app.py

import gradio as gr
import torch
import os
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import spaces

# Load Hugging Face token from the environment variable
HF_TOKEN = os.getenv("HF_TOKEN")
if HF_TOKEN is None:
    raise ValueError("HF_TOKEN environment variable is not set. Please set it before running the script.")

# Check for GPU support and configure appropriately
device = "cuda" if torch.cuda.is_available() else "cpu"
zero = torch.Tensor([0]).to(device)
print(f"Device being used: {zero.device}")

# Model configurations
MSA_TO_SYRIAN_MODEL = "Omartificial-Intelligence-Space/Shami-MT"
SYRIAN_TO_MSA_MODEL = "Omartificial-Intelligence-Space/SHAMI-MT-2MSA"

# Load models and tokenizers
print("Loading MSA to Syrian model...")
msa_to_syrian_tokenizer = AutoTokenizer.from_pretrained(MSA_TO_SYRIAN_MODEL)
msa_to_syrian_model = AutoModelForSeq2SeqLM.from_pretrained(MSA_TO_SYRIAN_MODEL).to(device)

print("Loading Syrian to MSA model...")
syrian_to_msa_tokenizer = AutoTokenizer.from_pretrained(SYRIAN_TO_MSA_MODEL)
syrian_to_msa_model = AutoModelForSeq2SeqLM.from_pretrained(SYRIAN_TO_MSA_MODEL).to(device)

print("Models loaded successfully!")

@spaces.GPU(duration=120)
def translate_msa_to_syrian(text):
    """Translate from Modern Standard Arabic to Syrian dialect"""
    if not text.strip():
        return ""
    
    try:
        input_ids = msa_to_syrian_tokenizer(text, return_tensors="pt").input_ids.to(device)
        outputs = msa_to_syrian_model.generate(input_ids, max_length=128, num_beams=5, early_stopping=True)
        translated_text = msa_to_syrian_tokenizer.decode(outputs[0], skip_special_tokens=True)
        return translated_text
    except Exception as e:
        return f"Translation error: {str(e)}"

@spaces.GPU(duration=120)
def translate_syrian_to_msa(text):
    """Translate from Syrian dialect to Modern Standard Arabic"""
    if not text.strip():
        return ""
    
    try:
        input_ids = syrian_to_msa_tokenizer(text, return_tensors="pt").input_ids.to(device)
        outputs = syrian_to_msa_model.generate(input_ids, max_length=128, num_beams=5, early_stopping=True)
        translated_text = syrian_to_msa_tokenizer.decode(outputs[0], skip_special_tokens=True)
        return translated_text
    except Exception as e:
        return f"Translation error: {str(e)}"

def bidirectional_translate(text, direction):
    """Handle bidirectional translation based on user selection"""
    if direction == "MSA → Syrian":
        return translate_msa_to_syrian(text)
    elif direction == "Syrian → MSA":
        return translate_syrian_to_msa(text)
    else:
        return "Please select a translation direction"

# Create Gradio interface
with gr.Blocks(title="SHAMI-MT: Bidirectional Syria Arabic Dialect MT Framework") as demo:
    
    gr.HTML("""
    <div style="text-align: center; margin-bottom: 2rem;">
        <h1>🌍 SHAMI-MT: Bidirectional Arabic Translation</h1>
        <p>Translate between Modern Standard Arabic (MSA) and Syrian Dialect</p>
        <p><strong>Built on AraT5v2-base-1024 architecture</strong></p>
    </div>
    """)
    
    with gr.Row():
        with gr.Column(scale=1):
            gr.HTML("""
            <div style="background: #f8f9fa; padding: 1rem; border-radius: 8px; margin: 1rem 0;">
                <h3>📚 Model Information</h3>
                <ul>
                    <li><strong>Model Type:</strong> Sequence-to-Sequence Translation</li>
                    <li><strong>Base Model:</strong> UBC-NLP/AraT5v2-base-1024</li>
                    <li><strong>Languages:</strong> Arabic (MSA ↔ Syrian Dialect)</li>
                    <li><strong>Device:</strong> GPU/CPU Auto-detection</li>
                </ul>
            </div>
            """)
    
        with gr.Column(scale=2):
            direction = gr.Dropdown(
                choices=["MSA → Syrian", "Syrian → MSA"],
                value="MSA → Syrian",
                label="Translation Direction"
            )
            
            input_text = gr.Textbox(
                label="Input Text",
                placeholder="Enter Arabic text here...",
                lines=5
            )
            
            translate_btn = gr.Button("🚀 Translate", variant="primary")
            
            output_text = gr.Textbox(
                label="Translation",
                lines=5
            )
    
    # Connect the interface
    translate_btn.click(
        fn=bidirectional_translate,
        inputs=[input_text, direction],
        outputs=output_text
    )
    
    # Add example inputs
    gr.Examples(
        examples=[
            ["أنا لا أعرف إذا كان سيتمكن من الحضور اليوم أم لا.", "MSA → Syrian"],
            ["كيف حالك؟", "MSA → Syrian"],
            ["ما بعرف إذا رح يقدر يجي اليوم ولا لأ.", "Syrian → MSA"],
            ["شلونك؟", "Syrian → MSA"]
        ],
        inputs=[input_text, direction],
        outputs=output_text,
        fn=bidirectional_translate
    )

# Launch the app
if __name__ == "__main__":
    demo.launch(server_name="0.0.0.0", server_port=7860, share=True)