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app.py

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+from transformers import AutoModelForSequenceClassification, AutoTokenizer,pipeline
+import torch
+import gradio as gr
+
+model = AutoModelForSequenceClassification.from_pretrained('NLP-LTU/bertweet-large-sexism-detector')
+def predict(prompt):
+    tokenizer = AutoTokenizer.from_pretrained('NLP-LTU/bertweet-large-sexism-detector') 
+    classifier = pipeline("text-classification", model=model, tokenizer=tokenizer)
+    prediction=classifier(prompt)
+    #label_pred = 'not sexist' if prediction == 0 else 'sexist'
+    return prediction
+gr.Interface(
+    fn=predict,
+    inputs="textbox",
+    outputs="text",
+    title=title,
+    description=description,
+    article=article,
+    examples=[["Every woman wants to be a model. It's codeword for 'I get everything for free and people want me' "], ["basically I placed more value on her than I should then?"]],
+).launch(share=True)

requirements .txt

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+transformers
+torch