Update app.py

app.py

@@ -276,47 +276,89 @@ def main():
                 st.write(f"**{MORAL_FOUNDATIONS[foundation]}**: {score:.2%}")
                 
         with tab2:
-            st.subheader("Emotional Trajectory")
-            emotional_trajectory = analyzer.analyze_emotional_trajectory(text)
+            st.subheader("Speech Trajectory Analysis")
             
-            # Normalize and scale the sentiment values
-            scaled_trajectory = np.array(emotional_trajectory) * 2 - 1  # Scale to [-1, 1]
-            
-            # Create segment labels
-            num_segments = len(scaled_trajectory)
-            segment_labels = [f"{i+1}" for i in range(num_segments)]
+            col1, col2 = st.columns(2)
             
-            trajectory_fig = go.Figure(data=go.Scatter(
-                x=segment_labels,
-                y=scaled_trajectory,
-                mode='lines+markers',
-                line=dict(
-                    color='#1f77b4',
-                    width=3
-                ),
-                marker=dict(
-                    size=8,
-                    color='#1f77b4',
-                    symbol='circle'
+            with col1:
+                st.write("### Emotional Flow")
+                # Get raw sentiment scores
+                sentiment_results = [analyzer.sentiment_pipeline(segment)[0] for segment in analyzer.split_text(text)]
+                
+                # Convert sentiment labels to numerical values
+                sentiment_values = [1 if result['label'] == 'POSITIVE' else -1 for result in sentiment_results]
+                
+                # Create segment labels
+                num_segments = len(sentiment_values)
+                segment_labels = [f"{i+1}" for i in range(num_segments)]
+                
+                trajectory_fig = go.Figure(data=go.Scatter(
+                    x=segment_labels,
+                    y=sentiment_values,
+                    mode='lines+markers',
+                    line=dict(
+                        color='#1f77b4',
+                        width=3
+                    ),
+                    marker=dict(
+                        size=8,
+                        color='#1f77b4',
+                        symbol='circle'
+                    )
+                ))
+                
+                trajectory_fig.update_layout(
+                    title='Emotional Flow Throughout the Speech',
+                    xaxis_title='Speech Segments',
+                    yaxis_title='Emotional Tone',
+                    yaxis=dict(
+                        ticktext=['Very Negative', 'Neutral', 'Very Positive'],
+                        tickvals=[-1, 0, 1],
+                        range=[-1.1, 1.1],
+                        gridcolor='lightgray'
+                    ),
+                    hovermode='x unified',
+                    showlegend=False,
+                    plot_bgcolor='white'
                 )
-            ))
-            
-            trajectory_fig.update_layout(
-                title='Emotional Flow Throughout the Speech',
-                xaxis_title='Speech Segments',
-                yaxis_title='Emotional Tone',
-                yaxis=dict(
-                    ticktext=['Very Negative', 'Neutral', 'Very Positive'],
-                    tickvals=[-1, 0, 1],
-                    range=[-1.1, 1.1],
-                    gridcolor='lightgray'
-                ),
-                hovermode='x unified',
-                showlegend=False,
-                plot_bgcolor='white'
-            )
+                
+                st.plotly_chart(trajectory_fig)
             
-            st.plotly_chart(trajectory_fig)
+            with col2:
+                st.write("### Moral Foundations Flow")
+                # Get moral scores per segment
+                segments = analyzer.split_text(text)
+                moral_trajectories = {foundation: [] for foundation in MORAL_FOUNDATIONS}
+                
+                for segment in segments:
+                    segment_scores = analyzer.analyze_moral_foundations(segment)
+                    for foundation, score in segment_scores.items():
+                        moral_trajectories[foundation].append(score)
+                
+                moral_fig = go.Figure()
+                for foundation, scores in moral_trajectories.items():
+                    moral_fig.add_trace(go.Scatter(
+                        x=list(range(1, len(scores) + 1)),
+                        y=scores,
+                        name=MORAL_FOUNDATIONS[foundation],
+                        mode='lines+markers'
+                    ))
+                
+                moral_fig.update_layout(
+                    title='Moral Foundations Flow',
+                    xaxis_title='Speech Segments',
+                    yaxis_title='Foundation Strength',
+                    hovermode='x unified',
+                    plot_bgcolor='white',
+                    showlegend=True,
+                    legend=dict(
+                        yanchor="top",
+                        y=0.99,
+                        xanchor="left",
+                        x=1.05
+                    )
+                )
+                st.plotly_chart(moral_fig)
         
         with tab3:
             st.subheader("Linguistic Analysis")