Update app.py

app.py

@@ -126,39 +126,60 @@ class SpeechAnalyzer:
         }
         
         return aggregated_scores
-
+    
     def analyze_emotional_trajectory(self, text, window_size=5):
-        """Enhanced emotional trajectory analysis using sentence-level processing"""
+        """Enhanced emotional trajectory analysis using batch processing"""
         segments = self.split_text(text, max_length=512)
         sentiment_scores = []
         
         for segment in segments:
-            # Split into sentences using spaCy or NLTK
+            # Split into sentences using NLTK
             sentences = nltk.sent_tokenize(segment)
             
             # Process sentences in batches
             batch_size = 64
             segment_scores = []
+            
             for i in range(0, len(sentences), batch_size):
                 batch = sentences[i:i+batch_size]
-                results = self.sentiment_pipeline(batch)
-                batch_scores = [result['score'] for result in results]
-                segment_scores.extend(batch_scores)
+                
+                try:
+                    # Pad or truncate sentences to ensure consistent length
+                    batch = [sent[:512] for sent in batch]  # Truncate to max model input
+                    
+                    # Ensure all sentences are strings and non-empty
+                    batch = [sent if sent.strip() else "." for sent in batch]
+                    
+                    results = self.sentiment_pipeline(batch)
+                    
+                    # Process batch results
+                    batch_scores = []
+                    for result in results:
+                        # Convert to signed score (-1 to 1 range)
+                        score = result['score']
+                        score = score * 2 - 1 if result['label'] == 'POSITIVE' else -score
+                        batch_scores.append(score)
+                    
+                    segment_scores.extend(batch_scores)
+                
+                except Exception as e:
+                    print(f"Batch processing error: {e}")
+                    # Fallback to individual processing if batch fails
+                    for sent in batch:
+                        try:
+                            result = self.sentiment_pipeline(sent)[0]
+                            score = result['score']
+                            score = score * 2 - 1 if result['label'] == 'POSITIVE' else -score
+                            segment_scores.append(score)
+                        except:
+                            segment_scores.append(0)
             
+            # Calculate average score for the segment
             avg_score = np.mean(segment_scores) if segment_scores else 0
             sentiment_scores.append(avg_score)
         
-        # Normalize scores
-        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)