app.py

import gradio
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
from scipy.special import softmax


def predict(text):
    model_name = "deepset/bert-base-german-cased-hatespeech-GermEval18Coarse"
    short_score_descriptions = {0: "Kein Hasskommentar", 1: "Hasskommentar"}
    tokenizer = AutoTokenizer.from_pretrained(model_name)
    model = AutoModelForSequenceClassification.from_pretrained(model_name)
    model_input = tokenizer(*([text],), padding=True, return_tensors="pt")
    with torch.no_grad():
        output = model(**model_input)
        logits = softmax(output[0][0].detach().numpy()).tolist()
    return {short_score_descriptions[k]: v for k, v in enumerate(logits)}


gradio.Interface(
    title="Klassifikator deutschsprachiger Hasskommentare",
    inputs=[
        gradio.Textbox(label="Kommentar"),
    ],
    fn=predict,
    outputs=[
        gradio.Label(label="Klassifikation"),
    ],
).launch()