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| import gradio as gr | |
| import os | |
| import pandas as pd | |
| path_to_generation_model = str(os.environ['path_to_generation_model']) | |
| path_to_L_model = str(os.environ['path_to_L_model']) | |
| path_to_S_model = str(os.environ['path_to_S_model']) | |
| read_token = str(os.environ['read_token']) | |
| read_token_ii = str(os.environ['read_token_ii']) | |
| languages = pd.read_csv("model_lang.csv", names=["Lang_acr"]) | |
| def check_lang(lang_acronym): | |
| if lang_acronym in languages["Lang_acr"].to_list(): | |
| return "True" | |
| else: | |
| return "False" | |
| title = "DSA: version III" | |
| description_main = """ | |
| A set of pre-trained LLMs tuned to perform sentiment analysis. You can choose between a Multilingual or English-only. | |
| Use the current interface to check if a language is included in the multilingual model, using language acronyms (e.g. it for Italian). | |
| Click on one of the upper buttons to select and start querying one of the two models. | |
| """ | |
| description_L = """ | |
| XLM-R tuned model, EN-tuned, pre-trained with 94 languages available (see original model [card](https://huggingface.co/xlm-roberta-large) to see which are available) | |
| """ | |
| description_S = """ | |
| A BERT-base-cased model pre-trained and tuned on English data. | |
| """ | |
| description_G = """ | |
| A t5 model tuned to performer text-generation, and predict emotion as well as the character experiencing those emotions. | |
| """ | |
| example_main = ["en", "it", "pl"] | |
| examples = [ | |
| ["I was followed by the blue monster but was not scared. I was calm and relaxed."], | |
| ["Ero seguito dal mostro blu, ma non ero spaventato. Ero calmo e rilassato."], | |
| ["Śledził mnie niebieski potwór, ale się nie bałem. Byłem spokojny i zrelaksowany."], | |
| ] | |
| examples_g = [ | |
| ["I'm in an auditorium. Susie S is concerned at her part in this disability awareness spoof we are preparing. I ask, 'Why not do it? Lots of AB's represent us in a patronizing way. Why shouldn't we represent ourselves in a good, funny way?' I watch the video we all made. It is funny. I try to sit on a folding chair. Some guy in front talks to me. Merle is in the audience somewhere. [BL]"], | |
| ] | |
| interface_words = gr.Interface( | |
| fn=check_lang, | |
| inputs="text", | |
| outputs="text", | |
| title=title, | |
| description=description_main, | |
| examples=example_main, | |
| ) | |
| interface_model_L = gr.Interface.load( | |
| name=path_to_L_model, | |
| description=description_L, | |
| examples=examples, | |
| title="DSA Large Multilingual", | |
| api_key=read_token, | |
| ) | |
| interface_model_S = gr.Interface.load( | |
| name=path_to_S_model, | |
| description=description_S, | |
| examples=examples[0], | |
| title="DSA Base English-Only", | |
| api_key=read_token_ii, | |
| ) | |
| interface_model_G = gr.Interface.load( | |
| name=path_to_generation_model, | |
| description=description_G, | |
| examples=examples_g, | |
| title="DSA Generation", | |
| api_key=read_token_ii, | |
| ) | |
| gr.TabbedInterface( | |
| [interface_words, interface_model_L, interface_model_S, interface_model_G], | |
| ["Intro", "Large Multilingual", "Base En", "En Generation"] | |
| ).launch() | |