from io import StringIO
import sqlite3
import boto3
import pandas as pd
import os
from malware_detection_utils.utils import logMessage

LOGTYPE_ERROR = 'ERROR'
LOGTYPE_INFO = 'INFO'
LOGTYPE_DEBUG = 'DEBUG'

def main():

    aws_region=os.environ.get('AWS_REGION')
    bucket_name=os.environ.get('BUCKET_NAME', 'sorel-20m')
    object_name=os.environ.get('OBJECT_NAME','09-DEC-2020/processed-data/meta.db')
    file_name=os.environ.get('FILE_NAME','meta.db')
    metadatadb_bucket=os.environ.get('METADATADB_BUCKET')
    
    logMessage(f"AWS_REGION {aws_region}", LOGTYPE_INFO)
    logMessage(f"BUCKET_NAME {bucket_name}", LOGTYPE_INFO)
    logMessage(f"OBJECT_NAME {object_name}", LOGTYPE_INFO)
    logMessage(f"FILE_NAME {file_name}", LOGTYPE_INFO)
    logMessage(f"METADATADB_BUCKET {metadatadb_bucket}", LOGTYPE_INFO)
    try:
        # creating file path
        s3 = boto3.client('s3')
        s3.download_file(bucket_name, object_name, file_name)
        dbfile = 'meta.db'
        # Create a SQL connection to our SQLite database
        con = sqlite3.connect(dbfile)
        # creating cursor
        cur = con.cursor()
        # reading all table names
        table_list = cur.execute("SELECT * FROM meta")
        # Calling DataFrame constructor on list
        df = pd.DataFrame(table_list)
        # Be sure to close the connection
        con.close()

        s3 = boto3.resource('s3')

        csv_buffer = StringIO()
        df.to_csv(csv_buffer)
        s3.Object(metadatadb_bucket, f'malware.{file_name}').put(Body=csv_buffer.getvalue())
    except Exception as ex:
        logMessage("Error occurred" + str(ex), LOGTYPE_INFO)

print("Python file invoked")
if __name__ == '__main__':
   main()