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import gradio as gr
from transformers import MarianMTModel, MarianTokenizer
# Load English to Arabic translation model and tokenizer
en_ar_model_name = "Helsinki-NLP/opus-mt-en-ar"
en_ar_model = MarianMTModel.from_pretrained(en_ar_model_name)
en_ar_tokenizer = MarianTokenizer.from_pretrained(en_ar_model_name)
# Load Arabic to English translation model and tokenizer
ar_en_model_name = "Helsinki-NLP/opus-mt-ar-en"
ar_en_model = MarianMTModel.from_pretrained(ar_en_model_name)
ar_en_tokenizer = MarianTokenizer.from_pretrained(ar_en_model_name)
def translate_en_to_ar(text):
inputs = en_ar_tokenizer.encode(text, return_tensors="pt")
translation = en_ar_model.generate(inputs, max_length=128)
translated_text = en_ar_tokenizer.decode(translation[0], skip_special_tokens=True)
return translated_text
def translate_ar_to_en(text):
inputs = ar_en_tokenizer.encode(text, return_tensors="pt")
translation = ar_en_model.generate(inputs, max_length=128)
translated_text = ar_en_tokenizer.decode(translation[0], skip_special_tokens=True)
return translated_text
# Create Gradio interfaces for both translation directions
en_ar_interface = gr.Interface(
fn=translate_en_to_ar,
inputs=gr.inputs.Textbox(),
outputs=gr.outputs.Textbox(),
title="English to Arabic Translation",
description="Translate English text to Arabic."
)
ar_en_interface = gr.Interface(
fn=translate_ar_to_en,
inputs=gr.inputs.Textbox(),
outputs=gr.outputs.Textbox(),
title="Quara: Arabic to English Translation",
description="Translate Arabic text to English, made by @abhisheky127"
)
# # Combine the interfaces in a single app
# app = gr.Interface(
# fn=[en_ar_interface, ar_en_interface],
# layout="horizontal",
# title="Translation App",
# description="Translate text between English and Arabic."
# )
if __name__ == "__main__":
# app.launch()
ar_en_interface.launch()
# gr.Parallel([en_ar_interface, ar_en_interface]).launch()
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