Create app_backend.py

app_backend.py

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+import pandas as pd
+import numpy as np
+import plotly.express as px
+from datetime import datetime, timedelta
+import requests
+
+# Function to fetch real-time weather data
+def fetch_weather(api_key, location):
+    url = f"http://api.openweathermap.org/data/2.5/weather?q={location}&appid={api_key}&units=metric"
+    response = requests.get(url).json()
+    if response["cod"] == 200:
+        return {
+            "temperature": response["main"]["temp"],
+            "wind_speed": response["wind"]["speed"],
+            "weather": response["weather"][0]["description"]
+        }
+    return None
+
+# Generate synthetic grid data
+def generate_synthetic_data():
+    time_index = pd.date_range(start=datetime.now(), periods=24, freq="H")
+    return pd.DataFrame({
+        "timestamp": time_index,
+        "total_consumption_kwh": np.random.randint(200, 500, len(time_index)),
+        "grid_generation_kwh": np.random.randint(150, 400, len(time_index)),
+        "storage_usage_kwh": np.random.randint(50, 150, len(time_index)),
+        "solar_output_kw": np.random.randint(50, 150, len(time_index)),
+        "wind_output_kw": np.random.randint(30, 120, len(time_index)),
+        "grid_health": np.random.choice(["Good", "Moderate", "Critical"], len(time_index))
+    })
+
+# Load optimization recommendation
+def optimize_load(demand, solar, wind):
+    renewable_supply = solar + wind
+    if renewable_supply >= demand:
+        return "Grid Stable"
+    return "Use Backup or Adjust Load"
+
+# Export functions for use in Streamlit
+if __name__ == "__main__":
+    print("Backend ready!")