Update app.py

app.py

@@ -344,167 +344,88 @@ def main():
             progress_bar.progress(40)
             st.subheader("Speech Trajectory Analysis")
             
+            # Create consistent segments and get 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)]
+            sentiment_scores, basic_emotions = analyzer.analyze_emotional_trajectory(text)
+            
+            # Get moral foundations data
+            moral_trajectories = {
+                '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])
+            
+            # Create unified figure
+            unified_fig = go.Figure()
+            
+            # Add traces for each analysis type
             viz_options = st.multiselect(
-                "Select visualizations to display:",
+                "Select analyses to display:",
                 ["Sentiment Flow", "Moral Foundations Flow", "Basic Emotions Flow"],
                 default=["Sentiment Flow"]
             )
             
-            # Create consistent segments for analyses
-            segments = analyzer.split_text(text, max_length=512)
-            num_segments = len(segments)
-            segment_labels = [f"{i+1}" for i in range(num_segments)]
-            
             if "Sentiment Flow" in viz_options:
-                st.write("### Sentiment Flow")
-                sentiment_scores, _ = analyzer.analyze_emotional_trajectory(text)  # Unpack the tuple
-                
-                trajectory_fig = go.Figure(data=go.Scatter(
+                unified_fig.add_trace(go.Scatter(
                     x=segment_labels,
                     y=sentiment_scores,
+                    name='Sentiment',
                     mode='lines+markers',
                     line=dict(color='#1f77b4', width=3),
                     marker=dict(
-                        size=8, 
-                        color=['#ff4444' if score < -0.3 else '#44ff44' if score > 0.3 else '#888888' for score in sentiment_scores],
-                        symbol='circle'
+                        size=8,
+                        color=['#ff4444' if score < -0.3 else '#44ff44' if score > 0.3 else '#888888' for score in sentiment_scores]
                     )
-                ))    
-                trajectory_fig.update_layout(
-                    title='Emotional Flow Throughout the Speech',
-                    xaxis_title='Speech Segments',
-                    yaxis_title='Emotional Tone',
-                    yaxis=dict(
-                        ticktext=['Very Negative', 'Negative', 'Neutral', 'Positive', 'Very Positive'],
-                        tickvals=[-1, -0.5, 0, 0.5, 1],
-                        range=[-1.1, 1.1],
-                        gridcolor='lightgray'
-                    ),
-                    hovermode='x unified',
-                    showlegend=False,
-                    plot_bgcolor='white',
-                    height=500
-                )
-                
-                trajectory_fig.update_traces(
-                    hovertemplate="Segment: %{x}<br>Score: %{y:.2f}<extra></extra>"
-                )
-                
-                st.plotly_chart(trajectory_fig, use_container_width=True)
+                ))
             
             if "Moral Foundations Flow" in viz_options:
-                st.write("### Moral Foundations Flow")
-                moral_trajectories = {
-                    '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])
-                
-                moral_fig = go.Figure()
                 colors = px.colors.qualitative.Set3[:5]
-                
                 for idx, (foundation, scores) in enumerate(moral_trajectories.items()):
-                    moral_fig.add_trace(go.Scatter(
+                    unified_fig.add_trace(go.Scatter(
                         x=segment_labels,
                         y=scores,
                         name=MORAL_FOUNDATIONS[foundation],
                         mode='lines+markers',
                         line=dict(color=colors[idx], width=2),
-                        marker=dict(size=6, color=colors[idx])
+                        marker=dict(size=6)
                     ))
-                
-                moral_fig.update_layout(
-                    title='Moral Foundations Flow',
-                    xaxis_title='Speech Segments',
-                    yaxis_title='Foundation Strength',
-                    yaxis=dict(range=[0, 1]),
-                    hovermode='x unified',
-                    plot_bgcolor='white',
-                    showlegend=True,
-                    height=500,
-                    legend=dict(
-                        orientation="h",
-                        yanchor="bottom",
-                        y=1.02,
-                        xanchor="right",
-                        x=1
-                    )
-                )
-                
-                moral_fig.update_traces(
-                    hovertemplate="Segment: %{x}<br>Strength: %{y:.2f}<extra></extra>"
-                )
-                
-                st.plotly_chart(moral_fig, use_container_width=True)
-        
+            
             if "Basic Emotions Flow" in viz_options:
-                st.write("### Basic Emotions Flow")
-                emotions = []
-                for segment in segments:
-                    try:
-                        result = analyzer.emotion_classifier(segment[:512])[0]
-                        emotions.append(result['label'])
-                    except:
-                        emotions.append('neutral')
-                
                 emotions_df = pd.DataFrame({
                     'Segment': segment_labels,
-                    'Emotion': emotions
+                    'Emotion': basic_emotions
                 })
-                
-                emotions_fig = px.bar(
-                    emotions_df,
-                    x='Segment',
-                    y='Emotion',
-                    color='Emotion',
-                    title='Basic Emotions Flow',
-                    color_discrete_sequence=px.colors.qualitative.Set2
-                )
-                
-                emotions_fig.update_layout(
-                    xaxis_title='Speech Segments',
-                    yaxis_title='Emotion',
-                    showlegend=True,
-                    plot_bgcolor='white',
-                    height=500,
-                    legend=dict(
-                        orientation="h",
-                        yanchor="bottom",
-                        y=1.02,
-                        xanchor="right",
-                        x=1
-                    )
-                )
-                
-                emotions_fig.update_traces(
-                    hovertemplate="Segment: %{x}<br>Emotion: %{y}<extra></extra>"
-                )
-                
-                st.plotly_chart(emotions_fig, use_container_width=True)
-        
-            # Add download button
-            if viz_options:
-                results_df = pd.DataFrame({
-                    'Segment': segment_labels,
-                    'Sentiment': sentiment_scores if "Sentiment Flow" in viz_options else None,
-                    'Emotion': emotions if "Basic Emotions Flow" in viz_options else None
-                })
-                
-                if "Moral Foundations Flow" in viz_options:
-                    for foundation, scores in moral_trajectories.items():
-                        results_df[f'Moral_{foundation}'] = scores
-                
-                csv = results_df.to_csv(index=False)
-                st.download_button(
-                    label="Download Analysis Results",
-                    data=csv,
-                    file_name="speech_analysis_results.csv",
-                    mime="text/csv"
-                )
+                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',
+                    marker_color='#2ecc71',
+                    opacity=0.6
+                ))
+            
+            unified_fig.update_layout(
+                title='Speech Analysis Flow',
+                xaxis_title='Speech Segments',
+                yaxis_title='Analysis Values',
+                plot_bgcolor='white',
+                height=600,
+                showlegend=True,
+                legend=dict(
+                    orientation="h",
+                    yanchor="bottom",
+                    y=1.02,
+                    xanchor="right",
+                    x=1
+                ),
+                hovermode='x unified'
+            )
+            
+            st.plotly_chart(unified_fig, use_container_width=True)
 
         with tab3:
             status_text.text('Analyzing Linguistic Features...')