|
|
| import gradio as gr |
| import os |
| import base64 |
| import spacy |
| from utilities import constants |
| from spacy import displacy |
| os.system('python -m spacy download en_core_web_sm') |
| nlp = spacy.load("en_core_web_sm") |
|
|
| input_examples=[ |
| "Every day may not be good, but there's something good in every day.", |
| "The best preparation for tomorrow is doing your best today.", |
| "Believe you can, and you're halfway there."] |
|
|
| def AnalyzeText(text): |
| doc = nlp(text) |
| svg = displacy.render(doc, style='dep') |
| svg_base64_encoded = base64.b64encode(svg.encode('utf-8')).decode('utf-8') |
| nlp_html = f""" |
| <div style='display: flex; justify-content: center; align-items: center; width: 100%; overflow-x: auto;'> |
| <img src="data:image/svg+xml;base64,{svg_base64_encoded}" |
| style='display: block; max-width: 100%; height: auto; margin: auto;'/> |
| </div> |
| """ |
| pos_count = { |
| "char_count": len(text), |
| "token_count": len(doc) |
| } |
| pos_tokens = [] |
| for token in doc: |
| pos_tokens.extend([(token.text, token.pos_), (" ", None)]) |
| return pos_tokens, pos_count, nlp_html |
|
|
| def Clear(clearBtn): |
| return(constants.NLP_PROMPT, [], {}, []) |
|
|
| with gr.Blocks() as ui: |
| label = gr.Label(show_label=False, value=constants.TEXT_ANALYSIS, container=False) |
| with gr.Column(): |
| with gr.Row(): |
| with gr.Column(): |
| gr.Markdown(constants.PURPOSE_MD) |
| gr.Markdown(constants.NLP_ANALYSIS_PURPOSE) |
| with gr.Column(): |
| gr.Markdown(constants.DIRECTIONS_MD) |
| gr.Markdown(value=constants.NLP_ANALYSIS_DIRECTIONS) |
| with gr.Row(): |
| with gr.Column(): |
| inputString=gr.Textbox(placeholder=constants.NLP_PROMPT, |
| label="Input Text", lines=3,interactive=True) |
| with gr.Row(): |
| clearBtn=gr.Button(constants.CLEAR, variant="secondary") |
| submitBtn=gr.Button(constants.SUBMIT, variant="primary") |
| with gr.Column(): |
| posTags=gr.HighlightedText(label=constants.TOKENS) |
| gr.Markdown(constants.NLP_ANALYSIS_MD) |
| posCount=gr.JSON() |
| |
| inputExampleSelect = gr.Examples(input_examples,inputs=[inputString],label="Or select an example." ) |
| gr.Markdown(constants.NLP_POS_MAP_MD) |
| posTokens=gr.HTML() |
| submitBtn.click(AnalyzeText, inputs=[inputString], outputs=[posTags,posCount,posTokens]) |
| clearBtn.click(Clear, inputs=[clearBtn], outputs=[inputString,posTags,posCount,posTokens]) |
