Update app.py

app.py

@@ -282,6 +282,21 @@ class SpeechAnalyzer:
             G.nodes[node]['pos'] = pos[node]
         
         return G
+        
+@st.cache_data
+def process_all_analyses(text, analyzer):
+    segments = analyzer.split_text(text, max_length=512)
+    num_segments = len(segments)
+    segment_labels = [f"{i+1}" for i in range(num_segments)]
+    
+    sentiment_scores, basic_emotions = analyzer.analyze_emotional_trajectory(text)
+    moral_trajectories = {foundation: [] for foundation in ['care', 'fairness', 'loyalty', 'authority', 'sanctity']}
+    for segment in segments:
+        moral_scores = analyzer.analyze_moral_foundations(segment)
+        for foundation in moral_trajectories.keys():
+            moral_trajectories[foundation].append(moral_scores[foundation])
+            
+    return segments, segment_labels, sentiment_scores, basic_emotions, moral_trajectories
 def main():
     st.title("🗣️ Political Text Analysis Toolkit")
     
@@ -344,19 +359,8 @@ def main():
             progress_bar.progress(40)
             st.subheader("Speech Trajectory Analysis")
             
-            # Process ALL data upfront
-            segments = analyzer.split_text(text, max_length=512)
-            num_segments = len(segments)
-            segment_labels = [f"{i+1}" for i in range(num_segments)]
-            
-            # Get all analysis data at once
-            with st.spinner('Processing all analyses...'):
-                sentiment_scores, basic_emotions = analyzer.analyze_emotional_trajectory(text)
-                moral_trajectories = {foundation: [] for foundation in ['care', 'fairness', 'loyalty', 'authority', 'sanctity']}
-                for segment in segments:
-                    moral_scores = analyzer.analyze_moral_foundations(segment)
-                    for foundation in moral_trajectories.keys():
-                        moral_trajectories[foundation].append(moral_scores[foundation])
+            # Get cached data
+            segments, segment_labels, sentiment_scores, basic_emotions, moral_trajectories = process_all_analyses(text, analyzer)
             
             # Create unified figure
             unified_fig = go.Figure()
@@ -401,9 +405,12 @@ def main():
                 unified_fig.add_trace(go.Bar(
                     x=segment_labels,
                     y=[1 if e == 'joy' else 0.5 if e == 'neutral' else -0.5 for e in basic_emotions],
-                    name='Emotions',
+                    name=f'Emotions ({", ".join(set(basic_emotions))})',
                     marker_color='#2ecc71',
-                    opacity=0.6
+                    opacity=0.6,
+                    hovertemplate="Segment: %{x}<br>Emotion: " + 
+                                  "<br>".join([f"{e}" for e in basic_emotions]) +
+                                  "<extra></extra>"
                 ))
             
             unified_fig.update_layout(