from rest_framework import status, response
from rest_framework.test import APITestCase
from lots.models import Lot
class LotsTestCase(APITestCase):
def setUp(self) -> None:
self.lot = Lot.objects.create(name="1",
address="Dont Know",
phone_num="010-4451-2211",
latitude=127.12,
longitude=352.123,
basic_rate=20000,
additional_rate=2000,
partnership=False,
section_count=3,)
def test_delete(self):
response = self.client.delete(f'api/lots/{self.lot["name"]}')
# response = self.client.delete(f'/api/users/{self.users[0].pk}')
# url = reverse(f'/api/lots/{self.lot}', kwargs={'pk': self.lot.pk})
# self.client.delete(url)
self.assertEqual(response.status_code, status.HTTP_204_NO_CONTENT)
self.assertEqual(self.lot.objects.filter(pk=self.lot.pk.count()))
I have problems with the test code above. Why doesn't it work? I know it has to do with calling dictionary values but I just can't figure it out. Thanks for your help.
Lot.objects.create(...) returns a Lot type so you access name by self.lot.name.
Related
I'm struggling to correctly set Vertex AI pipeline which does the following:
read data from API and store to GCS and as as input for batch prediction.
get an existing model (Video classification on Vertex AI)
create Batch prediction job with input from point 1.
As it will be seen, I don't have much experience with Vertex Pipelines/Kubeflow thus I'm asking for help/advice, hope it's just some beginner mistake.
this is the gist of the code I'm using as pipeline
from google_cloud_pipeline_components import aiplatform as gcc_aip
from kfp.v2 import dsl
from kfp.v2.dsl import component
from kfp.v2.dsl import (
Output,
Artifact,
Model,
)
PROJECT_ID = 'my-gcp-project'
BUCKET_NAME = "mybucket"
PIPELINE_ROOT = "{}/pipeline_root".format(BUCKET_NAME)
#component
def get_input_data() -> str:
# getting data from API, save to Cloud Storage
# return GS URI
gcs_batch_input_path = 'gs://somebucket/file'
return gcs_batch_input_path
#component(
base_image="python:3.9",
packages_to_install=['google-cloud-aiplatform==1.8.0']
)
def load_ml_model(project_id: str, model: Output[Artifact]):
"""Load existing Vertex model"""
import google.cloud.aiplatform as aip
model_id = '1234'
model = aip.Model(model_name=model_id, project=project_id, location='us-central1')
#dsl.pipeline(
name="batch-pipeline", pipeline_root=PIPELINE_ROOT,
)
def pipeline(gcp_project: str):
input_data = get_input_data()
ml_model = load_ml_model(gcp_project)
gcc_aip.ModelBatchPredictOp(
project=PROJECT_ID,
job_display_name=f'test-prediction',
model=ml_model.output,
gcs_source_uris=[input_data.output], # this doesn't work
# gcs_source_uris=['gs://mybucket/output/'], # hardcoded gs uri works
gcs_destination_output_uri_prefix=f'gs://{PIPELINE_ROOT}/prediction_output/'
)
if __name__ == '__main__':
from kfp.v2 import compiler
import google.cloud.aiplatform as aip
pipeline_export_filepath = 'test-pipeline.json'
compiler.Compiler().compile(pipeline_func=pipeline,
package_path=pipeline_export_filepath)
# pipeline_params = {
# 'gcp_project': PROJECT_ID,
# }
# job = aip.PipelineJob(
# display_name='test-pipeline',
# template_path=pipeline_export_filepath,
# pipeline_root=f'gs://{PIPELINE_ROOT}',
# project=PROJECT_ID,
# parameter_values=pipeline_params,
# )
# job.run()
When running the pipeline it throws this exception when running Batch prediction:
details = "List of found errors: 1.Field: batch_prediction_job.model; Message: Invalid Model resource name.
so I'm not sure what could be wrong. I tried to load model in the notebook (outside of component) and it correctly returns.
