final

app.py

@@ -26,21 +26,22 @@ models = [
 
 pipelines = {model: pipeline(task="text-classification",
  model=f"hynky/{model.replace(' ', '_')}", tokenizer="ufal/robeczech-base",
- return_all_scores=True
+ truncation=True, max_length=512,
+ top_k=5
 ) for model in models}
 
-    
+
 def predict(article):
     article = normalize_text(article)
-    predictions = {model: pipelines[model](article) for model in models}
-    return predictions
+    predictions = [pipelines[model](article)[0] for model in models]
+    predictions = [{pred["label"]: round(pred["score"], 3) for pred in task_preds} for task_preds in predictions]
+    return tuple(predictions)
 
 gr.Interface(
     predict,
-    inputs=gr.inputs.Textbox(lines=2, placeholder="Paste a news article here..."),
+    inputs=gr.inputs.Textbox(lines=4, placeholder="Paste a news article here..."),
     # multioutput of gradio text
-    outputs=[gr.outputs.Label(num_top_classses=5)
-    
+    outputs=[gr.outputs.Label(num_top_classes=5, label=model)
     for model in models],
     title="News Article Classifier",
 ).launch()