Initial version

app.py

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+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()

requirements.txt

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+torch
+transformers