add article topic outlink

[mercelisvaughan]

added article outlink topic and updated the init file to include, this can make a request to the model and return a response with the following

client = ArticleTopicModel()
payload = {
    "page_title": "Douglas Adams",
    "lang": "en",
}
result = client.request(payload=payload)
print(result)

[kevinbazira]

LGTM!

[isaranto]

I have added the fields as optional using the Optional annotation so that it is more explicit

from liftwing import ArticleTopicModel

client = ArticleTopicModel()
payload = {
    "page_title": "Douglas Adams",
    "lang": "en",
    "debug": True,
}
result = client.request(payload=payload)

[kevinbazira]

I have added the fields as optional using the Optional annotation so that it is more explicit

looks like this change didn't take effect :)

>>> from liftwing import ArticleTopicModel
>>> 
>>> client = ArticleTopicModel()
>>> payload = {
...     "page_title": "Douglas Adams",
...     "lang": "en",
...     "debug": True,
... }
>>> result = client.request(payload=payload)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/home/kbazira/kevinbazira/sharedfolder/liftwing-python/ilias1/liftwing-python/liftwing/models/liftwing_model.py", line 31, in request
    _ = self.payload_model(**payload)
  File "/home/kbazira/kevinbazira/sharedfolder/liftwing-python/ilias1/.venv/lib/python3.10/site-packages/pydantic/main.py", line 192, in __init__
    self.__pydantic_validator__.validate_python(data, self_instance=self)
pydantic_core._pydantic_core.ValidationError: 2 validation errors for ArticleTopicPayload
threshold
  Field required [type=missing, input_value={'page_title': 'Douglas A...g': 'en', 'debug': True}, input_type=dict]
    For further information visit https://errors.pydantic.dev/2.8/v/missing
features_str
  Field required [type=missing, input_value={'page_title': 'Douglas A...g': 'en', 'debug': True}, input_type=dict]
    For further information visit https://errors.pydantic.dev/2.8/v/missing

[isaranto]

I have added the fields as optional using the Optional annotation so that it is more explicit

I actually hadn't pushed anything :( It required a force push and I missed it. Now it should be ok. Apologies!

[kevinbazira]

I actually hadn't pushed anything :( It required a force push and I missed it. Now it should be ok. Apologies!

Great! Now it works like a charm:

