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from flask import Flask, request, jsonify
import nltk
from nltk.sentiment import SentimentIntensityAnalyzer
from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
from scipy.special import softmax
import torch
# Initialize Flask app
app = Flask(__name__)
# Check if the VADER lexicon is already downloaded
try:
nltk.data.find('sentiment/vader_lexicon.zip')
except LookupError:
nltk.download('vader_lexicon')
# Load NLTK's VADER lexicon once
sia = SentimentIntensityAnalyzer()
# Lazy load transformer model and tokenizer
def get_transformer_pipeline():
tokenizer = AutoTokenizer.from_pretrained('cardiffnlp/twitter-roberta-base-sentiment')
model = AutoModelForSequenceClassification.from_pretrained('cardiffnlp/twitter-roberta-base-sentiment')
nlp = pipeline('sentiment-analysis', model=model, tokenizer=tokenizer)
return nlp
def analyze_sentiment(text):
# VADER sentiment analysis
vader_result = sia.polarity_scores(text)
# RoBERTa sentiment analysis
nlp = get_transformer_pipeline()
roberta_result = nlp(text)[0]
sentiment_scores = {
'vader_neg': vader_result['neg'],
'vader_neu': vader_result['neu'],
'vader_pos': vader_result['pos'],
'roberta_neg': roberta_result['score'] if roberta_result['label'] == 'LABEL_0' else 0,
'roberta_neu': roberta_result['score'] if roberta_result['label'] == 'LABEL_1' else 0,
'roberta_pos': roberta_result['score'] if roberta_result['label'] == 'LABEL_2' else 0
}
return sentiment_scores
def sentiment_to_stars(sentiment_score):
thresholds = [0.2, 0.4, 0.6, 0.8]
if sentiment_score <= thresholds[0]:
return 1
elif sentiment_score <= thresholds[1]:
return 2
elif sentiment_score <= thresholds[2]:
return 3
elif sentiment_score <= thresholds[3]:
return 4
else:
return 5
@app.route('/analyze', methods=['POST'])
def analyze():
data = request.json
text = data['text']
sentiment_scores = analyze_sentiment(text)
star_rating = sentiment_to_stars(sentiment_scores['roberta_pos'])
# Convert float32 values to standard float
sentiment_scores = {k: float(v) for k, v in sentiment_scores.items()}
response = {
'sentiment_scores': sentiment_scores,
'star_rating': star_rating
}
return jsonify(response)
# Health check endpoint
@app.route('/')
def health_check():
return jsonify({"status": "OK"}), 200
if __name__ == "__main__":
app.run(host="0.0.0.0", port=5000)
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