Update app_backend.py

app_backend.py

@@ -1,6 +1,47 @@
+# 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!")
+
 import pandas as pd
 import numpy as np
-import plotly.express as px
 from datetime import datetime, timedelta
 import requests
 
@@ -16,28 +57,25 @@ def fetch_weather(api_key, location):
         }
     return None
 
-# Generate synthetic grid data
+# Generate synthetic 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))
+        "total_power_consumption_mw": np.random.randint(200, 500, len(time_index)),
+        "grid_generation_mw": np.random.randint(100, 300, len(time_index)),
+        "storage_utilization_mw": np.random.randint(50, 150, 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"
+# Generate storage data
+def generate_storage_data():
+    return {
+        "wind": 5,
+        "solar": 7,
+        "turbine": 10,
+        "total_stored_kwh": 2000
+    }
 
 # Export functions for use in Streamlit
 if __name__ == "__main__":
     print("Backend ready!")
-
-