app.py

import gradio as gr
from transformers import EncoderDecoderModel
from encoder_decoder_tokenizer import EncoderDecoderTokenizer
import torch
import re

from huggingface_hub import snapshot_download

# Download the repo to a local folder
path_to_downloaded = snapshot_download(
    repo_id="Darsala/Georgian-Translation",
    local_dir="./Georgian-Translation",
    local_dir_use_symlinks=False
)

# Load the model and tokenizer from the downloaded folder
model = EncoderDecoderModel.from_pretrained(path_to_downloaded)
tokenizer = EncoderDecoderTokenizer.from_pretrained(path_to_downloaded)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

def translate(
    text: str,
    num_beams: int = 5,
    max_length: int = 256,
) -> str:
    """
    Translate a single string with the given EncoderDecoderModel.
    """
    text = text.lower()
    text = re.sub(r'\s+', ' ', text)
    # tokenize & move to device
    inputs = tokenizer(
        text,
        return_tensors="pt",
        truncation=True,
        padding="longest"
    ).to(device)

    # generation
    generated_ids = model.generate(
        input_ids=inputs.input_ids,
        attention_mask=inputs.attention_mask,
        num_beams=num_beams,
        max_length=max_length,
        early_stopping=True,
    )
    output = tokenizer.decode(generated_ids[0], skip_special_tokens=True)
    print(f"English: {text}")
    print(f"Translated: {output}")
    # decode the first (and only) sequence
    return output

demo = gr.Interface(
    fn=translate,
    inputs=[
        gr.components.Textbox(label="Text"),
    ],
    outputs=["text"],
    examples=[["Hello, what's your name?"]],
    cache_examples=False,
    title="Translation Demo",
    description="This demo is a Georgian-Translation model"
)

demo.launch()