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from transformers import AutoModelForSeq2SeqLM, AutoTokenizer | |
# Path to your model's checkpoints | |
model_checkpoint_path = "barbaroo/nllb_200_600M_fo_en" | |
# Load the tokenizer and model | |
tokenizer = AutoTokenizer.from_pretrained(model_checkpoint_path) | |
model = AutoModelForSeq2SeqLM.from_pretrained(model_checkpoint_path) | |
# Function to perform translation | |
def translate(text, model, tokenizer): | |
# Encode the text | |
inputs = tokenizer.encode(text, return_tensors="pt") | |
# Generate translation outputs | |
outputs = model.generate(inputs, max_length=80, num_beams=4, early_stopping=True) | |
# Decode and return the translated text | |
translated_text = tokenizer.decode(outputs[0], skip_special_tokens=True) | |
return translated_text | |
# Sample translation (You can comment this out when deploying to Gradio) | |
faroese_text = "Granskingarvirksemi fevnir bæði um føroyskar og altjóða granskingarverkætlanir." | |
translated_text = translate(faroese_text, model, tokenizer) | |
print(translated_text) | |
# Gradio Interface setup | |
# Ensure Gradio is installed | |
!pip install gradio | |
# Importing Gradio | |
import gradio as gr | |
# Define the Gradio interface | |
def gradio_translate(text): | |
return translate(text, model, tokenizer) | |
iface = gr.Interface(fn=gradio_translate, | |
inputs="text", | |
outputs="text", | |
title="Faroese to English Translator", | |
description="Translate Faroese text to English using a state-of-the-art model.") | |
# Launch the interface | |
iface.launch() |