Second issue I'm having is referencing GCS URI as output from component to batch job input.
input_data = get_input_data2()
gcc_aip.ModelBatchPredictOp(
project=PROJECT_ID,
job_display_name=f'test-prediction',
model=ml_model.output,
gcs_source_uris=[input_data.output], # this doesn't work
# gcs_source_uris=['gs://mybucket/output/'], # hardcoded gs uri works
gcs_destination_output_uri_prefix=f'gs://{PIPELINE_ROOT}/prediction_output/'
)
During compilation, I get following exception TypeError: Object of type PipelineParam is not JSON serializable, though I think this could be issue of ModelBatchPredictOp component.
Again any help/advice appreciated, I'm dealing with this from yesterday, so maybe I missed something obvious.
libraries I'm using:
google-cloud-aiplatform==1.8.0
google-cloud-pipeline-components==0.2.0
kfp==1.8.10
kfp-pipeline-spec==0.1.13
kfp-server-api==1.7.1
UPDATE
After comments, some research and tuning, for referencing model this works:
#component
def load_ml_model(project_id: str, model: Output[Artifact]):
region = 'us-central1'
model_id = '1234'
model_uid = f'projects/{project_id}/locations/{region}/models/{model_id}'
model.uri = model_uid
model.metadata['resourceName'] = model_uid
and then I can use it as intended:
batch_predict_op = gcc_aip.ModelBatchPredictOp(
project=gcp_project,
job_display_name=f'batch-prediction-test',
model=ml_model.outputs['model'],
gcs_source_uris=[input_batch_gcs_path],
gcs_destination_output_uri_prefix=f'gs://{BUCKET_NAME}/prediction_output/test'
)
UPDATE 2
regarding GCS path, a workaround is to define path outside of the component and pass it as an input parameter, for example (abbreviated):
#dsl.pipeline(
name="my-pipeline",
pipeline_root=PIPELINE_ROOT,
)
def pipeline(
gcp_project: str,
region: str,
bucket: str
):
ts = datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
gcs_prediction_input_path = f'gs://{BUCKET_NAME}/prediction_input/video_batch_prediction_input_{ts}.jsonl'
batch_input_data_op = get_input_data(gcs_prediction_input_path) # this loads input data to GCS path
batch_predict_op = gcc_aip.ModelBatchPredictOp(
project=gcp_project,
model=training_job_run_op.outputs["model"],
job_display_name='batch-prediction',
# gcs_source_uris=[batch_input_data_op.output],
gcs_source_uris=[gcs_prediction_input_path],
gcs_destination_output_uri_prefix=f'gs://{BUCKET_NAME}/prediction_output/',
).after(batch_input_data_op) # we need to add 'after' so it runs after input data is prepared since get_input_data doesn't returns anything
still not sure, why it doesn't work/compile when I return GCS path from get_input_data component
I'm glad you solved most of your main issues and found a workaround for model declaration.
For your input.output observation on gcs_source_uris, the reason behind it is because the way the function/class returns the value. If you dig inside the class/methods of google_cloud_pipeline_components you will find that it implements a structure that will allow you to use .outputs from the returned value of the function called.
If you go to the implementation of one of the components of the pipeline you will find that it returns an output array from convert_method_to_component function. So, in order to have that implemented in your custom class/function your function should return a value which can be called as an attribute. Below is a basic implementation of it.
class CustomClass():
def __init__(self):
self.return_val = {'path':'custompath','desc':'a desc'}
#property
def output(self):
return self.return_val
hello = CustomClass()
print(hello.output['path'])
If you want to dig more about it you can go to the following pages:
convert_method_to_component, which is the implementation of convert_method_to_component
Properties, basics of property in python.
