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)
    html = displacy.render(doc, style="dep", page=True)
    html = (
        "<div style='max-width:100%; max-height:360px; overflow:auto'>"
        + html
        + "</div>"
    )
    pos_count = {
        "char_count": len(text),
        "token_count": 0,
    }
    pos_tokens = []
  
    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, html

demo = gr.Interface(
    text_analysis,
    gr.Textbox(placeholder="Enter sentence here..."),
    [gr.Dataframe(), "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()