Update app.py

app.py

@@ -1,37 +1,47 @@
 from flask import Flask, request, jsonify
 import nltk
 from nltk.sentiment import SentimentIntensityAnalyzer
-from transformers import AutoTokenizer, AutoModelForSequenceClassification
+from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
 from scipy.special import softmax
-import pandas as pd
+import torch
 
 # Initialize Flask app
 app = Flask(__name__)
 
-# Load NLTK's VADER
-nltk.download('vader_lexicon')
+# 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()
 
-# Load the transformer model and tokenizer (e.g., RoBERTa)
-tokenizer = AutoTokenizer.from_pretrained('cardiffnlp/twitter-roberta-base-sentiment')
-model = AutoModelForSequenceClassification.from_pretrained('cardiffnlp/twitter-roberta-base-sentiment')
+# 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
-    encoded_input = tokenizer(text, return_tensors='pt')
-    output = model(**encoded_input)
-    scores = output[0][0].detach().numpy()
-    scores = softmax(scores)
-    roberta_result = {
-        'roberta_neg': scores[0],
-        'roberta_neu': scores[1],
-        'roberta_pos': scores[2]
+    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 {**vader_result, **roberta_result}
+    return sentiment_scores
 
 def sentiment_to_stars(sentiment_score):
     thresholds = [0.2, 0.4, 0.6, 0.8]
@@ -52,21 +62,21 @@ def analyze():
     text = data['text']
     sentiment_scores = analyze_sentiment(text)
     star_rating = sentiment_to_stars(sentiment_scores['roberta_pos'])
-    
-    # Log the sentiment scores and star rating
-    app.logger.info("Sentiment scores: %s", sentiment_scores)
-    app.logger.info("Star rating: %s", star_rating)
-    
+
+    # 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
     }
-    
-    # Log the complete response before returning it
-    app.logger.info("Complete response: %s", response)
-    
+
     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)
+if __name__ == "__main__":
+    app.run(host="0.0.0.0", port=5000)