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import json | |
from pathlib import Path | |
import gradio as gr | |
from transformers import ( | |
AutoTokenizer, | |
AutoModelForSequenceClassification, | |
TextClassificationPipeline, | |
) | |
labels = [ | |
# 'agency', | |
# 'humanComparison', | |
# 'hyperbole', | |
# 'historyComparison', | |
# 'unjustClaims', | |
# 'deepSounding', | |
# 'skeptics', | |
# 'deEmphasize', | |
# 'performanceNumber', | |
# 'inscrutable', | |
# 'objective' | |
"agency", | |
# "suggestiveImagery", | |
"comparisonWithHumanIntelligence", | |
"comparisonWithHumanSkills", | |
"hyperbole", | |
"uncriticalHistoryComparison", | |
"unjustifiedClaimsAboutFuture", | |
"falseClaimsAboutProgress", | |
"incorrectClaimsAboutStudyReport", | |
"deepSoundingTermsForBanalities", | |
"treatingSpokespeopleAsNeutral", | |
"repeatingPRTerms", | |
"noDiscussionOfLimitations", | |
"deEmphasizingLimitations", | |
"limitationsAddressedBySkeptics", | |
"downplayingHumanLabour", | |
"performanceNumbersWithoutCaveats", | |
# "inscrutability", | |
] | |
models = {} | |
pipes = {} | |
tokenizer = AutoTokenizer.from_pretrained("bert-base-cased") | |
for label in labels: | |
models[label] = AutoModelForSequenceClassification.from_pretrained( | |
f'xt0r3/aihype_{label}-vs-rest', | |
) | |
pipes[label] = TextClassificationPipeline( | |
model=models[label], tokenizer=tokenizer, top_k=None, | |
) | |
def predict(text): | |
preds = {} | |
for label in labels: | |
pred_array = pipes[label](text)[0] | |
for pred in pred_array: | |
if pred['label'] == 'LABEL_1': | |
preds[label] = pred['score'] | |
return preds | |
examples = [ | |
"Machine Learning is at the forefront of education, replacing human jobs", | |
"Machine Learning is at the forefront of education, but it isn't replacing human jobs just yet", | |
"AI model leaves scientists confused", | |
"Tech company unveils radical new assistant that will 'do everything for you'", | |
] | |
intf = gr.Interface(fn=predict, inputs="textbox", | |
outputs="label", examples=examples) | |
intf.launch(inline=False) | |