diff --git a/ebike.db b/ebike.db
new file mode 100644
index 0000000..ebb9384
Binary files /dev/null and b/ebike.db differ
diff --git a/insertLoading.py b/insertLoading.py
new file mode 100644
index 0000000..7f07d1a
--- /dev/null
+++ b/insertLoading.py
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+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+
+from fitparse import FitFile
+from datetime import datetime, timedelta
+import os, sys, shutil
+from os import walk
+import csv
+import pandas as pd
+
+
+path = "/home/manuel/Dropbox/Apps/HealthFitExporter/"
+#path = "/Users/manuel/Projekte/eBike-Influx-Calculation/"
+ext = ('.fit')
+csv_file = "/www/wwwroot/www.manuelw.de/ebike/ebike_data.csv"
+
+output = {}
+fitData = {}
+fitData["total_distance"] = 0
+fitData["total_ascent"] = 0
+fitData["avg_speed"] = 0
+fitData["avg_speed_count"] = 0
+fitFilesCount = 0
+
+loading = sys.argv[1]
+watt = sys.argv[2]
+row = []
+
+##
+### auf neue Files überwachen
+def main():
+	f = []
+	for base, dirs, files in os.walk(path):
+		files = [ fi for fi in files if fi.endswith(".fit") ]
+		if files:
+			processFIT(files)
+
+##
+### parse FIT file und bestimme Handling
+def processFIT(files):
+	global fitFilesCount
+
+	print("Files:", len(files))
+	fitFilesCount = len(files)
+	for filename in files:
+		#print(filename)
+		file = FitFile(path+filename)
+		for record in file.get_messages("session"):
+			## nur wenn Radfahren
+			if record.get_value("sport") == "cycling":
+				## Verarbeitung starten
+				doAll(file)
+
+	showEnd()
+	readCSV()
+	deleteFiles()
+
+
+##
+### todo on all fit files
+def doAll(file):
+	for record in file.get_messages("session"):
+		fitData["avg_cadence"] = record.get_value("avg_cadence")
+		#fitData["avg_speed"] += round(float(record.get_value("avg_speed")) / 0.27777777777778, 1)
+		fitData["avg_speed"] += record.get_value("avg_speed")
+		fitData["avg_speed_count"] +=1
+		#fitData["total_ascent"] = float(record.get_value("total_ascent")) if record.get_value("total_ascent") != None else 0
+		fitData["total_distance"] += float(round(record.get_value("total_distance") / 1000, 1))
+
+	fitData["avg_speed"] = round(float(fitData["avg_speed"] / fitData["avg_speed_count"]), 1)
+	doHealthFit(file, record.get_value("min_altitude"))
+
+
+def doHealthFit(file, min_alt=0.0):
+	### Kadenz
+	i=0
+	cadence=0
+	last_elev=min_alt
+	ges_elev=0.0
+
+	for record in file.get_messages("record"):
+		## korrekte Kadenz ermitteln
+		if record.get_value("cadence") != None and record.get_value("cadence") > 0:
+			i+=1
+			cadence += record.get_value("cadence")
+
+		## korrekte Höhenmeter ermitteln
+		if record.get_value("altitude") != None:
+			if record.get_value("altitude") > last_elev:
+				ges_elev += record.get_value("altitude") - last_elev
+
+			last_elev = record.get_value("altitude")
+
+	#fitData["avg_cadence"] = int(cadence/i)
+	fitData["total_ascent"] += round(float(ges_elev), 1)
+	fitData["total_ascent"] = round(float(fitData["total_ascent"]), 1)
+
+
+def showEnd():
+	print("Akku geladen",loading)
+	print("Ladung Wh",watt)
+	print("Total Range", fitData["total_distance"])
+	print("Total Ascent", fitData["total_ascent"])
+	print("AVG Speed", fitData["avg_speed"])
+
+
+def readCSV():
+	global fitData, fitFilesCount
+
+	with open(csv_file, "r", encoding="utf-8", errors="ignore") as scraped:
+		row = []
+		try:
+			row.extend(scraped.readlines()[-1].replace("\n", "").split(","))
+			row = row[0:-5]
+			dropCSV()
+			row.extend([fitData["total_distance"], fitData["total_ascent"], fitData["avg_speed"], watt, fitFilesCount])
+			writeCSV(row)
+
+		except:
+			print("EXCEPTION")
+			row.extend(["Date", "Loading", "Total Range", "Total Ascent", "AVG Speed", "Loaded Wh", "Fahrten"])
+			writeCSV(row)
+
+
+def writeCSV(row):
+	with open(csv_file, 'a', newline='') as csvfile:
+		spamwriter = csv.writer(csvfile, delimiter=',', quotechar='|', quoting=csv.QUOTE_MINIMAL)
+		spamwriter.writerow(row)
+		spamwriter.writerow([datetime.today().strftime('%d-%m-%Y'), loading, 0, 0, 0, 0, 0])
+
+
+def dropCSV():
+	df = pd.read_csv(csv_file, index_col='Date', on_bad_lines='skip')
+	df = df.iloc[:-1]
+	df.to_csv(csv_file, index=True)
+
+
+def deleteFiles():
+	for filename in os.listdir(path):
