import spacy
import gradio as gr
from spacy import displacy
from pdfminer.high_level import extract_text

nlp = spacy.load("en_cv_info_extr")

colors = {}
for label in nlp.get_pipe('ner').labels:
  colors[label] = "linear-gradient(90deg, #aa9cfc, #fc9ce7)"

options = {"ents": list(nlp.get_pipe('ner').labels), "colors": colors}

def resume_ner(file):
    resume = extract_text(file.name)
    doc = nlp(resume)
    html = displacy.render(doc, style="ent", page=True, options=options)
    html = (
        "<div style='max-width:100%; max-height:500px; overflow:auto'>"
        + html
        + "</div>"
    )
    return html

demo = gr.Interface(
    resume_ner,
    gr.File(file_types=[".pdf"]),
    ["html"],
)

demo.launch()