Create app.py

app.py

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+import gradio as gr
+import sentencepiece as spm
+import ctranslate2
+from huggingface_hub import hf_hub_download
+import os
+
+languages = {
+    "Kurdish": "ku",
+    "Samoan": "sm",
+    "Xhosa": "xh",
+    "Lao": "lo",
+    "Corsican": "co",
+    "Cebuano": "ceb",
+    "Galician": "gl",
+    "Yiddish": "yi",
+    "Swahili": "sw",
+    "Yoruba": "yo",
+    "English": "en",
+}
+
+def get_repo_id(src_lang, tgt_lang):
+    return f"lingvanex/{src_lang.lower()}-to-{tgt_lang.lower()}-translation"
+
+def download_models(src_lang, tgt_lang):
+    repo_id = get_repo_id(src_lang, tgt_lang)
+    models = {
+        "src_spm": hf_hub_download(repo_id=repo_id, filename=f"{languages[src_lang]}.spm.model"),
+        "tgt_spm": hf_hub_download(repo_id=repo_id, filename=f"{languages[tgt_lang]}.spm.model"),
+        "model": hf_hub_download(repo_id=repo_id, filename="model.bin"),
+        "source_vocab": hf_hub_download(repo_id=repo_id, filename="source_vocabulary.txt"),
+        "target_vocab": hf_hub_download(repo_id=repo_id, filename="target_vocabulary.txt"),
+        "config": hf_hub_download(repo_id=repo_id, filename="config.json"),
+    }
+    return models
+
+def translate(text, src_lang, tgt_lang):
+    if src_lang == tgt_lang:
+        return text
+
+    models = download_models(src_lang, tgt_lang)
+
+    spm_encoder = spm.SentencePieceProcessor(models["src_spm"])
+    spm_decoder = spm.SentencePieceProcessor(models["tgt_spm"])
+
+    model_dir = os.path.dirname(models["model"])
+
+    translator = ctranslate2.Translator(model_dir, device="cpu")
+
+    tokens = spm_encoder.encode(text, out_type=str)
+
+    result = translator.translate_batch([tokens])
+
+    output = spm_decoder.decode(result[0].hypotheses[0], out_type=str)
+
+    return output
+
+
+def update_target_lang(src_lang):
+    if src_lang == "English":
+        return gr.Dropdown(choices=sorted(languages.keys())), ""
+    else:
+        return gr.Dropdown(choices=["English"]), "Note: Translations are only supported from this language to English."
+
+
+with gr.Blocks() as demo:
+    gr.Markdown("# Multilingual Translation with Lingvanex")
+    gr.Markdown("""
+    This translator allows you to translate text between English and a variety of other languages. Please note that translations are supported only in the following directions:
+    - From English to the target language (e.g., English → Kurdish).
+    - From the source language to English (e.g., Kurdish → English).
+    For this demo, language-specific model pairs are utilized, so translations between two non-English languages are not supported at this time.
+    """)
+
+    with gr.Row():
+        src_lang = gr.Dropdown(choices=sorted(languages.keys()), label="Source Language", value="English")
+        tgt_lang = gr.Dropdown(choices=sorted(languages.keys()), label="Target Language", value="Kurdish")
+        note = gr.Markdown("")
+
+    src_lang.change(update_target_lang, inputs=src_lang, outputs=[tgt_lang, note])
+
+    text_input = gr.Textbox(label="Input Text", placeholder="Enter text to translate...")
+    text_output = gr.Textbox(label="Translated Text")
+
+    examples = gr.Examples(
+        examples=[
+            ["Hello, how are you?", "English", "Kurdish"],
+            ["Silav halê we çawa ye?", "Kurdish", "English"],
+        ],
+        inputs=[text_input, src_lang, tgt_lang],
+    )
+
+    translate_btn = gr.Button("Translate")
+    translate_btn.click(translate, inputs=[text_input, src_lang, tgt_lang], outputs=text_output)
+
+demo.launch(share=True)