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app.py

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+import gradio as gr
+from transformers import AutoModelForSeq2SeqLM, NllbTokenizer
+
+# Load the pre-trained model and tokenizer
+model_name = "sarahai/nllb-ru-uz"
+model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
+tokenizer = NllbTokenizer.from_pretrained(model_name)
+
+def translate(text, src_lang="rus_Cyrl", tgt_lang="uzn_Latn"):
+    """Translates text from source to target language."""
+    inputs = tokenizer(text, return_tensors="pt")
+    translated = model.generate(**inputs)
+    return tokenizer.batch_decode(translated, skip_special_tokens=True)[0]
+
+# Define the Gradio interface
+interface = gr.Interface(
+    fn=translate,
+    inputs=[gr.Textbox(label="Text to Translate"), gr.Dropdown(choices=["ru", "uz"], label="Source Language"), gr.Dropdown(choices=["uz"], label="Target Language")],
+    outputs="textbox",
+    title="Russian to Uzbek Translator",
+    description="Translate text from Russian to Uzbek using the `sarahai/nllb-ru-uz` model.",
+)
+
+# Launch the Gradio app
+interface.launch(share=True, debug=True)  # Set share=True to create a Hugging Face Space

requirements.txt

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+gradio
+transformers
+torch