>>> from liftwing import ArticleTopicModel
>>> client = ArticleTopicModel()
>>> payload = {
...     "page_title": "Douglas Adams",
...     "lang": "en",
...     "debug": True,
... }
>>> result = client.request(payload=payload)
>>> print(result)
{'prediction': {'article': 'https://en.wikipedia.org/wiki/Douglas Adams', 'results': [{'topic': 'Culture.Media.Media*', 'score': 0.6859594583511353}, {'topic': 'Culture.Biography.Biography*', 'score': 0.5544804334640503}, {'topic': 'Culture.Literature', 'score': 0.5312193632125854}, {'topic': 'Culture.Media.Entertainment', 'score': 0.14415885508060455}, {'topic': 'Culture.Visual_arts.Visual_arts*', 'score': 0.11597072333097458}, {'topic': 'Culture.Visual_arts.Comics_and_Anime', 'score': 0.048867784440517426}, {'topic': 'STEM.STEM*', 'score': 0.04469086229801178}, {'topic': 'Geography.Regions.Americas.North_America', 'score': 0.04337579756975174}, {'topic': 'Geography.Regions.Europe.Europe*', 'score': 0.03623005375266075}, {'topic': 'Culture.Media.Television', 'score': 0.034110426902770996}, {'topic': 'Culture.Media.Books', 'score': 0.029322227463126183}, {'topic': 'Culture.Media.Films', 'score': 0.02596735954284668}, {'topic': 'Culture.Biography.Women', 'score': 0.02369946800172329}, {'topic': 'Geography.Regions.Europe.Northern_Europe', 'score': 0.022296184673905373}, {'topic': 'History_and_Society.Society', 'score': 0.020974241197109222}, {'topic': 'Culture.Internet_culture', 'score': 0.020974241197109222}, {'topic': 'History_and_Society.Business_and_economics', 'score': 0.01799621433019638}, {'topic': 'History_and_Society.Education', 'score': 0.014073628932237625}, {'topic': 'Culture.Philosophy_and_religion', 'score': 0.01206377986818552}, {'topic': 'STEM.Technology', 'score': 0.010996942408382893}, {'topic': 'Culture.Media.Music', 'score': 0.00942259095609188}, {'topic': 'History_and_Society.Politics_and_government', 'score': 0.009135636501014233}, {'topic': 'History_and_Society.History', 'score': 0.008071991614997387}, {'topic': 'STEM.Biology', 'score': 0.006913838442414999}, {'topic': 'Culture.Media.Video_games', 'score': 0.006913838442414999}, {'topic': 'Culture.Media.Radio', 'score': 0.006913838442414999}, {'topic': 'STEM.Computing', 'score': 0.005921069998294115}, {'topic': 'STEM.Earth_and_environment', 'score': 0.005564924795180559}, {'topic': 'Culture.Media.Software', 'score': 0.005230126902461052}, {'topic': 'Geography.Regions.Asia.Asia*', 'score': 0.004765199031680822}, {'topic': 'Culture.Visual_arts.Architecture', 'score': 0.004619571380317211}, {'topic': 'Geography.Regions.Africa.Africa*', 'score': 0.004341503139585257}, {'topic': 'Geography.Geographical', 'score': 0.0038344827480614185}, {'topic': 'Geography.Regions.Europe.Western_Europe', 'score': 0.003717252518981695}, {'topic': 'Culture.Sports', 'score': 0.0036036029923707247}, {'topic': 'Culture.Linguistics', 'score': 0.0034934247378259897}, {'topic': 'History_and_Society.Military_and_warfare', 'score': 0.0032830648124217987}, {'topic': 'Culture.Performing_arts', 'score': 0.0029910325538367033}, {'topic': 'STEM.Medicine_&_Health', 'score': 0.0027249858248978853}, {'topic': 'STEM.Libraries_&_Information', 'score': 0.002482622629031539}, {'topic': 'History_and_Society.Transportation', 'score': 0.00240673148073256}, {'topic': 'Culture.Food_and_drink', 'score': 0.0023331623524427414}, {'topic': 'Geography.Regions.Europe.Eastern_Europe', 'score': 0.0023331623524427414}, {'topic': 'STEM.Engineering', 'score': 0.0021927168127149343}, {'topic': 'Geography.Regions.Asia.West_Asia', 'score': 0.0021927168127149343}, {'topic': 'Geography.Regions.Oceania', 'score': 0.0021927168127149343}, {'topic': 'Geography.Regions.Europe.Southern_Europe', 'score': 0.0019977742340415716}, {'topic': 'Geography.Regions.Americas.South_America', 'score': 0.0019367348868399858}, {'topic': 'STEM.Physics', 'score': 0.0018775658681988716}, {'topic': 'Geography.Regions.Africa.Northern_Africa', 'score': 0.0017107223393395543}, {'topic': 'Geography.Regions.Asia.East_Asia', 'score': 0.0016584827098995447}, {'topic': 'Geography.Regions.Asia.South_Asia', 'score': 0.0016078451881185174}, {'topic': 'Geography.Regions.Americas.Central_America', 'score': 0.001465063076466322}, {'topic': 'STEM.Space', 'score': 0.0011795104946941137}, {'topic': 'STEM.Mathematics', 'score': 0.0011795104946941137}, {'topic': 'Geography.Regions.Asia.Southeast_Asia', 'score': 0.001108730910345912}, {'topic': 'Geography.Regions.Africa.Southern_Africa', 'score': 0.0010749627836048603}, {'topic': 'Geography.Regions.Africa.Western_Africa', 'score': 0.0008395892800763249}, {'topic': 'Culture.Visual_arts.Fashion', 'score': 0.0008140857680700719}, {'topic': 'STEM.Chemistry', 'score': 0.0006978511810302734}, {'topic': 'Geography.Regions.Asia.North_Asia', 'score': 0.0005984087474644184}, {'topic': 'Geography.Regions.Africa.Eastern_Africa', 'score': 0.0004542184469755739}, {'topic': 'Geography.Regions.Africa.Central_Africa', 'score': 1.0000003385357559e-05}, {'topic': 'Geography.Regions.Asia.Central_Asia', 'score': 1.0000003385357559e-05}]}}