app.py

import gradio as gr
from kwextractor import KeyWordExtractor
import csv, io


kw_ex=KeyWordExtractor()
csv_encoded=io.StringIO()
writer = csv.writer(csv_encoded)


def generate_kws(context,num_kw, kw_ngs):
    
    context=context.strip()
    if context:
        try:
            num_kw=int(num_kw)
        except ValueError:
            num_kw=None
        try:
            kw_ngs=int(kw_ngs)
        except ValueError:
            kw_ngs=None
        #csv_encoded.truncate(0)
        #writer.writerow([context])
        #context=csv_encoded.getvalue()
        return kw_ex.extract(context, num_kw, kw_ngs) or ""
    else:
        raise gr.Error("Please enter text in inputbox!!!!")
        


inputs=gr.Textbox(value="", lines=5, label="Input Context",elem_id="inp_div")
nkws = gr.Textbox(label="Number of keywords to extract",default="3",elem_id="inp_div")
kw_ngs= gr.Textbox(label="Maximum number of ngrams per keyword",default="3",elem_id="inp_div")
outputs=gr.Textbox(label="Generated Keywords",lines=6,elem_id="inp_div")

demo = gr.Interface(
    generate_kws,
    [inputs,nkws,kw_ngs],
    outputs,
    title="Keyword Extraction Model",
    css=".gradio-container {background-color: lightgray} #inp_div {background-color: #7FB3D5;}",
    article="""<p style='text-align: center;'>Feel free to give us your <a href="https://www.pragnakalp.com/contact/" target="_blank">feedback</a> on this Keyword Extraction demo.</p>
    """
    
)
demo.launch()