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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() | |