Update app.py

app.py

@@ -675,33 +675,39 @@ def summarize_conversation(messages):
     summary_response = chat([SystemMessage(content=summary_prompt)])
     return summary_response.content.strip()
 
-# Generate insights for the selected session
-def generate_insights(session):
-    messages = session['messages']
-    if not messages:
-        return "No messages to summarize.", pd.DataFrame()
-    
-    conversation_summary = summarize_conversation(messages)
-    
-    mood_labels = ['Positive', 'Neutral', 'Negative']
-    mood_counts = [0, 0, 0]
-    for message in messages:
-        if isinstance(message, HumanMessage):
-            content = message.content.lower()
-            if any(word in content for word in ['happy', 'good', 'great', 'awesome']):
-                mood_counts[0] += 1
-            elif any(word in content for word in ['okay', 'fine', 'alright', 'normal']):
-                mood_counts[1] += 1
-            elif any(word in content for word in ['sad', 'bad', 'terrible', 'awful']):
-                mood_counts[2] += 1
-
-    mood_data = [count if count != 0 else 0.1 for count in mood_counts]  # Ensure no NaNs
-    mood_df = pd.DataFrame({
-        'Mood': mood_labels,
-        'Count': mood_data
-    })
+def generate_insights(filename):
+    with open(f'{filename}.txt', 'r') as file:
+        messages = file.read() 
+        # messages = sessions_table['messages']
+        print("\nMessages \n", messages)
+        
+        if not messages:
+            return "No messages to summarize.", pd.DataFrame()
+
+        conversation_summary = messages
+        mood_labels = ['Positive', 'Neutral', 'Negative']
+        mood_counts = [10, 0, 0]  # Example starting counts
+
+        for message in messages:
+            if isinstance(message, HumanMessage):
+                content = message.content.lower()
+                if any(word in content for word in ['happy', 'good', 'great', 'awesome']):
+                    mood_counts[0] += 1
+                elif any(word in content for word in ['okay', 'fine', 'alright', 'normal']):
+                    mood_counts[1] += 1
+                elif any(word in content for word in ['sad', 'bad', 'terrible', 'awful']):
+                    mood_counts[2] += 1
+
+        # Ensure no NaNs in mood data
+        mood_data = [count if count != 0 else 0.1 for count in mood_counts]
+
+        mood_df = pd.DataFrame({
+            'Mood': mood_labels,
+            'Count': mood_data
+        })
+
+        return messages, conversation_summary, mood_df
 
-    return conversation_summary, mood_df
 
 if page == "Today":
     # CSS Injection
@@ -1135,40 +1141,50 @@ elif page == "Therapists":
 
 elif page == "Insights":
     st.subheader("Insights")
+    
+    # Retrieve session titles for the selectbox
     session_titles = [session["title"] for session in st.session_state['sessions']]
-    selected_session_title = st.selectbox("Select a session",, session_titles)
+    selected_session_title = st.selectbox("Select a session", session_titles)
     
     if selected_session_title:
+        # Find the selected session based on the title
         selected_session = next(session for session in st.session_state['sessions'] if session["title"] == selected_session_title)
-        if selected_session and 'messages' in selected _session:
-            message, conversation_summary, mood_dt = generate_insights (selected_session_title)
-            st.write (f"Messages: {message}")
-            st.write(f" nMood: {mood_df}")
-            st.markdown ("### Conversation Summary")
+
+        if selected_session and 'messages' in selected_session:
+            # Generate insights based on the selected session
+            message, conversation_summary, mood_df = generate_insights(selected_session_title)
+
+            st.write(f"Messages: {message}")
+            st.markdown("### Conversation Summary")
             st.write(conversation_summary)
-        if not mood_df.empty:
-            st.markdown ("### Mood Analysis")
-            fig, ax = plt.subplots)
-            ax.pie(mood_df['Count'], labels=mood
-            _df['Mood'], autopct='%1.1f%%', startangle=90,
-            colors=['#4CAF50', '#FFC107', '#F44336'])
-            ax.axis ('equal')
-            st.pyplot(fig)
-            st.markdown ("### Mood Over Time")
-            mood_timeline = pd.Series([msg.content.lower) for msg in selected_session ['messages'] if
-            isinstance(msg, HumanMessage)]).apply(
-            lambda x: 1 if any(word in x for word in ['happy', 'good', 'great', 'awesome']) else ( O if any(word in x for word in ['okay', 'fine', 'alright', 'normal']) else -1
-            )
-            mood _timeline.index = pd.to_datetime(mood_timeline.index, unit='s')
-            mood_timeline_df = mood_timeline.reset_
-            _index)
-            mood.
-            _timeline_df.columns = ['Time', 'Mood']
-            st.line_chart(mood_timeline_df.set_index('Time'))
+
+            # If mood data exists, display mood analysis
+            if not mood_df.empty:
+                st.markdown("### Mood Analysis")
+                
+                # Plot a pie chart of the mood analysis
+                fig, ax = plt.subplots()
+                ax.pie(mood_df['Count'], labels=mood_df['Mood'], autopct='%1.1f%%', startangle=90, colors=['#4CAF50', '#FFC107', '#F44336'])
+                ax.axis('equal')
+                st.pyplot(fig)
+                
+                # Mood over time chart
+                st.markdown("### Mood Over Time")
+                mood_timeline = pd.Series([msg.content.lower() for msg in selected_session['messages'] if isinstance(msg, HumanMessage)]).apply(
+                    lambda x: 1 if any(word in x for word in ['happy', 'good', 'great', 'awesome']) else (
+                        0 if any(word in x for word in ['okay', 'fine', 'alright', 'normal']) else -1
+                    )
+                )
+                
+                mood_timeline.index = pd.to_datetime(mood_timeline.index, unit='s')
+                mood_timeline_df = mood_timeline.reset_index()
+                mood_timeline_df.columns = ['Time', 'Mood']
+                st.line_chart(mood_timeline_df.set_index('Time'))
+            else:
+                st.write("No mood data to display.")
         else:
-            st.write("No mood data to display.")
-    else:
-        st.write("No messages to summarize.")
+            st.write("No messages to summarize.")
+
 
 
 elif page == "How to use?":