app.py

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

# Load model and tokenizer
model_name = "./model"
tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=False)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)

# Check if CUDA (GPU) is available, otherwise fall back to CPU
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = model.to(device)

# Define lang code to token ID mapping manually
lang_code_to_token = {
    "eng_Latn": "__eng_Latn__",
    "deu_Latn": "__deu_Latn__",
    "fra_Latn": "__fra_Latn__",
    "spa_Latn": "__spa_Latn__"
}

# Choose target language (change as needed)
target_lang_code = "eng_Latn"  # ← change to "deu_Latn" for German
src_lang_code = "mya"          # Burmese

def translate(text):
    if not text.strip():
        return "Please enter some Burmese text."

    # Set source language for tokenizer
    tokenizer.src_lang = src_lang_code

    # Tokenize input
    inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=512).to(device)

    # ✅ Get forced BOS token ID manually
    lang_token = lang_code_to_token[target_lang_code]
    forced_bos_token_id = tokenizer.convert_tokens_to_ids(lang_token)

    # Generate translation
    with torch.no_grad():
        generated_tokens = model.generate(
            **inputs,
            forced_bos_token_id=forced_bos_token_id,
            max_length=128
        )

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

# Gradio UI
with gr.Blocks(title="Burmese to English Translator") as demo:
    gr.Markdown("## 🇲🇲 ➡️ 🇬🇧 Burmese to English Translator")
    with gr.Row():
        with gr.Column():
            input_text = gr.Textbox(label="Burmese Text", placeholder="Type Burmese here...", lines=4)
            btn = gr.Button("Translate")
        with gr.Column():
            output_text = gr.Textbox(label="Translation", interactive=False)

    btn.click(translate, inputs=input_text, outputs=output_text)

demo.launch()