I'm trying to read contacts from my person gmail account and the instructions provided by Google from the People API is returning an empty list. I'm not sure why. I've tried another solution from a few years ago, but that doens't seem to work. I've pasted my code below. Any help troubleshooting this is appreciated!
import os.path
from googleapiclient.discovery import build
from google_auth_oauthlib.flow import InstalledAppFlow
from google.auth.transport.requests import Request
from google.oauth2.credentials import Credentials
# If modifying these scopes, delete the file token.json.
SCOPES = ['https://www.googleapis.com/auth/contacts.readonly']
from google.oauth2 import service_account
SERVICE_ACCOUNT_FILE = '<path name hidden>.json'
credentials = service_account.Credentials.from_service_account_file(
SERVICE_ACCOUNT_FILE, scopes=SCOPES)
def main():
#Shows basic usage of the People API.
#Prints the name of the first 10 connections.
creds = None
service = build('people', 'v1', credentials=credentials)
# Call the People API
print('List 10 connection names')
results = service.people().connections().list(
resourceName='people/me',
pageSize=10,
personFields='names,emailAddresses').execute()
connections = results.get('connections', [])
request = service.people().searchContacts(pageSize=10, query="A", readMask="names")
results = service.people().connections().list(resourceName='people/me',personFields='names,emailAddresses',fields='connections,totalItems,nextSyncToken').execute()
for i in results:
print ('result', i)
for person in connections:
names = person.get('names', [])
if names:
name = names[0].get('displayName')
print(name)
if __name__ == '__main__':
main()
I am trying to extract the content of the [Documentation] section as a string for comparision with other part in a Python script.
I was told to use Robot framework API https://robot-framework.readthedocs.io/en/stable/
to extract but I have no idea how.
However, I am required to work with version 3.1.2
Example:
*** Test Cases ***
ATC Verify that Sensor Battery can enable and disable manufacturing mode
[Documentation] E1: This is the description of the test 1
... E2: This is the description of the test 2
[Tags] E1 TRACE{Trace_of_E1}
... E2 TRACE{Trace_of_E2}
Extract the string as
E1: This is the description of the test 1
E2: This is the description of the test 2
Have a look at these examples. I did something similar to generate testplans descritio. I tried to adapt my code to your requirements and this could maybe work for you.
import os
import re
from robot.api.parsing import (
get_model, get_tokens, Documentation, EmptyLine, KeywordCall,
ModelVisitor, Token
)
class RobotParser(ModelVisitor):
def __init__(self):
# Create object with remarkup_text to store formated documentation
self.text = ''
def get_text(self):
return self.text
def visit_TestCase(self, node):
# The matched `TestCase` node is a block with `header` and
# `body` attributes. `header` is a statement with familiar
# `get_token` and `get_value` methods for getting certain
# tokens or their value.
for keyword in node.body:
# skip empty lines
if keyword.get_value(Token.DOCUMENTATION) == None:
continue
self.text += keyword.get_value(Token.ARGUMENT)
def visit_Documentation(self,node):
# The matched "Documentation" node with value
self.remarkup_text += node.value + self.new_line
def visit_File(self, node):
# Call `generic_visit` to visit also child nodes.
return self.generic_visit(node)
if __name__ == "__main__":
path = "../tests"
for filename in os.listdir(path):
if re.match(".*\.robot", filename):
model = get_model(os.path.join(path, filename))
robot_parser = RobotParser()
robot_parser.visit(model)
text=robot_parser._text()
The code marked as best answer didn't quite work for me and has a lot of redundancy but it inspired me enough to get into the parsing and write it in a much readable and efficient way that actually works as is. You just have to have your own way of generating & iterating through filesystem where you call the get_robot_metadata(filepath) function.
