app.py

import json
from pathlib import Path

import gradio as gr
from transformers import (
    AutoTokenizer,
    BertForSequenceClassification,
    TextClassificationPipeline,
)

labels = [
    'agency',
    # 'humanComparison',
    # 'hyperbole',
    # 'historyComparison',
    # 'unjustClaims',
    # 'deepSounding',
    # 'skeptics',
    # 'deEmphasize',
    # 'performanceNumber',
    # 'inscrutable',
    # 'objective'
]

models = {}

pipes = {}

tokenizer = AutoTokenizer.from_pretrained("bert-base-cased")

for label in labels:
    models[label] = BertForSequenceClassification.from_pretrained(
        f'xt0r3/aihype_{label}-vs-rest',
    )
    pipe = TextClassificationPipeline(
        model=models[label], tokenizer=tokenizer, return_all_scores=True
    )


def predict(text):
    preds = {
        label: pipe[label][0][1] for label in labels
    }

    return preds


examples = [
    "Machine Learning is at the forefront of education, replacing human jobs",
    "AI model leaves scientists confused",
    "This model is not really cool",
]

intf = gr.Interface(fn=predict, inputs="textbox",
                    outputs="label", examples=examples)
intf.launch(inline=False)