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| import torch | |
| import gradio as gr | |
| from fastai.text.all import * | |
| from transformers import BartTokenizer | |
| from datasets import load_dataset, load_metric | |
| # Load the pre-trained model and tokenizer (adjust for Bart if needed) | |
| pretrained_model_name = "facebook/bart-large-cnn" # Or "facebook/bart-base" | |
| hf_tokenizer = BartTokenizer.from_pretrained(pretrained_model_name) | |
| # Load the SQuAD dataset | |
| squad = load_dataset("squad") | |
| # Load the SQuAD metric | |
| squad_metric = load_metric("squad", data_dir="path/to/squad/dataset", trust_remote_code=True) | |
| def summarize(article): | |
| # Define your data transformation pipeline here, if applicable | |
| # ... | |
| # Load the exported model | |
| learn = load_learner('article_highlights.pkl') | |
| # Generate the summary | |
| summary = learn.blurr_generate(article)[0] | |
| return summary | |
| # Create the Gradio interface | |
| iface = gr.Interface( | |
| fn=summarize, | |
| inputs="text", | |
| outputs="text", | |
| title="Article Summarizer", | |
| description="Enter an article and get a summary.", | |
| examples=[["This is an example article..."]] | |
| ) | |
| # Launch the Gradio interface | |
| iface.launch() | |