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import gradio as gr
import os
import pandas as pd
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'])
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 II"
description_main = """
A set of models to perform sentiment analysis. Choose between Large-Multilingual or Small-En-only.
Use the interface to check if a language is included in the multilingual model, using language acronyms (e.g. it for Italian).
Select one of the two pages to 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.
"""
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."],
]
interface_words = gr.Interface(
fn=check_lang,
inputs="text",
outputs="text",
description=description_main,
)
interface_model_L = gr.Interface.load(
name=path_to_L_model,
description=description_L,
examples=examples,
title=title,
api_key=read_token,
)
interface_model_S = gr.Interface.load(
name=path_to_S_model,
description=description_S,
examples=examples[0],
title=title,
api_key=read_token,
)
gr.TabbedInterface(
[interface_words, interface_model_L, interface_model_S],
["Intro", "Large Multilingual", "Base En"]
).launch()
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