st.subheader("Energy Trade Availability")
+#     trading_options = generate_trading_options(data)
+#     st.write(trading_options)