Create energy_api.py

web_app/api/energy_api.py

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+from flask import Flask, jsonify, request
+from flask_openapi import OpenAPI
+import pandas as pd
+import numpy as np
+from sklearn.ensemble import RandomForestRegressor
+
+app = Flask(__name__)
+api = OpenAPI(app)
+
+# Load energy data
+energy_data = pd.read_csv("energy_data.csv")
+
+# Define forecasting model
+model = RandomForestRegressor(n_estimators=100)
+
+# Define API endpoints
+@api.route("/energy/forecast", methods=["GET"])
+def get_forecast():
+    # Get forecast data
+    forecast_data = model.predict(energy_data)
+    return jsonify({"forecast": forecast_data.tolist()})
+
+@api.route("/energy/realtime", methods=["GET"])
+def get_realtime_data():
+    # Get real-time data
+    realtime_data = energy_data.iloc[-1]
+    return jsonify({"realtime": realtime_data.to_dict()})
+
+@api.route("/energy/historical", methods=["GET"])
+def get_historical_data():
+    # Get historical data
+    historical_data = energy_data.to_dict("records")
+    return jsonify({"historical": historical_data})
+
+if __name__ == "__main__":
+    app.run(debug=True)