import os
os.system('pip install https://huggingface.co/kormilitzin/en_core_med7_lg/resolve/main/en_core_med7_lg-any-py3-none-any.whl')

import gradio as gr
from spacy import displacy
import spacy

med7 = spacy.load("en_core_med7_lg")

def get_med7_ent(text):

    # create distinct colours for labels
    col_dict = {}
    seven_colours = ['#e6194B', '#3cb44b', '#ffe119', '#ffd8b1', '#f58231', '#f032e6', '#42d4f4']
    for label, colour in zip(med7.pipe_labels['ner'], seven_colours):
        col_dict[label] = colour

    options = {'ents': med7.pipe_labels['ner'], 'colors':col_dict}

#     text = 'A patient was prescribed Magnesium hydroxide 400mg/5ml suspension PO of total 30ml bid for the next 5 days.'
    doc = med7(text)

#     spacy.displacy.render(doc, style='ent', jupyter=True, options=options)

#     [(ent.text, ent.label_) for ent in doc.ents]
    
    html = displacy.render(doc, style='ent', jupyter=True, options=options)
    print([(ent.text, ent.label_) for ent in doc.ents])
    return html

exp=["A patient was prescribed Magnesium hydroxide 400mg/5ml suspension PO of total 30ml bid for the next 5 days."]

desc="Med7 — an information extraction model for clinical natural language processing"

inp=gr.inputs.Textbox(lines=5, placeholder=None, default="", label="text to extract Med7 Entities")
out=gr.outputs.HTML(label=None)

iface = gr.Interface(fn=get_med7_ent, inputs=inp, outputs=out,examples=exp,article=desc,title="Med7",theme="huggingface",layout='horizontal')
iface.launch()