Update app.py

app.py

@@ -76,7 +76,7 @@ class SpeechAnalyzer:
         self.ner_model = AutoModelForTokenClassification.from_pretrained("dslim/bert-base-NER")
         self.ner_pipeline = pipeline("ner", model=self.ner_model, tokenizer=self.ner_tokenizer)
 
-    def split_text(self, text, max_length=256, overlap=50):
+    def split_text(self, text, max_length=512):
         """Split long text into overlapping segments"""
         words = text.split()
         segments = []
@@ -128,7 +128,7 @@ class SpeechAnalyzer:
 
     def analyze_emotional_trajectory(self, text, window_size=5):
         """Enhanced emotional trajectory analysis using sentence-level processing"""
-        segments = self.split_text(text, max_length=256)
+        segments = self.split_text(text, max_length=512)
         sentiment_scores = []
         
         for segment in segments:
@@ -315,7 +315,7 @@ def main():
             col1, col2 = st.columns(2)
             
             # Create consistent segments for both analyses
-            segments = analyzer.split_text(text, max_length=256)
+            segments = analyzer.split_text(text, max_length=512)
             num_segments = len(segments)
             segment_labels = [f"{i+1}" for i in range(num_segments)]