from robot.api.parsing import (get_model, ModelVisitor, Token)
class RobotParser(ModelVisitor):
def __init__(self):
self.testcases = {}
def visit_TestCase(self, node):
testcasename = (node.header.name)
self.testcases[testcasename] = {}
for section in node.body:
if section.get_value(Token.DOCUMENTATION) != None:
documentation = section.value
self.testcases[testcasename]['Documentation'] = documentation
elif section.get_value(Token.TAGS) != None:
tags = section.values
self.testcases[testcasename]['Tags'] = tags
def get_testcases(self):
return self.testcases
def get_robot_metadata(filepath):
if filepath.endswith('.robot'):
robot_parser = RobotParser()
model = get_model(filepath)
robot_parser.visit(model)
metadata = robot_parser.get_testcases()
return metadata
This function will be able to extract the [Documentation] section from the testcase:
def documentation_extractor(testcase):
documentation = []
for setting in testcase.settings:
if len(setting) > 2 and setting[1].lower() == "[documentation]":
for doc in setting[2:]:
if doc.startswith("#"):
# the start of a comment, so skip rest of the line
break
documentation.append(doc)
break
return "\n".join(documentation)
I want to get a channel's members' count but I don't know which method should I use?
I am not admin in that channel, I just want to get the count number.
EDIT:I am using main telegram api, not telegram Bot api
You can use getChatMembersCount method.
Use this method to get the number of members in a chat.
It worked for me :)
from telethon import TelegramClient, sync
from telethon.tl.functions.channels import GetFullChannelRequest
api_id = API ID
api_hash = 'API HASH'
client = TelegramClient('session_name', api_id, api_hash)
client.start()
if (client.is_user_authorized() == False):
phone_number = 'PHONE NUMBER'
client.send_code_request(phone_number)
myself = client.sign_in(phone_number, input('Enter code: '))
channel = client.get_entity('CHANNEL LINK')
members = client.get_participants(channel)
print(len(members))
It is possible to do it also through GetFullChannelRequest in telethon
async def main():
async with client_to_manage as client:
full_info = await client(GetFullChannelRequest(channel="moscowproc"))
print(f"count: {full_info.full_chat.participants_count}")
if __name__ == '__main__':
client_to_manage.loop.run_until_complete(main())
or to write it without async/await
def main():
with client_to_manage as client:
full_info = client.loop.run_until_complete(client(GetFullChannelRequest(channel="moscowproc")))
print(f"count: {full_info.full_chat.participants_count}")
if __name__ == '__main__':
main()
Also as above was said, it is also feasible by bot-api with
getChatMembersCount method. You can curl it or use python to query needed url
with python code can look like this one:
import json
from urllib.request import urlopen
url ="https://api.telegram.org/bot<your-bot-api-token>/getChatMembersCount?chat_id=#<channel-name>"
with urlopen(url) as f:
resp = json.load(f)
print(resp['result'])
where <your-bot-api-token> is token provided by BotFather, and <channel-name> is channel name which amount of subscribers you want to know (of course, everything without "<>")
to check firstly, simply curl it:
curl https://api.telegram.org/bot<your-bot-api-token>/getChatMembersCount?chat_id=#<channel-name>
I am using Google Custom Search to index content on my website.
When I use a REST client to make the get request at
https://www.googleapis.com/customsearch/v1?key=xxx&q=query&cx=xx
I get response in sub seconds.
But when I try to make the call using my code, it takes up six seconds. What am I doing wrong ?
__author__ = 'xxxx'
import urllib2
import logging
import gzip
from cfc.apikey.googleapi import get_api_key
from cfc.url.processor import set_query_parameter
from StringIO import StringIO
CX = 'xxx:xxx'
URL = "https://www.googleapis.com/customsearch/v1?key=%s&cx=%s&q=sd&fields=kind,items(title)" % (get_api_key(), CX)
def get_results(query):
url = set_query_parameter(URL, 'q', query)
request = urllib2.Request(url)
request.add_header('Accept-encoding', 'gzip')
request.add_header('User-Agent','cfc xxxx (gzip)')
response = urllib2.urlopen(request)
if response.info().get('Content-Encoding') == 'gzip':
buf = StringIO(response.read())
f = gzip.GzipFile(fileobj=buf)
data = f.read()
return data
I have implemented performance tips mentioned in Performance Tips. I would appreciate any help. Thanks.