Spaces:
Sleeping
Sleeping
| import gradio as gr | |
| import torch | |
| from transformers import AutoTokenizer, AutoModelForSeq2SeqLM | |
| # Predict with test data (first 5 rows) | |
| model_ckpt = "GenzNepal/mt5-summarize-nepali" | |
| device = "cuda" if torch.cuda.is_available() else "cpu" | |
| t5_tokenizer = AutoTokenizer.from_pretrained(model_ckpt) | |
| model = AutoModelForSeq2SeqLM.from_pretrained(model_ckpt).to(device) | |
| def summarize(text): | |
| inputs = t5_tokenizer(text, return_tensors="pt", max_length=1024, padding= "max_length", truncation=True, add_special_tokens=True) | |
| generation = model.generate( | |
| input_ids = inputs['input_ids'].to(device), | |
| attention_mask=inputs['attention_mask'].to(device), | |
| num_beams=6, | |
| num_return_sequences=1, | |
| no_repeat_ngram_size=2, | |
| repetition_penalty=1.0, | |
| min_length=100, | |
| max_length=250, | |
| length_penalty=2.0, | |
| early_stopping=True | |
| ) | |
| # # Convert id tokens to text | |
| output = t5_tokenizer.decode(generation[0], skip_special_tokens=True, clean_up_tokenization_spaces=True) | |
| return output | |
| demo = gr.Interface( | |
| fn=summarize, | |
| inputs=gr.Textbox(placeholder="Enter news " , lines=5, max_lines=20, label="News"), | |
| outputs=gr.Textbox(label="Generated Summary") | |
| ) | |
| if __name__ == "__main__": | |
| demo.launch() | |