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import gradio as gr
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer, pipeline
# Define available languages with their corresponding model suffixes
languages = {
"english": "en",
"spanish": "es",
"french": "fr",
"german": "de",
"chinese": "zh",
"japanese": "ja",
"korean": "ko",
"italian": "it",
"portuguese": "pt",
"russian": "ru",
"hindi": "hi",
"arabic": "ar",
"dutch": "nl",
"turkish": "tr",
"greek": "el",
"urdu": "ur"
}
# Define the translation function
def translate_text(text, target_language):
# Get the correct model name based on the target language
target_language_code = languages.get(target_language.lower())
if target_language_code:
model_name = f"Helsinki-NLP/opus-mt-en-{target_language_code}"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
translator = pipeline("translation", model=model, tokenizer=tokenizer)
translation = translator(text)[0]['translation_text']
return translation
else:
return "Error: Language not supported or incorrect."
# Set up the Gradio interface with a submit button and side-by-side layout
with gr.Blocks() as iface:
gr.Markdown("# Text Translator")
gr.Markdown("Translate text into multiple languages using Hugging Face models.")
# Create a row for input and output
with gr.Row():
# Input components on the left
with gr.Column(scale=1): # This column is smaller
text_input = gr.Textbox(label="Enter text to translate")
language_dropdown = gr.Dropdown(list(languages.keys()), label="Target Language", type="value")
# Output components on the right
with gr.Column(scale=1): # This column is also smaller
translation_output = gr.Textbox(label="Translation", interactive=False)
# Button to submit the translation
submit_button = gr.Button("Translate")
# When button is clicked, trigger the translation
submit_button.click(fn=translate_text, inputs=[text_input, language_dropdown], outputs=translation_output)
iface.launch()
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