app_backend.py

# 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!")



# code2


import pandas as pd
import numpy as np
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 data
def generate_synthetic_data():
    time_index = pd.date_range(start=datetime.now(), periods=24, freq="H")
    return pd.DataFrame({
        "timestamp": 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)),
    })

# 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!")


# code 3
# import pandas as pd
# import numpy as np
# from datetime import datetime, timedelta

# # Function to fetch weather data remains unchanged

# # 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,
#         "power_consumption_mw": np.random.randint(50, 200, len(time_index)),
#         "grid_generation_mw": np.random.randint(30, 150, len(time_index)),
#         "storage_utilization_mw": np.random.randint(10, 50, len(time_index)),
#         "grid_health": np.random.choice(["Good", "Moderate", "Critical"], len(time_index))
#     })

# # Generate synthetic storage data
# def generate_storage_data():
#     wind_storage = np.random.randint(5, 15)
#     solar_storage = np.random.randint(7, 20)
#     turbine_storage = np.random.randint(10, 25)
#     total_storage = wind_storage + solar_storage + turbine_storage
#     return {
#         "wind_storage_mw": wind_storage,
#         "solar_storage_mw": solar_storage,
#         "turbine_storage_mw": turbine_storage,
#         "total_storage_mw": total_storage
#     }

# # Generate synthetic trade data
# def generate_trade_data():
#     countries = ["Country A", "Country B", "Country C"]
#     exports = np.random.randint(10, 50, len(countries))
#     imports = np.random.randint(5, 30, len(countries))
#     return pd.DataFrame({
#         "country": countries,
#         "exports_mw": exports,
#         "imports_mw": imports
#     })

# # Updated optimization recommendation
# def optimize_load(demand, generation, storage):
#     if generation + storage >= demand:
#         return "Grid is Stable with Current Supply"
#     elif demand - (generation + storage) < 20:
#         return "Activate Backup or Optimize Load"
#     else:
#         return "Immediate Action Required: Adjust Load or Increase Generation"

# # Export functions
# if __name__ == "__main__":
#     print("Backend ready for enhanced dashboard!")