Update app.py

app.py

@@ -125,30 +125,37 @@ class SpeechAnalyzer:
         return aggregated_scores
 
     def analyze_emotional_trajectory(self, text, window_size=5, ngram_size=3):
-        """Enhanced emotional trajectory analysis using n-grams and relative scoring"""
+        """Enhanced emotional trajectory analysis using efficient batch processing"""
         segments = self.split_text(text, max_length=512)
         sentiment_scores = []
         
+        # Process n-grams in larger batches while keeping full text
+        batch_size = 32
         for segment in segments:
             words = segment.split()
             ngrams = [' '.join(words[i:i+ngram_size]) for i in range(0, len(words)-ngram_size+1)]
             
+            # Process full ngrams in batches
             segment_scores = []
-            for ngram in ngrams:
-                result = self.sentiment_pipeline(ngram)[0]
-                segment_scores.append(result['score'])
+            for i in range(0, len(ngrams), batch_size):
+                batch = ngrams[i:i+batch_size]
+                results = self.sentiment_pipeline(batch)
+                batch_scores = [result['score'] for result in results]
+                segment_scores.extend(batch_scores)
             
             avg_score = np.mean(segment_scores) if segment_scores else 0
             sentiment_scores.append(avg_score)
         
-        # Normalize scores
-        min_score = min(sentiment_scores)
-        max_score = max(sentiment_scores)
-        score_range = max_score - min_score
-        if score_range > 0:
-            sentiment_scores = [(s - min_score) / score_range * 2 - 1 for s in sentiment_scores]
+        # Normalize scores to preserve full range of sentiment
+        if sentiment_scores:
+            min_score = min(sentiment_scores)
+            max_score = max(sentiment_scores)
+            score_range = max_score - min_score
+            if score_range > 0:
+                sentiment_scores = [(s - min_score) / score_range * 2 - 1 for s in sentiment_scores]
         
         return sentiment_scores
+
     def detect_named_entities(self, text):
         """Detect named entities in the text"""
         entities = self.ner_pipeline(text)