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from flask import Flask, render_template, request
import pickle
import string
import nltk
from nltk.corpus import stopwords
from nltk.stem import PorterStemmer
nltk.download('stopwords')
app = Flask(__name__)
# Load the trained model and vectorizer
model = pickle.load(open('model.pkl', 'rb'))
vectorizer = pickle.load(open('vectorizer.pkl', 'rb'))
@app.route('/')
def home():
return render_template('index.html')
@app.route('/new-url', methods=['POST'])
def new_url_predict():
comment = request.form['comment']
processed_comment = preprocess_comment(comment)
features = vectorizer.transform([processed_comment])
prediction = model.predict(features)[0]
sentiment = get_sentiment_label(prediction)
return render_template('result.html', comment=comment, sentiment=sentiment)
@app.route('/predict', methods=['POST'])
def predict():
comment = request.form['comment']
processed_comment = preprocess_comment(comment)
features = vectorizer.transform([processed_comment])
prediction = model.predict(features)[0]
sentiment = get_sentiment_label(prediction)
return render_template('result.html', comment=comment, sentiment=sentiment)
def preprocess_comment(comment):
# Comment preprocessing code here
comment = comment.lower()
comment = comment.translate(str.maketrans('', '', string.punctuation))
comment = remove_stopwords(comment)
comment = stem_words(comment)
return comment
def remove_stopwords(comment):
stopwords_english = set(stopwords.words('english'))
comment_tokens = comment.split()
comment = ' '.join([word for word in comment_tokens if word not in stopwords_english])
return comment
def stem_words(comment):
stemmer = PorterStemmer()
comment_tokens = comment.split()
comment = ' '.join([stemmer.stem(word) for word in comment_tokens])
return comment
def get_sentiment_label(prediction):
if prediction == 0:
return 'negative '
elif prediction == 1:
return 'neutral '
elif prediction == 2:
return 'positive '
else:
return 'unknown'
if __name__ == '__main__':
app.run(debug=True, port=5001)
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