Spaces:
Running
Running
File size: 1,630 Bytes
40d1f52 5a493cf 40d1f52 5a493cf 40d1f52 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 |
import gradio as gr
import os
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'])
title = "DSA II"
description_main = """
A set of models to perform sentiment analysis. Choose between Large-Multilingual or Small-En-only.
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 tuned BERT-base-cased model.
"""
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=None,
inputs=None,
outputs=None,
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() |