Create app.py

app.py

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+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)