app.py

import gradio as gr
import os
os.system('python -m spacy download en_core_web_sm')
import spacy
from spacy import displacy
import pandas as pd

nlp = spacy.load("en_core_web_sm")

def text_analysis(text):
    doc = nlp(text)
    dependency_parsing = displacy.render(doc, style="dep", page=True)
    named_entity_recognition = displacy.render(doc, style="ent",jupyter=False, page=True)
    
    visual1 = (
        "<div style='max-width:100%; overflow:auto'>"
        + dependency_parsing
        + "</div>"
    )
    
    visual2 = (
        "<div style='max-width:100%; overflow:auto'>"
        + named_entity_recognition
        + "</div>"
    )
  
    rows = []
    for token in doc:
        rows.append((token.text, token.lemma_, token.pos_, token.tag_, token.dep_,
            token.shape_, token.is_alpha, token.is_stop))
  
    table = pd.DataFrame(rows, columns = ["TEXT", "LEMMA","POS","TAG","DEP","SHAPE","ALPHA","STOP"])

    return table, visual2, visual1


demo = gr.Interface(
    text_analysis,
    inputs = gr.Textbox(placeholder="Enter sentence here..."),
    outputs = [ gr.Dataframe(), "html", "html"],
    examples=[
        ["Data Science Dojo is the leading platform providing training in data science, data analytics, and machine learning."],
        ["It's the best time to execute the plan."],
    ],
)

demo.launch()