import pandas as pd
from API_call_method import callAPI
import traceback
df = pd.read_csv('mongo_data.csv')
df1 = pd.DataFrame(
columns=['ObjectId', 'Display_learnpath_name', 'Learnpath_name', 'Do_learnpaths_match', 'Question_code'])
i = 0
for ind in df.index:
object_id = df['_id'][ind]
#print(object_id)
url = f"/learning_objects/{object_id}?embed=true&next_status=true"
response = callAPI('GET', 'https://knowledge-blue.embibe.com', url,
'a3e65d48-1c51-4191-96fb-1ee2d07d47da:7e02ba0f3c16a3f5231c51a42ffa76b84cf18480f74a02ca4c43ceb85b6ea8ba1f27564951cc70d49033d96334cdde23674939f05175339680ce3baabcb51ffd',
'{}')
# print(response.json())
try:
content = response.json()['content']
try:
question_code = response.json()['question_code']
except:
question_code = 'question code key not available'
for item in content:
try:
for item in content['question_meta_tags']:
try:
for item in item['learning_maps_data']:
try:
display_learnpath_name = item['display_learnpath_name']
except:
display_learnpath_name = 'display_learnpath_name key not present'
def main(self, child_id, board, grade, exam, goal, embibe_token):
payload = {
"board": goal,
"child_id": child_id,
"exam": exam,
"exam_name": exam,
"goal": goal,
"grade": grade,
"fetch_all_content": True
}
response1 = callAPI('POST', 'https://preprodms.embibe.com',
'/fiber_ms/v1/home/practise', embibe_token,
json.dumps(payload))
# response1 = self.callAPI(
# f"/fiber_ms/v1/home",
# json.dumps(payload),
# 'POST', embibe_token)
# df_positive_results_all_subjects = pd.read_csv("positive_learn_results_all_subjects.csv")
# df_negative_results_all_subjects = pd.read_csv("negative_learn_results_all_subjects.csv")
df_positive_results = pd.read_csv("positive_learn_results.csv")
df_negative_results = pd.read_csv("negative_learn_results.csv")
try:
for item in response1.json():
home_data = [child_id, exam, goal, grade]
if item["content_section_type"] == "SUBJECTS":
for data in item["content"]:
if data["subject"] == "All Subjects":
continue
else:
try:
subject_data_extractor(
child_id, board, grade, exam, goal,
embibe_token, data["subject"], home_data,
df_negative_results, df_positive_results)
except Exception as e:
print(e)
if (item["contentType"] == "chapter"):
try:
section_name = item["section_name"]
except:
section_name = None
for data in item["content"]:
try:
title = data["title"]
except:
title = None
try:
description = data["description"]
except:
description = None
try:
format_refrence = data["learnmap_id"]
except:
format_refrence = None
a_string = format_refrence
split_string = a_string.split("/", 1)
format_refrence = split_string[0]
try:
Type = data["type"]
except:
Type = None
try:
subject_tagged = data["subject"]
except:
subject_tagged = None
try:
learnpath_name = data["learnpath_name"]
except:
learnpath_name = None
LIST = []
LIST = learnpath_name.split('--')
chapter = (LIST[len(LIST) - 1])
learnmap_id = data["learnmap_id"]
df_positive_results.loc[len(
df_positive_results)] = home_data + [
title, Type, format_refrence, section_name,
"All Subjects", subject_tagged, learnpath_name,
learnmap_id, chapter
]
df_positive_results.to_csv(
"positive_learn_results.csv", index=False)
except:
df_positive_results.loc[len(df_positive_results)] = home_data + [
response1.json(), '', '', '', "All Subjects", '', '', '', ''
]
df_positive_results.to_csv("positive_learn_results.csv",
index=False)
grade = df['Grade'][ind]
print(goal, "--", exam)
payload = {
"board": goal,
"child_id": dictionary['user_id'],
"exam": exam,
"exam_name": exam,
"goal": goal,
"grade": grade,
"fetch_all_content": True
}
# embibe_token = 'eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzUxMiJ9.eyJjb3VudHJ5IjoxLCJ1c2VyX3R5cGUiOjEsInBhcmVudF91c2VyX2lkIjoxNTAwMDA2MzQ0LCJjcmVhdGVkIjoxNjEyMjcwNTI0LCJvcmdhbml6YXRpb25faWQiOiIxIiwiaWQiOjE1MDAwMDYzNDUsImV4cCI6MTYxMzQ4MDEyNCwiZGV2aWNlSWQiOiIxNjExODU2NDMwMDY4IiwibW9iaWxlX3ZlcmlmaWNhdGlvbl9zdGF0dXMiOmZhbHNlLCJlbWFpbF92ZXJpZmljYXRpb25fc3RhdHVzIjpmYWxzZSwiZW1haWwiOiIxNTAwMDA2MzQ0XzE1Mjg5OTE3OTAwMzcxNDA0QGVtYmliZS11c2VyLmNvbSJ9.zaIn_Q18tJVGpoOxjPYQs1td1HRq_wamuhLOaZ0LxbwS5gSQGqkcKPaWCmZ-Drsm4a_p_-BpVH6_Vt_JSJe-eA'
embibe_token = dictionary['embibe-token']
try:
response = callAPI('POST', host, '/fiber_ms/v1/home/', embibe_token,
