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import gradio as gr | |
import torch | |
from transformers import RobertaTokenizerFast, BertTokenizerFast, EncoderDecoderModel | |
models_paths = dict() | |
models_paths["fr"] = "mrm8488/camembert2camembert_shared-finetuned-french-summarization" | |
models_paths["de"] = "mrm8488/bert2bert_shared-german-finetuned-summarization" | |
models_paths["tu"] = "mrm8488/bert2bert_shared-turkish-summarization" | |
models_paths["es"] = "Narrativa/bsc_roberta2roberta_shared-spanish-finetuned-mlsum-summarization" | |
device = 'cuda' if torch.cuda.is_available() else 'cpu' | |
def summarize(lang, text): | |
tokenizer = RobertaTokenizerFast.from_pretrained(models_paths[lang]) if lang == "fr" or lang == "es" else BertTokenizerFast.from_pretrained(models_paths[lang]) | |
model = EncoderDecoderModel.from_pretrained(models_paths[lang]).to(device) | |
inputs = tokenizer([text], padding="max_length", | |
truncation=True, max_length=512, return_tensors="pt") | |
input_ids = inputs.input_ids.to(device) | |
attention_mask = inputs.attention_mask.to(device) | |
output = model.generate(input_ids, attention_mask=attention_mask) | |
return tokenizer.decode(output[0], skip_special_tokens=True) | |
theme = "darkgrass" | |
title = "Multilingual Summarization model (MLSUM)" | |
description = "Gradio Demo for Summarization models trained on MLSUM dataset by Manuel Romero" | |
article = "<p style='text-align: center'><a href='https://hf.com/mrm8488' target='_blank'>More models</a></p>" | |
gr.Interface(fn=summarize, inputs=[gr.inputs.Radio(["fr", "de", "tu", "es"]), gr.inputs.Textbox( | |
lines=7, label="Input Text")], outputs="text", theme=theme, title=title, description=description, article=article, enable_queue=True).launch(inline=False) | |