#!/usr/bin/env python3
import json
import csv
import boto3


# def lambda_handler(event, context):
#     s3_info = event['Records'].pop(0)['s3']
#     s3 = boto3.resource('s3')

#     bucket_name = s3_info['bucket']['name']
#     key_name = s3_info['object']['key']

#     # obj = s3.Object(bucket_name, key_name)



#     s3.meta.client.download_file(bucket_name, key_name, '/tmp/hello.csv')

#     with open('/tmp/hello.csv', newline='') as csvfile:
#         spamreader = csv.reader(csvfile, delimiter=',', quotechar='|')
#         for row in spamreader:
#             print('???'.join(row))
#             print("////////////////////////////////////////////////////")

#     return {
#         'statusCode': 200,
#         'body': json.dumps('Hello from Lambda!')
#     }

score_board = {'Strongly Agree':5, 'Agree':4, 'Neutral':3, 'Disagree':2, 'Strongly Disagree':1}
# dic_answers = {"lms":"12345", "trainer":"hoho", "answers":[]}
list_ilt_questions = ["QID2","QID1","QID31","QID67","QID32","QID36","QID38","QID58","QID59","QID60","QID61","QID62"]
eval_category = {"Overall satisfaction":["QID2","QID1","QID36"], "Instructor":["QID60","QID61","QID62"], "Classroom":["QID58","QID59"], "Contents":["QID31","QID67","QID32"]}


def fixed_ilt_questions_format():
    dic_score={}
    for qid in list_ilt_questions:
        dic_score[qid] = {"answers" : [], "average" : None}
    return dic_score

#'/Users/dayoungle/WorkDocs/class/evaluation/raw/181012_arch.csv'

def read_and_parse_csv(filename):
    dic_format = fixed_ilt_questions_format()
    with open(filename) as csvfile:
        csvreader = csv.reader(csvfile)
        headers = next(csvreader, None)
        header_txt = next(csvreader, None)
    #     # header_count = 0
    #     # answer_count = len(csvreader)

        for line in csvreader:
            for col in range(0, len(list_ilt_questions)):
                if line[col] != '':
                    dic_format[list_ilt_questions[col]]["answers"].append(line[col])
    return dic_format
# read_and_parse_csv('/Users/dayoungle/WorkDocs/class/evaluation/raw/181012_arch.csv', fixed_ilt_questions_format())
# print(dic_score)
#DDB insert??
#

def calculate_average_by_qId(dic_score):
    dic_average = {}
    for qId, val in dic_score.items():
        list_answer = val["answers"]
        answer_count = 0
        score_sum = 0

        if qId == "QID38":
            continue

        for answer in list_answer:
            if answer in score_board:
                score_sum = score_sum + score_board[answer]
                answer_count = answer_count + 1

        dic_average[qId] = round(score_sum/answer_count, 2)
    return dic_average
# print(dic_score)

def final_result(dic_score):
    final_result = {}
    for key in eval_category:
        sum = 0
        category_count = 0
        for qId in eval_category[key]:
            sum = sum + dic_score[qId]
            category_count = category_count + 1
        final_result[key] = round(sum/category_count,2)

    return final_result


def __main__():
    parsed = read_and_parse_csv('')
    # print(parsed)
    dic_average = calculate_average_by_qId(parsed)
    comment = parsed["QID38"]
    final = final_result(dic_average)
    final['Comment'] = comment
    print(final)