json.dumps(payload))
for item in response.json():
if item['contentType'] == 'Video':
section_name = item['section_name']
for item in item['content']:
try:
title = item['title']
except:
title = None
try:
description = item['description']
except:
description = None
try:
duration = item['length']
except:
def subject_data(child_id, board, grade, exam, goal, embibe_token, subject, df,
host):
payload = {
"board": goal,
"child_id": child_id,
"exam": exam,
"exam_name": exam,
"goal": goal,
"grade": grade,
"onlyPractise": False,
"fetch_all_content": True
}
response1 = callAPI('POST', host, f"/fiber_ms/v1/home/{subject}",
embibe_token, json.dumps(payload))
# url = f"/fiber_ms/v2/home/learn/{subject}?exam={exam}&goal={goal}&fetch_all_content=true"
# response1 = callAPI('GET', host, url, embibe_token, '{}')
try:
for item in response1.json():
if item["contentType"] == "Book":
section = item['section_name']
for item in item['content']:
try:
teaser_url = item['teaser_url']
except:
teaser_url = "KEY NOT AVAILABLE"
try:
title = item['title']
except:
title = 'KEY NOT FOUND'
if teaser_url == '' or teaser_url == 'KEY NOT AVAILABLE':
teaser_url_working = 'teaser url not present'
else:
url = teaser_url
response33 = callAPI2(url, '{}', 'GET', '')
try:
if response33.status_code == 200:
teaser_url_working = 'Yes'
else:
teaser_url_working = 'No'
except:
teaser_url_working = response33.text
df.loc[len(df)] = [
f'Learn/home{subject}', goal, exam, title, section,
teaser_url, teaser_url_working
]
for item in response1.json():
if item["contentType"] == "Video":
section = item['section_name']
for item in item['content']:
try:
preview_url = item['preview_url']
except:
preview_url = "KEY NOT AVAILABLE"
try:
title = item['title']
except:
title = 'KEY NOT FOUND'
if preview_url == '' or preview_url == 'KEY NOT AVAILABLE':
preview_url_working = 'preview url not present'
else:
url = preview_url
response33 = callAPI2(url, '{}', 'GET', '')
try:
if response33.status_code == 200:
preview_url_working = 'Yes'
else:
preview_url_working = 'No'
except:
preview_url_working = response33.text
df.loc[len(df)] = [
f'Learn/home{subject}', goal, exam, title, section,
preview_url, preview_url_working
]
except Exception as e:
print(e)
df.loc[len(df)] = [f'Learn/home/{subject}', goal, exam, '', '', '', '']
df.to_csv('teaser_urls_working_learn.csv', index=False)
def main(self, child_id, board, grade, exam, goal, embibe_token, subject,
home_data, df_negative_results, df_positive_results):
payload = {
"board": goal,
"child_id": child_id,
"exam": exam,
"exam_name": exam,
"goal": goal,
"grade": grade,
"onlyPractise": "true",
"fetch_all_content": "true"
}
response1 = callAPI('POST', 'https://preprodms.embibe.com',
f"/fiber_ms/v1/home/{subject}", embibe_token,
json.dumps(payload))
# response1 = self.callAPI(
# f"/fiber_ms/v1/home/{subject}",
# json.dumps(payload),
# 'POST', embibe_token)
try:
for item in response1.json():
if item["contentType"] == "chapter":
try:
section_name = item["section_name"]
except:
section_name = None
for data in item["content"]:
try:
title = data["title"]
except:
title = None
try:
description = data["description"]
except:
description = None
try:
format_refrence = data["learnmap_id"]
except:
format_refrence = None
a_string = format_refrence
split_string = a_string.split("/", 1)
format_refrence = split_string[0]
try:
Type = data["type"]
except:
Type = None
try:
subject_tagged = data["subject"]
except:
subject_tagged = None
try:
learnpath_name = data["learnpath_name"]
except:
learnpath_name = None
LIST = []
LIST = learnpath_name.split('--')
chapter = (LIST[len(LIST) - 1])
learnmap_id = data["learnmap_id"]
df_positive_results.loc[len(
df_positive_results)] = home_data + [
title, Type, format_refrence, section_name,
subject, subject_tagged, learnpath_name,
learnmap_id, chapter
]
df_positive_results.to_csv(
"positive_learn_results.csv", index=False)
except:
df_positive_results.loc[len(df_positive_results)] = home_data + [
response1.json(), '', '', '', subject, '', '', '', ''
]
df_positive_results.to_csv("positive_learn_results.csv",
index=False)
def home_data(child_id, board, grade, exam, goal, embibe_token, host):
