Update app.py

app.py

@@ -5,7 +5,7 @@ import gradio as gr
 
 # init important things
 tokenizer = BertTokenizerFast.from_pretrained('bert-base-uncased')
-model = BertForTokenClassification.from_pretrained('ethical-spectacle/social-bias-ner')
+model = BertForTokenClassification.from_pretrained('maximuspowers/bias-detection-ner')
 model.eval()
 model.to('cuda' if torch.cuda.is_available() else 'cpu')
 
@@ -40,4 +40,19 @@ def predict_ner_tags(sentence):
             labels = [id2label[idx.item()] for idx in label_indices] if label_indices.numel() > 0 else ['O']
             result.append({"token": token, "labels": labels})
 
-    return json.dumps(result, indent=4)
+    return json.dumps(result, indent=4)
+
+# startup gradio
+iface = gr.Interface(
+    fn=predict_ner_tags,
+    inputs="text",
+    outputs="text",
+    title="Social Bias Named Entity Recognition (with BERT) 🕵",
+    description=("Enter a sentence to predict biased parts of speech tags. This model uses multi-label BertForTokenClassification, to label the entities: (GEN)eralizations, (UNFAIR)ness, and (STEREO)types. Labels follow BIO format. Try it out :)."
+                 "<br><br>Read more about how this model was trained in this <a href='https://huggingface.co/blog/maximuspowers/bias-entity-recognition' target='_blank'>blog post</a>."
+                 "<br>Model Page: <a href='https://huggingface.co/maximuspowers/bias-detection-ner' target='_blank'>Bias Detection NER</a>."),
+    allow_flagging="never"
+)
+
+if __name__ == "__main__":
+    iface.launch()