mirror of
https://github.com/Akkudoktor-EOS/EOS.git
synced 2025-04-19 08:55:15 +00:00
229 lines
8.8 KiB
Python
229 lines
8.8 KiB
Python
from flask import Flask, jsonify, request
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import numpy as np
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from datetime import datetime
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from modules.class_load import *
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from modules.class_ems import *
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from modules.class_pv_forecast import *
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from modules.class_akku import *
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from modules.class_strompreis import *
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from modules.class_heatpump import *
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from modules.class_load_container import *
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from modules.class_eauto import *
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from pprint import pprint
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import matplotlib
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matplotlib.use('Agg') # Setzt das Backend auf Agg
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import matplotlib.pyplot as plt
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from modules.visualize import *
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from deap import base, creator, tools, algorithms
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import numpy as np
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import random
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import os
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start_hour = datetime.now().hour
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prediction_hours = 24
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hohe_strafe = 10.0
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def evaluate_inner(individual, ems):
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discharge_hours_bin = individual[0::2]
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eautocharge_hours_float = individual[1::2]
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#print(discharge_hours_bin)
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#print(len(eautocharge_hours_float))
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ems.reset()
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ems.set_akku_discharge_hours(discharge_hours_bin)
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ems.set_eauto_charge_hours(eautocharge_hours_float)
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o = ems.simuliere(start_hour)
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return o
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# Fitness-Funktion (muss Ihre EnergieManagementSystem-Logik integrieren)
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def evaluate(individual,ems,parameter):
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o = evaluate_inner(individual,ems)
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gesamtbilanz = o["Gesamtbilanz_Euro"]
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# Überprüfung, ob der Mindest-SoC erreicht wird
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final_soc = ems.eauto.ladezustand_in_prozent() # Nimmt den SoC am Ende des Optimierungszeitraums
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strafe = 0.0
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strafe = max(0,(parameter['eauto_min_soc']-ems.eauto.ladezustand_in_prozent()) * hohe_strafe )
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# print(ems.eauto.charge_array)
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# print(ems.eauto.ladezustand_in_prozent())
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# print(strafe)
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gesamtbilanz += strafe
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gesamtbilanz += o["Gesamt_Verluste"]/1000.0
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return (gesamtbilanz,)
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# Werkzeug-Setup
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creator.create("FitnessMin", base.Fitness, weights=(-1.0,))
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creator.create("Individual", list, fitness=creator.FitnessMin)
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toolbox = base.Toolbox()
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toolbox.register("attr_bool", random.randint, 0, 1)
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toolbox.register("attr_bool", random.randint, 0, 1)
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toolbox.register("individual", tools.initCycle, creator.Individual, (toolbox.attr_bool,toolbox.attr_bool), n=prediction_hours)
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toolbox.register("population", tools.initRepeat, list, toolbox.individual)
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toolbox.register("mate", tools.cxTwoPoint)
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toolbox.register("mutate", tools.mutFlipBit, indpb=0.05)
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toolbox.register("select", tools.selTournament, tournsize=3)
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# Genetischer Algorithmus
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def optimize(start_solution=None):
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population = toolbox.population(n=100)
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hof = tools.HallOfFame(1)
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stats = tools.Statistics(lambda ind: ind.fitness.values)
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stats.register("avg", np.mean)
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stats.register("min", np.min)
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stats.register("max", np.max)
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print("Start:",start_solution)
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if start_solution is not None and start_solution != -1:
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population.insert(0, creator.Individual(start_solution))
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#algorithms.eaMuPlusLambda(population, toolbox, 100, 200, cxpb=0.3, mutpb=0.3, ngen=500, stats=stats, halloffame=hof, verbose=True)
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algorithms.eaSimple(population, toolbox, cxpb=0.5, mutpb=0.8, ngen=200, stats=stats, halloffame=hof, verbose=True)
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return hof[0]
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app = Flask(__name__)
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# Dummy-Funktion für die Durchführung der Simulation/Optimierung
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# Ersetzen Sie diese Logik durch Ihren eigentlichen Optimierungscode
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def durchfuehre_simulation(parameter):
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############
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# Parameter
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############
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date = (datetime.now().date() + timedelta(hours = prediction_hours)).strftime("%Y-%m-%d")
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date_now = datetime.now().strftime("%Y-%m-%d")
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akku_size = parameter['pv_akku_cap'] # Wh
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year_energy = parameter['year_energy'] #2000*1000 #Wh
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einspeiseverguetung_cent_pro_wh = np.full(prediction_hours, parameter["einspeiseverguetung_euro_pro_wh"]) #= # € / Wh 7/(1000.0*100.0)
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max_heizleistung = parameter['max_heizleistung'] #1000 # 5 kW Heizleistung
