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import gradio as gr
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
# Load both translation models from Hugging Face
tokenizer_eng_to_darija = AutoTokenizer.from_pretrained("lachkarsalim/Helsinki-translation-English_Moroccan-Arabic")
model_eng_to_darija = AutoModelForSeq2SeqLM.from_pretrained("lachkarsalim/Helsinki-translation-English_Moroccan-Arabic")
tokenizer_darija_to_msa = AutoTokenizer.from_pretrained("Saidtaoussi/AraT5_Darija_to_MSA")
model_darija_to_msa = AutoModelForSeq2SeqLM.from_pretrained("Saidtaoussi/AraT5_Darija_to_MSA")
# Translation functions
def translate_darija_to_msa(darija_text):
inputs = tokenizer_darija_to_msa(darija_text, return_tensors="pt", padding=True)
translated = model_darija_to_msa.generate(**inputs)
translated_text = tokenizer_darija_to_msa.decode(translated[0], skip_special_tokens=True)
return translated_text
def translate_eng_to_darija(eng_text, direction="eng_to_darija"):
if direction == "eng_to_darija":
inputs = tokenizer_eng_to_darija(eng_text, return_tensors="pt", padding=True)
translated = model_eng_to_darija.generate(**inputs)
translated_text = tokenizer_eng_to_darija.decode(translated[0], skip_special_tokens=True)
else:
# Reverse translation (Darija to English)
inputs = tokenizer_eng_to_darija(eng_text, return_tensors="pt", padding=True)
translated = model_eng_to_darija.generate(**inputs)
translated_text = tokenizer_eng_to_darija.decode(translated[0], skip_special_tokens=True)
return translated_text
# Respond function
def respond(message, translation_choice):
if translation_choice == "Moroccan Arabic to MSA":
return translate_darija_to_msa(message)
elif translation_choice == "English to Moroccan Arabic":
return translate_eng_to_darija(message, direction="eng_to_darija")
# Gradio Interface Layout with organized components
with gr.Blocks() as demo:
with gr.Row():
with gr.Column():
# Header with emojis and logo
gr.Markdown("""
<h1 style="text-align: center;">
🇲🇦 🚀 Moroccan Arabic Translation Demo 😍
</h1>
<p style="text-align: center;">
🌟 Dima Meghrib 🌟 <br>
Select the translation direction and type your text. <br>
Get quick translations between **English** and **Moroccan Arabic (Darija)** or **Darija to Modern Standard Arabic (MSA)**! 🔥
</p>
<p style="text-align: center;">
This demo uses two advanced models:<br>
- English to Moroccan Arabic (Darija)<br>
- Moroccan Arabic (Darija) to Modern Standard Arabic (MSA)<br>
Choose your desired translation direction and get started!<br>
</p>
<p style="text-align: center;">
<img src="https://moroccan-culture-image.s3.eu-north-1.amazonaws.com/2159558.png"
style="width: 150px; display: block; margin: 20px auto;" alt="Moroccan Flag" />
</p>
""")
# Translation Inputs and Outputs
user_input = gr.Textbox(label="Enter Your Text", placeholder="Type your sentence here...")
translation_choice = gr.Dropdown(
label="Choose Translation Direction",
choices=["English to Moroccan Arabic", "Moroccan Arabic to MSA"],
value="English to Moroccan Arabic"
)
submit_button = gr.Button("Submit", elem_id="submit_button")
with gr.Row():
# Output area for translated text
output = gr.Textbox(label="Translated Text", placeholder="Translation will appear here...")
# Footer with your name at the bottom
gr.Markdown("<p style='text-align: center; font-size: 14px;'>Created by Eng Amal 🌟</p>")
# Define the action for submit
submit_button.click(fn=respond, inputs=[user_input, translation_choice], outputs=output)
# Launch the interface
demo.launch()
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