app.py

import gradio as gr
import torch
from transformers import MarianMTModel, MarianTokenizer

# Model configuration
MODEL_NAME = "cihanunlu/medical-nmt-tr-en"

# Load model and tokenizer
print(f"Loading model: {MODEL_NAME}")
tokenizer = MarianTokenizer.from_pretrained(MODEL_NAME)
model = MarianMTModel.from_pretrained(MODEL_NAME)
model.eval()

# Move to GPU if available
device = "cuda" if torch.cuda.is_available() else "cpu"
model = model.to(device)
print(f"Model loaded on {device}")

def translate_turkish_to_english(text, max_length=256, num_beams=5):
    if not text or not text.strip():
        return "Please enter Turkish text to translate."

    try:
        inputs = tokenizer(
            text,
            return_tensors="pt",
            padding=True,
            truncation=True,
            max_length=max_length
        )
        inputs = {k: v.to(device) for k, v in inputs.items()}

        with torch.no_grad():
            outputs = model.generate(
                **inputs,
                max_length=max_length,
                num_beams=num_beams,
                early_stopping=True
            )

        return tokenizer.decode(outputs[0], skip_special_tokens=True)

    except Exception as e:
        return f"Error: {str(e)}"

examples = [
    "Hastanın kan basıncı normal sınırlarda.",
    "Ameliyat başarılı bir şekilde tamamlandı.",
    "Hasta taburcu edilmeye hazır.",
    "Laboratuvar sonuçları bekleniyor.",
    "Dalak üst pol lokalizasyonunda iki adet 6 mm boyutunda aksesuar dalağı düşündüren şey var.",
]

demo = gr.Interface(
    fn=translate_turkish_to_english,
    inputs=gr.Textbox(
        label="🇹🇷 Turkish Text",
        placeholder="Enter Turkish medical text here...",
        lines=5
    ),
    outputs=gr.Textbox(
        label="🇬🇧 English Translation",
        lines=5
    ),
    title="🏥 Medical Translator: Turkish → English",
    description="""
    **Features:**
    - Domain-specific medical terminology
    - High-quality translations for healthcare contexts
    - Fast inference with beam search
    """,
    examples=examples
)

if __name__ == "__main__":
    demo.launch(
        theme=gr.themes.Citrus()
    )