app.py

import gradio as gr
from transformers import pipeline

model_id = "juliensimon/xlm-v-base-language-id"

p = pipeline("text-classification", model=model_id)


def process(text, top_k=5):
    pred = p(text, top_k=top_k)
    scores = {x["label"]: x["score"] for x in pred}
    print(scores)
    return scores


# Gradio inputs
input_text = gr.Text(label="Enter text")

# Gradio outputs
labels = gr.Label(label="Languages", num_top_classes=5)

description = "This Space lets you perform language identification on the 102 languages present in the google/fleurs dataset."

iface = gr.Interface(
    description=description,
    fn=process,
    inputs=input_text,
    outputs=labels,
    examples=[],
    allow_flagging="never",
)

iface.launch()