app.py

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

# Load tokenizer and model
model_name = "jsbeaudry/creole-translation-nllb-600M"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
# Assuming the necessary imports and model loading from the previous code are in place

def translate_text(text):
    # Set the source and target language codes
    src_lang = "eng_Latn"
    tgt_lang = "hat_Latn"
    tokenizer_ = tokenizer
    model_ = model

    # Set tokenizer to source language
    tokenizer_.src_lang = src_lang

    # Tokenize the input
    inputs = tokenizer_(text, return_tensors="pt")

    # Find the BOS token ID for the target language
    forced_bos_token_id = tokenizer_.convert_tokens_to_ids(tgt_lang)

    # Move model to GPU if available
    device = "cuda" if torch.cuda.is_available() else "cpu"
    model_ = model_.to(device)
    inputs = inputs.to(device)

    # Generate translation
    generated_tokens = model_.generate(
        **inputs,
        forced_bos_token_id=forced_bos_token_id,
        max_length=100
    )

    # Decode and print
    translated = tokenizer_.batch_decode(generated_tokens, skip_special_tokens=True)
    return translated[0]

iface = gr.Interface(
    fn=translate_text,
    inputs=gr.Textbox(lines=5, placeholder="Enter text to translate"),
    outputs="text",
    title="English to Haitian Creole Translation",
    description="Translate English text to Haitian Creole using a fine-tuned NLLB model."
)

iface.launch()