df = pd.read_csv('complete_video_details.csv')
payload = {
"board": goal,
"child_id": child_id,
"exam": exam,
"exam_name": exam,
"goal": goal,
"grade": grade,
"fetch_all_content": True
}
response1 = callAPI('POST', host, '/fiber_ms/v1/home', embibe_token,
json.dumps(payload))
# print(response1.json())
# url = f"/fiber_ms/v2/home/learn/?exam={exam}&goal={goal}&fetch_all_content=true"
# response1 = callAPI('GET', host, url, embibe_token, '{}')
try:
for item in response1.json():
if item["contentType"] == "Video":
section = item['section_name']
for item in item['content']:
try:
title = item['title']
except:
title = 'KEY NOT AVAILABLE'
try:
description = item['description']
except:
description = 'KEY NOT AVAILABLE'
try:
tags = item['learning_map']['tags']
except:
tags = 'KEY NOT AVAILABLE'
try:
subject_tagged = item['subject']
except:
subject_tagged = 'KEY NOT AVAILABLE'
try:
duration = item['length']
except:
duration = 'KEY NOT AVAILABLE'
try:
preview_url = item['preview_url']
except:
preview_url = "KEY NOT AVAILABLE"
try:
thumb = item['thumb']
except:
thumb = 'KEY NOT AVAILABLE'
try:
embium = item['currency']
except:
embium = 'KEY NOT AVAILABLE'
try:
topic_learnpath_name = item['learning_map'][
'topic_learnpath_name']
except:
topic_learnpath_name = 'KEY NOT AVAILABLE'
try:
id = item['id']
except:
id = 'KEY NOT AVAILABLE'
try:
format_id = item['learning_map']['format_id']
except:
format_id = 'KEY NOT AVAILABLE'
# CHECKS#_________________
if title == '' or title == 'KEY NOT AVAILABLE':
title_present = 'No'
else:
title_present = 'Yes'
if description == '' or description == 'KEY NOT AVAILABLE':
description_present = 'No'
else:
description_present = 'Yes'
if tags == [] or tags == 'KEY NOT AVAILABLE':
tags_present = 'No'
else:
tags_present = 'Yes'
if subject_tagged == '' or subject_tagged == 'KEY NOT AVAILABLE':
subject_tag_present = 'No'
else:
subject_tag_present = 'Yes'
if duration == '' or duration == 'KEY NOT AVAILABLE' or duration == 0:
duration_present = 'No'
else:
duration_present = 'Yes'
if thumb == '' or thumb == 'KEY NOT AVAILABLE':
thumb_present = 'No'
else:
thumb_present = 'Yes'
if embium == '' or embium == 'KEY NOT AVAILABLE':
embium_present = 'No'
else:
embium_present = 'Yes'
if preview_url == '' or preview_url == 'KEY NOT AVAILABLE':
preview_url_working = 'No'
else:
url = preview_url
if '.webm' in url or '.webp' in url:
response33 = callAPI2(url, '{}', 'GET', '', '')
try:
if response33.status_code == 200:
preview_url_working = 'Yes'
else:
preview_url_working = 'No'
except:
preview_url_working = response33.text
else:
preview_url_working = 'No'
if topic_learnpath_name == '' or topic_learnpath_name == 'KEY NOT AVAILABLE':
More_on_this_topic_api = 'Topic learnpath not present in api response'
Related_videos_api = 'topic learnpath not present in api response'
elif id == '' or id == 'KEY NOT AVAILABLE':
More_on_this_topic_api = 'id not present in api response'
Related_videos_api = 'id not present in api response'
else:
url11 = "/fiber_ms/v1/topics/related/" + str(
topic_learnpath_name
) + "?&content_id=" + str(id) + "&goal=" + str(
goal) + "&exam_name=" + str(exam) + "&locale=en"
response11 = callAPI2(url11, '{}', 'GET', host,
embibe_token)
if response11.status_code == 200:
Related_videos_api = 'Yes'
else:
Related_videos_api = 'No'
if format_id == '' or format_id == 'KEY NOT AVAILABLE':
More_on_this_topic_api = 'format id is not present in api response'
else:
url22 = "/fiber_ms/topics/more/" + str(
topic_learnpath_name) + "?content_id=" + str(
id) + "&format_id=" + str(
format_id) + "&locale=en"
response22 = callAPI2(url22, '{}', 'GET', host,
embibe_token)
if response22.status_code == 200:
More_on_this_topic_api = 'Yes'
else:
More_on_this_topic_api = 'No'
df.loc[len(df)] = [
goal, exam, section, title_present,
description_present, tags_present, subject_tag_present,
duration_present, preview_url_working, embium_present,
thumb_present, More_on_this_topic_api,
Related_videos_api
]
print(df)
df.to_csv('complete_video_details.csv', index=False)
except Exception as e:
print(e)
df.loc[len(df)] = [
goal, exam, response1.status_code, response1.text, '', '', '', '',
'', '', '', '', ''
]
df.to_csv('complete_video_details.csv', index=False)