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| import gradio as gr | |
| import transformers | |
| from transformers import AutoModelForSequenceClassification, AutoTokenizer | |
| from transformers import pipeline | |
| title = "Description to Pokemon-Type" | |
| description = """ | |
| <center> | |
| Given a written description of a character, real or imagined, living or dead - this bot imagines them as a pokemon | |
| and then describes which pokemon type they would be. For example... | |
| "Born and raised in the Austrian Empire, Nikolai Tesla studied engineering and physics in the 1870s without receiving a degree, gaining practical experience in the early 1880s working in telephony and at Continental Edison in the new electric power industry." | |
| Nikolai Tesla would be an electric-type pokemon. | |
| </center> | |
| """ | |
| article = "Inspired by [this article](https://medium.com/analytics-vidhya/predicting-pok%C3%A9mon-type-with-the-pok%C3%A9dex-7038754dc422)." | |
| model = AutoModelForSequenceClassification.from_pretrained('mrcoombes/distilbert-wikipedia-pokemon') | |
| tokenizer = AutoTokenizer.from_pretrained('distilbert-base-uncased') | |
| classifier = pipeline('text-classification', model = model, tokenizer=tokenizer, return_all_scores=True) | |
| clf = lambda x: sorted(classifier(x)[0], key=lambda y: y['score'], reverse=True) | |
| gr_labels = lambda list_of_dicts: {d['label']: d['score'] for d in list_of_dicts} | |
| compose = lambda description: gr_labels(clf(description)) | |
| demo = gr.Interface(fn=compose, inputs="text", outputs=gr.outputs.Label(num_top_classes=5), title=title, description=description, article=article) | |
| demo.launch() |