import os PATH = '/data/' # at least 150GB storage needs to be attached os.environ['TRANSFORMERS_CACHE'] = PATH os.environ['HF_HOME'] = PATH os.environ['HF_DATASETS_CACHE'] = PATH os.environ['TORCH_HOME'] = PATH import gradio as gr from interfaces.cap import demo as cap_demo from interfaces.manifesto import demo as manifesto_demo from interfaces.sentiment import demo as sentiment_demo from interfaces.emotion import demo as emotion_demo from interfaces.ner import demo as ner_demo from interfaces.ner import download_models as download_spacy_models from utils import download_hf_models with gr.Blocks() as demo: gr.Markdown( f""" <style> @import 'https://fonts.googleapis.com/css?family=Source+Sans+Pro:300,400'; </style> <div style="display: block; text-align: left; padding:0; margin:0;font-family: "Source Sans Pro", Helvetica, sans-serif;"> <h1 style="text-align: center;font-size: 17pt;">Babel Machine Demo</h1> <p style="font-size: 14pt;">This is a demo for text classification using language models finetuned on data labeled by <a href="https://www.comparativeagendas.net/">CAP</a>, <a href="https://manifesto-project.wzb.eu/">Manifesto Project</a>, sentiment, emotion coding and Named Entity Recognition systems.<br> For the coding of complete datasets, please visit the official <a href="https://babel.poltextlab.com/">Babel Machine</a> site.<br> Please note that named entity inputs are case sensitive.<br> </p> </div> """) gr.TabbedInterface( interface_list=[cap_demo, manifesto_demo, sentiment_demo, emotion_demo, ner_demo], tab_names=["CAP", "Manifesto", "Sentiment (3)", "Emotions (8)", "Named Entity Recognition"], ) if __name__ == "__main__": download_hf_models() download_spacy_models() demo.launch() # TODO: add all languages & domains