app.py

import nltk
import re
import pandas as pd
from nltk.corpus import stopwords
from nltk.sentiment.vader import SentimentIntensityAnalyzer

# Load the required NLTK corpora
nltk.download("vader_lexicon")
nltk.download("stopwords")

# Initialize the SentimentIntensityAnalyzer
sia = SentimentIntensityAnalyzer()

# Define a function to clean the text
def clean_text(text):
    text = re.sub("[^a-zA-Z]", " ", text)
    text = text.lower()
    text = text.split()
    text = [word for word in text if word not in set(stopwords.words("english"))]
    text = " ".join(text)
    return text

# Define a function to calculate the sentiment score
def sentiment_score(text):
    score = sia.polarity_scores(text)
    return score["compound"]

# Read the data into a pandas DataFrame
df = pd.read_csv("content.csv")

# Clean the text and calculate the sentiment score
df["Clean_Text"] = df["Content"].apply(lambda x: clean_text(x))
df["Sentiment_Score"] = df["Clean_Text"].apply(lambda x: sentiment_score(x))

# Classify the content quality based on the sentiment score
df["Content_Quality"] = df["Sentiment_Score"].apply(lambda x: "Good" if x >= 0.5 else "Bad")

# Print the final result
print(df)

from flask import Flask, request, render_template

app = Flask(__name__)

@app.route("/", methods=["GET", "POST"])
def index():
    if request.method == "POST":
        content = request.form["content"]
        clean_text = clean_text(content)
        sentiment_score = sentiment_score(clean_text)
        content_quality = "Good" if sentiment_score >= 0.5 else "Bad"
        return render_template("index.html", content_quality=content_quality)
    return render_template("index.html")

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