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wp = Waermepumpe(max_heizleistung,prediction_hours)
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pv_forecast_url = parameter['pv_forecast_url'] #"https://api.akkudoktor.net/forecast?lat=52.52&lon=13.405&power=5000&azimuth=-10&tilt=7&powerInvertor=10000&horizont=20,27,22,20&power=4800&azimuth=-90&tilt=7&powerInvertor=10000&horizont=30,30,30,50&power=1400&azimuth=-40&tilt=60&powerInvertor=2000&horizont=60,30,0,30&power=1600&azimuth=5&tilt=45&powerInvertor=1400&horizont=45,25,30,60&past_days=5&cellCoEff=-0.36&inverterEfficiency=0.8&albedo=0.25&timezone=Europe%2FBerlin&hourly=relativehumidity_2m%2Cwindspeed_10m"
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akku = PVAkku(kapazitaet_wh=akku_size,hours=prediction_hours,start_soc_prozent=parameter["pv_soc"])
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discharge_array = np.full(prediction_hours,1) #np.array([1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0]) #
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laden_moeglich = np.full(prediction_hours,1) # np.array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 0])
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eauto = PVAkku(kapazitaet_wh=parameter["eauto_cap"], hours=prediction_hours, lade_effizienz=parameter["eauto_charge_efficiency"], entlade_effizienz=1.0, max_ladeleistung_w=parameter["eauto_charge_power"] ,start_soc_prozent=parameter["eauto_soc"])
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eauto.set_charge_per_hour(laden_moeglich)
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min_soc_eauto = parameter['eauto_min_soc']
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start_params = parameter['start_solution']
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gesamtlast = Gesamtlast()
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###############
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# Load Forecast
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###############
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lf = LoadForecast(filepath=r'load_profiles.npz', year_energy=year_energy)
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#leistung_haushalt = lf.get_daily_stats(date)[0,...] # Datum anpassen
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leistung_haushalt = lf.get_stats_for_date_range(date_now,date)[0,...].flatten()
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gesamtlast.hinzufuegen("Haushalt", leistung_haushalt)
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###############
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# PV Forecast
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###############
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#PVforecast = PVForecast(filepath=os.path.join(r'test_data', r'pvprognose.json'))
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PVforecast = PVForecast(prediction_hours = prediction_hours, url=pv_forecast_url)
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pv_forecast = PVforecast.get_pv_forecast_for_date_range(date_now,date) #get_forecast_for_date(date)
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temperature_forecast = PVforecast.get_temperature_for_date_range(date_now,date)
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###############
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# Strompreise
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###############
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filepath = os.path.join (r'test_data', r'strompreise_akkudokAPI.json') # Pfad zur JSON-Datei anpassen
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#price_forecast = HourlyElectricityPriceForecast(source=filepath)
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price_forecast = HourlyElectricityPriceForecast(source="https://api.akkudoktor.net/prices?start="+date_now+"&end="+date+"")
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specific_date_prices = price_forecast.get_price_for_daterange(date_now,date)
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###############
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# WP
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##############
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leistung_wp = wp.simulate_24h(temperature_forecast)
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gesamtlast.hinzufuegen("Heatpump", leistung_wp)
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ems = EnergieManagementSystem(akku=akku, gesamtlast = gesamtlast, pv_prognose_wh=pv_forecast, strompreis_cent_pro_wh=specific_date_prices, einspeiseverguetung_cent_pro_wh=einspeiseverguetung_cent_pro_wh, eauto=eauto)
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o = ems.simuliere(start_hour)
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def evaluate_wrapper(individual):
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return evaluate(individual, ems, parameter)
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toolbox.register("evaluate", evaluate_wrapper)
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print()
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print("START:",start_params)
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print()
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start_solution = optimize(start_params)
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best_solution = start_solution
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o = evaluate_inner(best_solution, ems)
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eauto = ems.eauto.to_dict()
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discharge_hours_bin = best_solution[0::2]
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eautocharge_hours_float = best_solution[1::2]
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#print(o)
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visualisiere_ergebnisse(gesamtlast, pv_forecast, specific_date_prices, o,best_solution[0::2],best_solution[1::2] , temperature_forecast, start_hour, prediction_hours)
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#print(eauto)
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return {"discharge_hours_bin":discharge_hours_bin, "eautocharge_hours_float":eautocharge_hours_float ,"result":o ,"eauto_obj":eauto,"start_solution":best_solution}
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@app.route('/simulation', methods=['POST'])
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def simulation():
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if request.method == 'POST':
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parameter = request.json
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# Erforderliche Parameter prüfen
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erforderliche_parameter = [ 'pv_akku_cap', 'year_energy',"einspeiseverguetung_euro_pro_wh", 'max_heizleistung', 'pv_forecast_url', 'eauto_min_soc', "eauto_cap","eauto_charge_efficiency","eauto_charge_power","eauto_soc","pv_soc","start_solution"]
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for p in erforderliche_parameter:
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if p not in parameter:
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return jsonify({"error": f"Fehlender Parameter: {p}"}), 400
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# Optional Typen der Parameter prüfen und sicherstellen, dass sie den Erwartungen entsprechen
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# if not isinstance(parameter['start_hour'], int):
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# return jsonify({"error": "start_hour muss vom Typ int sein"}), 400
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# Simulation durchführen
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ergebnis = durchfuehre_simulation(parameter)
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return jsonify(ergebnis)
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if __name__ == '__main__':
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app.run(debug=True, host="0.0.0.0")
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