| import gradio as gr |
| import os |
| os.system('python -m spacy download en_core_web_sm') |
| import spacy |
| from spacy import displacy |
|
|
| nlp = spacy.load("en_core_web_sm") |
|
|
| def text_analysis(text): |
| doc = nlp(text) |
| html = displacy.render(doc, style="dep", page=True) |
| html = ( |
| "" |
| + html |
| + "" |
| ) |
| pos_count = { |
| "char_count": len(text), |
| "token_count": 0, |
| } |
| pos_tokens = [] |
|
|
| for token in doc: |
| pos_tokens.extend([(token.text, token.pos_), (" ", None)]) |
|
|
| return pos_tokens, pos_count, html |
|
|
| demo = gr.Interface( |
| text_analysis, |
| gr.Textbox(placeholder="Enter sentence here..."), |
| ["highlight", "json", "html"], |
| examples=[ |
| ["All my love,all my love is free"], |
| ["I want it,I got it, yeah"], |
| ["And for that,i say Thank u next"] |
| ], |
| ) |
|
|
| demo.launch() |
|
|
