app.py

from flask import Flask, request, render_template
import nltk
import requests
from bs4 import BeautifulSoup
nltk.download('punkt')
nltk.download('averaged_perceptron_tagger')
app = Flask(__name__)

@app.route("/", methods=["GET", "POST"])
def home():
    if request.method == "POST":
        url = request.form["url"]
        html = requests.get(url).content
        soup = BeautifulSoup(html, "html.parser")
        text = soup.get_text()

        # Use nltk to perform NLP analysis on the text
        tokens = nltk.word_tokenize(text)
        tagged = nltk.pos_tag(tokens)

        # Check for the presence of certain POS tags to determine content quality
        content_quality = "High Quality"
        for word, pos in tagged:
            if pos in ["CC", "DT", "IN", "MD", "PRP", "PRP$", "TO"]:
                content_quality = "Low Quality"
                break

        return render_template("result.html", quality=content_quality)
    return render_template("home.html")

if __name__ == "__main__":
    app.run(host="0.0.0.0",port=7860,debug=True)