Spaces:
Sleeping
Sleeping
| import gradio as gr | |
| from shitsu import ShitsuScorer | |
| scorer = ShitsuScorer('en') | |
| def get_score(user_text): | |
| score = scorer.score([user_text])[0] | |
| yield f'<div class="nice-box"> Score: {score}</div>' | |
| #language_options = ['am', 'ar', 'bg', 'bn', 'cs', 'da', 'de', 'el', 'en', 'es', 'fa', 'fi', 'fr', 'gu', 'ha', 'hi', 'hu', 'id', 'it', 'ja', 'jv', 'kn', 'ko', 'lt', 'mr', 'nl', 'no', 'yo', 'zh'] | |
| #https://huggingface.co/spaces/Dusduo/shitsu-text-scorer-demo/shitsu-logo.jpeg | |
| css = ''' | |
| #gen_btn{height: 100%} | |
| #title{text-align: center} | |
| #title h1{font-size: 3em; display:inline-flex; align-items:center} | |
| #title img{width: 100px; margin-right: 0.5em} | |
| #gallery .grid-wrap{height: 10vh} | |
| #lora_list{background: var(--block-background-fill);padding: 0 1em .3em; font-size: 90%} | |
| .card_internal{display: flex;height: 100px;margin-top: .5em} | |
| .card_internal img{margin-right: 1em} | |
| .styler{--form-gap-width: 0px !important} | |
| .nice-box { | |
| border: 2px solid #007bff; | |
| border-radius: 10px; | |
| padding: 15px; | |
| background-color: #f8f9fa; | |
| font-size: 18px; | |
| text-align: center; | |
| min-height: 60px; | |
| display: flex; | |
| align-items: center; | |
| justify-content: center; | |
| } | |
| ''' | |
| theme = gr.themes.Soft( | |
| primary_hue="blue", | |
| secondary_hue="sky", | |
| ) | |
| with gr.Blocks(theme=theme, css=css) as demo: | |
| title = gr.HTML( | |
| """<h1><img src="https://huggingface.co/spaces/Dusduo/shitsu-text-scorer-demo/shitsu-logo.jpeg" alt="LightBlue"> Shitsu Text Scorer</h1>""", | |
| elem_id="title", | |
| ) | |
| gr.Markdown( | |
| """This is a demo of [Shitsu text scorer](https://huggingface.co/lightblue/shitsu_text_scorer) which scores text based on the amount of useful, textbook-like information in it. | |
| It outputs a score generally between 0 and 1 but can exceed both of these bounds as it is a regressor. | |
| """ | |
| ) | |
| with gr.Row(): | |
| user_text = gr.Textbox(label='Input text...') | |
| with gr.Column(scale=0): | |
| submit_btn = gr.Button("Submit") | |
| score = gr.HTML( | |
| value='<div class="nice-box"> Score... </div>', | |
| label="Output" | |
| ) | |
| # Searchable dropdown to choose language | |
| # language_choice = gr.Dropdown( | |
| # choices=language_options, | |
| # label="Choose a language", | |
| # info="Type to search", | |
| # allow_custom_value=True, # Allows typing to search | |
| # ) | |
| gr.Markdown( | |
| """ | |
| This model is based on fasttext embeddings, meaning that it can be used on large amounts of data with limited compute quickly. | |
| This scorer can be used to filter useful information from large text corpora in many languages. | |
| This model can also be found on [Github](https://github.com/lightblue-tech/shitsu) and has its own pip installable package. | |
| """ | |
| ) | |
| user_text.submit(get_score, inputs=[user_text], outputs=[score]) | |
| submit_btn.click(get_score, inputs=[user_text], outputs=[score]) | |
| demo.launch() | |