Update app_backend.py

app_backend.py

@@ -148,41 +148,87 @@
 # code 4
 
 
-import pandas as pd
-import numpy as np
-import requests
-from datetime import datetime
-
-# 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,
-        "load_demand_kwh": np.random.randint(200, 500, 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"
-
-if __name__ == "__main__":
-    print("Backend ready!")
+# import pandas as pd
+# import numpy as np
+# import requests
+# from datetime import datetime
+
+# # 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,
+#         "load_demand_kwh": np.random.randint(200, 500, 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"
+
+# if __name__ == "__main__":
+#     print("Backend ready!")
+
+
+
+# code 5
+
+
+import random
+
+def fetch_data():
+    # Simulating fetching data from a database or API
+    data = {
+        'temperature': random.uniform(-10, 30),
+        'wind_speed': random.uniform(0, 20),
+        'weather_condition': random.choice(['Clear', 'Overcast Clouds', 'Thunderstorm', 'Rain']),
+        'timestamps': pd.date_range("2025-01-01", periods=10, freq='H'),
+        'total_consumption': [random.uniform(50, 100) for _ in range(10)],
+        'grid_generation': [random.uniform(30, 80) for _ in range(10)],
+        'storage_usage': [random.uniform(10, 30) for _ in range(10)],
+        'solar_storage': random.uniform(10, 30),
+        'wind_storage': random.uniform(10, 30),
+        'hydro_storage': random.uniform(10, 30),
+        'total_storage': random.uniform(50, 100),
+    }
+    return data
+
+def generate_recommendations(data):
+    recommendations = []
+    if data['total_consumption'][-1] > data['grid_generation'][-1]:
+        recommendations.append("Consider integrating additional renewable sources to meet the current demand.")
+    if data['storage_usage'][-1] > data['total_storage'] * 0.8:
+        recommendations.append("Energy storage is running low. Consider optimizing the grid or adding more storage.")
+    return recommendations
+
+def grid_health_status(data):
+    status = "Grid is operating normally."
+    if data['total_consumption'][-1] > 90:
+        status = "Warning: High consumption detected!"
+    if data['wind_speed'] > 15:
+        status = "Warning: High wind speeds, may affect wind turbine output."
+    return status
+
+def generate_trading_options(data):
+    if data['total_storage'] > 60:
+        return "Energy is available for export to neighboring countries."
+    else:
+        return "Energy reserves are low. Trading is not recommended at this moment."