added hawkishness score output

app.py

@@ -1,16 +1,16 @@
-from typing import Any, Dict
+from typing import Any
 
 from transformers import pipeline, LongformerForSequenceClassification, LongformerTokenizer, Trainer
 import gradio as gr
 
 
-def predict_fn(text: str) -> Dict[str, Any]:
+def predict_fn(text: str) -> tuple[Any, Any]:
     model = LongformerForSequenceClassification.from_pretrained("model")
     tokenizer = LongformerTokenizer.from_pretrained("allenai/longformer-base-4096")
     p = pipeline("sentiment-analysis", model=model, tokenizer=tokenizer)
     results = p(text)
     factor = 100 if results[0]['label'] == 'Hawkish' else -100
-    return {"label": results[0]['label'], "hawkishness_score": round(results[0]['score'] * factor, 0)}
+    return results[0]['label'], round(results[0]['score'] * factor, 0)
 
 
-gr.Interface(predict_fn, "textbox", "label").launch()
+gr.Interface(predict_fn, "textbox", ["label", "label"]).launch()