+		file_path = os.path.join(path, filename)
+		try:
+			if os.path.isfile(file_path) or os.path.islink(file_path):
+				os.unlink(file_path)
+				#print("lösche... test")
+			elif os.path.isdir(file_path):
+				shutil.rmtree(file_path)
+		except Exception as e:
+			print('Failed to delete %s. Reason: %s' % (file_path, e))
+
+
+if __name__ == '__main__':
+	main()
+
diff --git a/parseFitFiles.py b/parseFitFiles.py
new file mode 100644
index 0000000..e604891
--- /dev/null
+++ b/parseFitFiles.py
@@ -0,0 +1,121 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+
+from fitparse import FitFile
+from datetime import datetime
+import inotify.adapters
+import os, sys, shutil
+import sqlite3
+
+
+path = "/home/manuel/Dropbox/Apps/HealthFitExporter/"
+#path = "/Users/manuel/Projekte/eBike-Influx-Calculation/test/"
+ext = ('.fit')
+db_file = "/www/wwwroot/www.manuelw.de/ebike/ebike.db"
+#db_file = "/Users/manuel/Projekte/eBike-Influx-Calculation/ebike.db"
+
+fitData = {}
+
+##
+### auf neue Files überwachen
+def main():
+	i = inotify.adapters.InotifyTree(path)
+	
+	for event in i.event_gen(yield_nones=False):
+		(_, type_names, fpath, filename) = event
+		
+		if event != None:
+			print("PATH=[{}] FILENAME=[{}] EVENT_TYPES={}".format(path, filename, type_names))
+			if type_names[0] == "IN_MOVED_TO":
+				if filename.endswith(ext):
+					processFIT(filename)
+				
+		pass
+	
+##
+### parse FIT file und bestimme Handling
+def processFIT(filename):
+	file = FitFile(path+filename)
+	for record in file.get_messages("session"):
+		## nur wenn Radfahren
+		if record.get_value("sport") == "cycling":
+			## Verarbeitung starten
+			doAll(file)
+
+	writeDB()
+
+
+##
+### todo on all fit files
+def doAll(file):
+	global fitData
+	
+	for record in file.get_messages("session"):
+		fitData["avg_cadence"] = record.get_value("avg_cadence")
+		#fitData["avg_speed"] += round(float(record.get_value("avg_speed")) / 0.27777777777778, 1)
+		fitData["avg_speed"] = record.get_value("avg_speed")
+		#fitData["total_ascent"] = float(record.get_value("total_ascent")) if record.get_value("total_ascent") != None else 0
+		fitData["total_distance"] = round(record.get_value("total_distance") / 1000, 1)
+		fitData["date"] = record.get_value("start_time").strftime("%d.%m.%Y")
+		fitData["total_calories"] = record.get_value("total_calories")
+		if record.get_value("max_altitude"):
+			fitData["max_altitude"] = round(float(record.get_value("max_altitude")), 1)
+		else:
+			fitData["max_altitude"] = 0
+		fitData["max_heart_rate"] = record.get_value("max_heart_rate")
+		fitData["avg_heart_rate"] = record.get_value("avg_heart_rate")
+
+	fitData["avg_speed"] = round(float(fitData["avg_speed"]), 1)
+	doHealthFit(file, record.get_value("min_altitude"))
+
+
+def doHealthFit(file, min_alt=0.0):
+	global fitData
+	
+	### Kadenz
+	i=0
+	cadence=0
+	last_elev=min_alt
+	ges_elev=0.0
+
+	for record in file.get_messages("record"):
+		## korrekte Kadenz ermitteln
+		if record.get_value("cadence") != None and record.get_value("cadence") > 0:
+			i+=1
+			cadence += record.get_value("cadence")
+
+		## korrekte Höhenmeter ermitteln
+		if record.get_value("altitude") != None:
+			if record.get_value("altitude") > last_elev:
+				ges_elev += record.get_value("altitude") - last_elev
+
+			last_elev = record.get_value("altitude")
+
+	#fitData["avg_cadence"] = int(cadence/i)
+	fitData["total_ascent"] = round(float(ges_elev), 1)
+
+
+def writeDB():
+	global fitData, db_file
+	
+	db = sqlite3.connect(db_file)
+	cursor = db.cursor()
+	
+	data = [fitData["date"], fitData["total_distance"], fitData["total_ascent"], fitData["max_altitude"], fitData["avg_speed"], 
+		fitData["avg_heart_rate"], fitData["total_calories"]]
+	
+	cursor.executemany('INSERT INTO bike_drives VALUES (?)', data)
+	db.commit()
+	db.close()
+	
+	
+	#row.extend(["Date", "Total Distance", "Total Ascent", "Max Alt.", "AVG Speed", "AVG HR", "Calories"])
+	#row.extend([fitData["date"], fitData["total_distance"], fitData["total_ascent"], fitData["max_altitude"], fitData["avg_speed"], 
+	#	fitData["avg_heart_rate"], fitData["total_calories"] ])
+
+
+
+if __name__ == '__main__':
+	main()
+
+	
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