Add predict function

app.py

@@ -1,17 +1,40 @@
 """Gradio app to showcase the language detector."""
 
 import gradio as gr
+from transformers import pipeline
+
+
+# Get transformer model and set up a pipeline
+model_ckpt = "papluca/xlm-roberta-base-language-detection"
+pipe = pipeline("text-classification", model=model_ckpt)
+
+
+def predict(text: str) -> dict:
+    """Compute predictions for text."""
+    preds = pipe(text, return_all_scores=True)
+    if preds:
+        pred = preds[0]
+        return {p["label"]: float(p["score"]) for p in pred}
+    else:
+        return None
+
 
 title = "Language detection with XLM-RoBERTa"
-description = "Determine the language in which your text is written.\nSupported languages are (20): arabic (ar), bulgarian (bg), german (de), modern greek (el), english (en), spanish (es), french (fr), hindi (hi), italian (it), japanese (ja), dutch (nl), polish (pl), portuguese (pt), russian (ru), swahili (sw), thai (th), turkish (tr), urdu (ur), vietnamese (vi), and chinese (zh)."
+description = "Determine the language in which your text is written. Supported languages are (20): arabic (ar), bulgarian (bg), german (de), modern greek (el), english (en), spanish (es), french (fr), hindi (hi), italian (it), japanese (ja), dutch (nl), polish (pl), portuguese (pt), russian (ru), swahili (sw), thai (th), turkish (tr), urdu (ur), vietnamese (vi), and chinese (zh)."
 examples = [
     ["Better late than never."],
     ["Tutto è bene ciò che finisce bene."],
     ["Donde hay humo, hay fuego."],
 ]
 
-app = gr.Interface.load(
-    "huggingface/papluca/xlm-roberta-base-language-detection",
+app = gr.Interface(
+    fn=predict,
+    inputs=gr.inputs.Textbox(
+        placeholder="What's the text you want to know the language for?",
+        label="Text",
+        lines=3,
+    ),
+    outputs=gr.outputs.Label(num_top_classes=3, label="Your text is written in "),
     description=description,
     examples=examples,
 )