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| from pprint import pformat | |
| import spacy | |
| import gradio as gr | |
| from textacy import preprocessing | |
| import en_ethicalads_topics | |
| ea_nlp = en_ethicalads_topics.load() | |
| preprocessor = preprocessing.make_pipeline( | |
| preprocessing.normalize.unicode, | |
| preprocessing.remove.punctuation, | |
| preprocessing.normalize.whitespace, | |
| ) | |
| def classify(input_text): | |
| processed_input = preprocessor(input_text) | |
| ea_output = ea_nlp(processed_input) | |
| return pformat(sorted(ea_output.cats.items(), key=lambda x: x[1], reverse=True)) | |
| iface = gr.Interface( | |
| fn=classify, | |
| inputs=gr.Textbox(lines=5, placeholder="Input text to detect the topic classification. Works best on inputs of 100+ words."), | |
| outputs="text", | |
| ) | |
| iface.launch() | |