Update app.py

app.py

@@ -279,18 +279,44 @@ def main():
             st.subheader("Emotional Trajectory")
             emotional_trajectory = analyzer.analyze_emotional_trajectory(text)
             
-            # Plotly line chart
+            # Scale values to a -1 to 1 range
+            scaled_trajectory = np.array(emotional_trajectory)
+            scaled_trajectory = np.clip(scaled_trajectory, -1, 1)
+            
+            # Create segment labels for x-axis
+            num_segments = len(scaled_trajectory)
+            segment_labels = [f"Segment {i+1}" for i in range(num_segments)]
+            
             trajectory_fig = go.Figure(data=go.Scatter(
-                y=emotional_trajectory, 
+                x=segment_labels,
+                y=scaled_trajectory,
                 mode='lines+markers',
-                name='Emotional Intensity'
+                name='Emotional Intensity',
+                line=dict(
+                    color='#1f77b4',
+                    width=3
+                ),
+                marker=dict(
+                    size=8,
+                    color='#1f77b4'
+                )
             ))
+            
             trajectory_fig.update_layout(
-                title='Speech Emotional Trajectory',
-                xaxis_title='Speech Segments',
-                yaxis_title='Emotional Intensity'
+                title='Speech Emotional Flow',
+                xaxis_title='Speech Progression',
+                yaxis_title='Sentiment',
+                yaxis=dict(
+                    ticktext=['Very Negative', 'Neutral', 'Very Positive'],
+                    tickvals=[-1, 0, 1],
+                    range=[-1, 1]
+                ),
+                hovermode='x unified',
+                showlegend=False
             )
+            
             st.plotly_chart(trajectory_fig)
+
         
         with tab3:
             st.subheader("Linguistic Complexity")
@@ -379,17 +405,70 @@ def main():
         with tab5:
             st.subheader("Advanced NLP Analysis")
             
-            # Named Entities
-            st.write("### Named Entities")
+            # Named Entities with clear explanations
+            st.write("### Key People, Organizations, and Places")
             named_entities = analyzer.detect_named_entities(text)
+            
+            # Create intuitive mapping of entity types
+            entity_type_mapping = {
+                'PER': 'Person',
+                'ORG': 'Organization',
+                'LOC': 'Location',
+                'GPE': 'Country/City',
+                'MISC': 'Miscellaneous'
+            }
+            
+            # Transform the entities dataframe
             entities_df = pd.DataFrame(named_entities)
-            st.dataframe(entities_df)
+            entities_df['entity_type'] = entities_df['entity_group'].map(entity_type_mapping)
+            entities_df['confidence'] = entities_df['score'].apply(lambda x: f"{x*100:.1f}%")
             
-            # Rhetorical Devices
-            st.write("### Rhetorical Devices")
+            # Display enhanced table
+            display_df = entities_df[['word', 'entity_type', 'confidence']].rename(columns={
+                'word': 'Name/Term',
+                'entity_type': 'Type',
+                'confidence': 'Confidence Level'
+            })
+            
+            st.dataframe(
+                display_df,
+                column_config={
+                    "Name/Term": st.column_config.TextColumn(
+                        help="The identified name or term from the text"
+                    ),
+                    "Type": st.column_config.TextColumn(
+                        help="Category of the identified term"
+                    ),
+                    "Confidence Level": st.column_config.TextColumn(
+                        help="How certain the AI is about this identification"
+                    )
+                },
+                hide_index=True
+            )
+            
+            # Enhanced Rhetorical Devices section
+            st.write("### Persuasive Language Techniques")
             rhetorical_devices = analyzer.detect_rhetorical_devices(text)
+            
+            # Create columns for better layout
+            col1, col2 = st.columns(2)
+            
+            # Define friendly names and descriptions
+            device_explanations = {
+                'analogy': 'Comparisons (using "like" or "as")',
+                'repetition': 'Repeated phrases for emphasis',
+                'metaphor': 'Symbolic comparisons',
+                'hyperbole': 'Dramatic exaggerations',
+                'rhetorical_question': 'Questions asked for effect'
+            }
+            
             for device, count in rhetorical_devices.items():
-                st.write(f"**{device.capitalize()}**: {count} instances")
+                with col1:
+                    st.metric(
+                        label=device_explanations[device],
+                        value=f"{count} times"
+                    )
+
 
 if __name__ == "__main__":
     main()