| from transformers import AutoTokenizer, AutoModelForSeq2SeqLM |
| import gradio as gr |
|
|
| MODEL_ID = "adcelis/opus-books-en-es-flan-t5-small" |
|
|
| tokenizer = AutoTokenizer.from_pretrained(MODEL_ID) |
| model = AutoModelForSeq2SeqLM.from_pretrained(MODEL_ID) |
|
|
| def translate_text(text): |
| if not text or not text.strip(): |
| return "" |
|
|
| prompt = f"translate English to Spanish: {text}" |
|
|
| inputs = tokenizer(prompt, return_tensors="pt", truncation=True) |
| outputs = model.generate( |
| **inputs, |
| max_new_tokens=128 |
| ) |
|
|
| result = tokenizer.decode(outputs[0], skip_special_tokens=True) |
| return result |
|
|
| demo = gr.Interface( |
| fn=translate_text, |
| inputs=gr.Textbox( |
| lines=5, |
| label="Texto en inglés", |
| placeholder="Write here the English sentence you want to translate..." |
| ), |
| outputs=gr.Textbox( |
| lines=5, |
| label="Traducción al español" |
| ), |
| title="English to Spanish Translation", |
| description="Demo del modelo fine-tuned con opus_books para traducir texto del inglés al español.", |
| examples=[ |
| ["The house was quiet and the children were sleeping."], |
| ["I have never seen such a beautiful garden."], |
| ["She opened the window and looked at the sea."] |
| ], |
| flagging_mode="never" |
| ) |
|
|
| if __name__ == "__main__": |
| demo.launch() |
