Update app.py

app.py

@@ -1,62 +1,72 @@
-from flask import Flask, request, jsonify
-import nltk
-from nltk.sentiment import SentimentIntensityAnalyzer
-from transformers import AutoTokenizer, AutoModelForSequenceClassification
-from scipy.special import softmax
-import pandas as pd
-
-# Initialize Flask app
-app = Flask(__name__)
-
-# Load NLTK's VADER
-nltk.download('vader_lexicon')
-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')
-
-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]
-    }
-
-    return {**vader_result, **roberta_result}
-
-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'])
-    response = {
-        'sentiment_scores': sentiment_scores,
-        'star_rating': star_rating
-    }
-    return jsonify(response)
-
-if __name__ == '__main__':
-    app.run(host='0.0.0.0', port=5000)
+from flask import Flask, request, jsonify
+import nltk
+from nltk.sentiment import SentimentIntensityAnalyzer
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+from scipy.special import softmax
+import pandas as pd
+
+# Initialize Flask app
+app = Flask(__name__)
+
+# Load NLTK's VADER
+nltk.download('vader_lexicon')
+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')
+
+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]
+    }
+
+    return {**vader_result, **roberta_result}
+
+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'])
+    
+    # Log the sentiment scores and star rating
+    app.logger.info("Sentiment scores: %s", sentiment_scores)
+    app.logger.info("Star rating: %s", star_rating)
+    
+    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)
+
+
+if __name__ == '__main__':
+    app.run(host='0.0.0.0', port=5000)