app.py

import numpy as np
import onnxruntime as ort
import torch
from transformers import MarianMTModel, MarianTokenizer
import gradio as gr

# Load the MarianMT model and tokenizer from the local folder
model_path = "./model.onnx"  # Path to the folder containing the model files
tokenizer = MarianTokenizer.from_pretrained(model_name)
decoder_model = MarianMTModel.from_pretrained(model_name).get_decoder()

# Load the ONNX encoder
encoder_session = ort.InferenceSession("./onnx_model/encoder.onnx")

def translate_text(input_text):
    # Tokenize input text
    tokenized_input = tokenizer(
        input_text, return_tensors="pt", padding=True, truncation=True, max_length=512
    )
    input_ids = tokenized_input["input_ids"]
    attention_mask = tokenized_input["attention_mask"]

    # Generate translation using the model
    with torch.no_grad():
        outputs = model.generate(
            input_ids=input_ids,
            attention_mask=attention_mask,
            max_length=512,  # Maximum length of the output
            num_beams=5,  # Use beam search for better translations
            early_stopping=True,  # Stop generation when the model predicts the end-of-sequence token
        )

    # Decode the output tokens
    translated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
    return translated_text

interface.launch()