| import gradio as gr | |
| from transformers import BartTokenizer, BartForConditionalGeneration | |
| import torch | |
| MODEL_DIR = './BART model small/model' | |
| device = torch.device("cuda" if torch.cuda.is_available() else "cpu") | |
| tokenizer = BartTokenizer.from_pretrained(MODEL_DIR) | |
| model = BartForConditionalGeneration.from_pretrained(MODEL_DIR).to(device) | |
| def summarize(text): | |
| try: | |
| inputs = tokenizer( | |
| text, | |
| return_tensors="pt", | |
| max_length=1024, | |
| truncation=True | |
| ).to(device) | |
| summary_ids = model.generate( | |
| inputs['input_ids'], | |
| attention_mask=inputs['attention_mask'], | |
| max_length=150, | |
| min_length=30, | |
| num_beams=4, | |
| early_stopping=True | |
| ) | |
| summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True) | |
| return summary | |
| except Exception as e: | |
| return str(e) | |
| interface = gr.Interface( | |
| fn=summarize, | |
| inputs="text", | |
| outputs="text", | |
| title="BART Summarization", | |
| live=True, | |
| description="Enter an article to generate a summary using a fine-tuned BART model." | |
| ) | |
| interface.launch() |