| import gradio as gr | |
| from transformers import pipeline | |
| from transformers import AutoTokenizer, AutoModelForSequenceClassification | |
| sample_text = [ | |
| [ | |
| "Poems on various subjects. Whereto is prefixed a short essay on the structure of English verse" | |
| ], | |
| [ | |
| "Journal of a Residence in China and the neighbouring countries from 1830 to 1833. With an introductory essay by the Hon. and Rev. Baptist Wriothesley Noel. [With a map.]" | |
| ], | |
| ["The Adventures of Oliver Twist. [With plates.]"], | |
| ["['The Adventures of Sherlock Holmes', 'Single Works']"], | |
| [ | |
| "['Coal, Iron, and Oil; or, the Practical American miner. A plain and popular work on our mines and mineral resources ... With numerous maps and engravings, etc']" | |
| ], | |
| [ | |
| "Summer Travelling in Iceland; being the narrative of two journeys across the island ... With a chapter on Askja by E. Delmar Morgan ... Containing also a literal translation of three sagas. Maps, etc'" | |
| ], | |
| [ | |
| "Histoire de France au moyen aÃÇge, depuis Philippe-Auguste jusqu'aÃÄ la fin du reÃÄgne de Louis XI. 1223-1483. Troisieme eÃÅdition" | |
| ], | |
| [ | |
| "Two Centuries of Soho: its institutions, firms, and amusements. By the Clergy of St. Anne's, Soho, J. H. Cardwell ... H. B. Freeman ... G. C. Wilton ... assisted by other contributors, etc" | |
| ], | |
| ["""A Christmas Carol"""], | |
| ] | |
| description = """ | |
| British Library Books genre detection model. The interpretation interface helps what the model is using to make its predictions. Words highlighted in red contributed to the model being more confident about a prediction. The intensity of colour corresponds to the importance of that part of the input. The words that decrease the label confidence are highlighted in blue. | |
| """ | |
| tokenizer = AutoTokenizer.from_pretrained("BritishLibraryLabs/bl-books-genre") | |
| model = AutoModelForSequenceClassification.from_pretrained("BritishLibraryLabs/bl-books-genre") | |
| classifier = pipeline('text-classification',model=model, tokenizer=tokenizer, return_all_scores=True) | |
| def predict(text): | |
| predictions = classifier(text) | |
| return {pred['label']: pred['score'] for pred in predictions[0]} | |
| gr.Interface(predict, | |
| inputs=gr.inputs.Textbox(label="Book title"), | |
| outputs=gr.outputs.Label(label="Predicted genre"), | |
| interpretation='shap', num_shap=10, | |
| examples=sample_text,description=description, | |
| ).launch(enable_queue=True) | |