Update app.py

app.py

@@ -60,51 +60,51 @@ if uploaded_file:
         prompt_input = f"""Previous session context:\n{rag_context}\n\nCurrent session:\n{raw_transcript}"""
         summary = summarizer(prompt_input, max_length=256, min_length=60, do_sample=False)
 
-            # Emotion tagging
-            st.info("🎭 Extracting emotional tones...")
-            emotion_model = pipeline("text-classification", model="j-hartmann/emotion-english-distilroberta-base", return_all_scores=True)
-            emotion_scores = emotion_model(raw_transcript)
-
-            # Layout with Tabs
-
-            with tab1:
-                st.subheader("📝 Speaker-Simulated Transcript")
-                st.markdown(diarized_transcript, unsafe_allow_html=True)
-
-            with tab2:
-                st.subheader("📋 Contextual Summary")
-
-                # Insight Tracking based on previous sessions
-                insights = []
-                if "music" in raw_transcript.lower():
-                    if any("walking" in s["coping"] for s in past_sessions):
-                        insights.append("Patient previously mentioned walking as a helpful coping mechanism. This time, music is highlighted instead.")
-                if "sleep" in raw_transcript.lower():
-                    insights.append("Sleep continues to be a recurring theme across sessions.")
-        
-                final_output = f"{summary[0]['summary_text']}\n\nContextual Observations:\n" + "\n".join(insights)
-                st.write(final_output)
-
-            with tab3:
-                st.subheader("💬 Emotional Insights (Overall)")
-                if 'emotion_scores' in locals():
-                    for emo in emotion_scores[0]:
-                        st.write(f"{emo['label']}: {round(emo['score']*100, 2)}%")
-                else:
-                    st.write("No emotional data to display.")
-                
-            with tab4:
-                st.subheader("📈 Emotional Trends Over Time")
-
-                session_dates = ["2024-04-01", "2024-04-08", "2024-04-15", "2024-04-22"]
-                anxiety_scores = [70, 65, 55, 40]
-                sadness_scores = [30, 20, 25, 15]
+        # Emotion tagging
+        st.info("🎭 Extracting emotional tones...")
+        emotion_model = pipeline("text-classification", model="j-hartmann/emotion-english-distilroberta-base", return_all_scores=True)
+        emotion_scores = emotion_model(raw_transcript)
+
+        # Layout with Tabs
+
+        with tab1:
+            st.subheader("📝 Speaker-Simulated Transcript")
+            st.markdown(diarized_transcript, unsafe_allow_html=True)
+
+        with tab2:
+            st.subheader("📋 Contextual Summary")
+
+            # Insight Tracking based on previous sessions
+            insights = []
+            if "music" in raw_transcript.lower():
+                if any("walking" in s["coping"] for s in past_sessions):
+                    insights.append("Patient previously mentioned walking as a helpful coping mechanism. This time, music is highlighted instead.")
+            if "sleep" in raw_transcript.lower():
+                insights.append("Sleep continues to be a recurring theme across sessions.")
+    
+            final_output = f"{summary[0]['summary_text']}\n\nContextual Observations:\n" + "\n".join(insights)
+            st.write(final_output)
+
+        with tab3:
+            st.subheader("💬 Emotional Insights (Overall)")
+            if 'emotion_scores' in locals():
+                for emo in emotion_scores[0]:
+                    st.write(f"{emo['label']}: {round(emo['score']*100, 2)}%")
+            else:
+                st.write("No emotional data to display.")
             
-                fig = go.Figure()
-                fig.add_trace(go.Scatter(x=session_dates, y=anxiety_scores, mode='lines+markers', name='Anxiety'))
-                fig.add_trace(go.Scatter(x=session_dates, y=sadness_scores, mode='lines+markers', name='Sadness'))
-                fig.update_layout(title='Emotional Trends', xaxis_title='Date', yaxis_title='Score (%)')
-                st.plotly_chart(fig)
+        with tab4:
+            st.subheader("📈 Emotional Trends Over Time")
+
+            session_dates = ["2024-04-01", "2024-04-08", "2024-04-15", "2024-04-22"]
+            anxiety_scores = [70, 65, 55, 40]
+            sadness_scores = [30, 20, 25, 15]
+        
+            fig = go.Figure()
+            fig.add_trace(go.Scatter(x=session_dates, y=anxiety_scores, mode='lines+markers', name='Anxiety'))
+            fig.add_trace(go.Scatter(x=session_dates, y=sadness_scores, mode='lines+markers', name='Sadness'))
+            fig.update_layout(title='Emotional Trends', xaxis_title='Date', yaxis_title='Score (%)')
+            st.plotly_chart(fig)
 
             # Export Button
             st.subheader("📥 Export Session Report")