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