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
+st.title("Real-Time Smart Grid Dashboard")
+
+# Generate synthetic data
+data = generate_synthetic_data()
+
+# Plot load demand
+fig = px.line(data, x="timestamp", y="load_demand_kwh", title="Load Demand Over Time")
+st.plotly_chart(fig)
+
+# Renewable energy contribution
+fig = px.bar(
+    data,
+    x="timestamp",
+    y=["solar_output_kw", "wind_output_kw"],
+    title="Renewable Energy Contributions",
+    labels={"value": "Power (kW)", "variable": "Energy Source"}
+)
+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} kWh")
+st.write(f"Solar Output: {current_solar} kW")
+st.write(f"Wind Output: {current_wind} kW")
+st.write(f"Recommendation: {recommendation}")