Update session_analysis.py

session_analysis.py

@@ -447,55 +447,6 @@ def process_analysis_results(raw_analysis):
         "timestamp": datetime.now().isoformat()
     }
 
-def show_analysis_results():
-    """Display the analysis results in the dashboard"""
-    if not st.session_state.analysis_results:
-        return
-    
-    # Use the analysis results directly (they're already parsed)
-    analysis = st.session_state.analysis_results
-    
-    # Display MI Adherence Score
-    st.subheader("MI Adherence Score")
-    score = analysis.get('mi_adherence_score', 0)
-    create_gauge_chart(score)
-    
-    # Display Key Themes
-    st.subheader("Key Themes")
-    themes = analysis.get('key_themes', [])
-    if themes:
-        for theme in themes:
-            st.markdown(f"• {theme}")
-    
-    # Display Technique Usage
-    st.subheader("MI Technique Usage")
-    technique_usage = analysis.get('technique_usage', {})
-    if technique_usage:
-        fig = go.Figure(data=[
-            go.Bar(x=list(technique_usage.keys()), y=list(technique_usage.values()))
-        ])
-        fig.update_layout(title="Technique Usage Frequency")
-        st.plotly_chart(fig)
-    
-    # Display Strengths and Areas for Improvement
-    col1, col2 = st.columns(2)
-    
-    with col1:
-        st.subheader("Strengths")
-        strengths = analysis.get('strengths', [])
-        for strength in strengths:
-            st.markdown(f"✓ {strength}")
-    
-    with col2:
-        st.subheader("Areas for Improvement")
-        improvements = analysis.get('areas_for_improvement', [])
-        for improvement in improvements:
-            st.markdown(f"△ {improvement}")
-    
-    # Display Session Summary
-    st.subheader("Session Summary")
-    st.write(analysis.get('session_summary', ''))
-
 
 def show_mi_metrics_dashboard(metrics):
     st.subheader("MI Performance Dashboard")
@@ -866,7 +817,80 @@ def show_analysis_results():
                 for area in growth[0].strip().split('\n'):
                     if area.startswith('- '):
                         st.markdown(f"🔄 {area[2:]}")
+    # Technical Skills Tab
+    with tabs[1]:
+        st.subheader("OARS Technique Analysis")
+        
+        # Extract OARS counts
+        oars_pattern = r'OARS Usage Count:\n- Open Questions: (\d+)\n- Affirmations: (\d+)\n- Reflections: (\d+)\n- Summaries: (\d+)'
+        oars_match = re.search(oars_pattern, results)
+        
+        if oars_match:
+            open_q = int(oars_match.group(1))
+            affirm = int(oars_match.group(2))
+            reflect = int(oars_match.group(3))
+            summ = int(oars_match.group(4))
+            
+            # Create bar chart
+            fig = go.Figure(data=[
+                go.Bar(
+                    x=['Open Questions', 'Affirmations', 'Reflections', 'Summaries'],
+                    y=[open_q, affirm, reflect, summ],
+                    marker_color=['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728']
+                )
+            ])
+            
+            fig.update_layout(
+                title="OARS Techniques Usage",
+                xaxis_title="Technique Type",
+                yaxis_title="Frequency",
+                showlegend=False,
+                height=400
+            )
+            
+            st.plotly_chart(fig)
 
+            # Display detailed breakdown
+            col1, col2 = st.columns(2)
+            
+            with col1:
+                st.markdown("### Technique Counts")
+                st.markdown(f"🔹 **Open Questions:** {open_q}")
+                st.markdown(f"🔹 **Affirmations:** {affirm}")
+                st.markdown(f"🔹 **Reflections:** {reflect}")
+                st.markdown(f"🔹 **Summaries:** {summ}")
+
+            with col2:
+                # Calculate total and percentages
+                total = open_q + affirm + reflect + summ
+                st.markdown("### Technique Distribution")
+                st.markdown(f"🔸 **Open Questions:** {(open_q/total*100):.1f}%")
+                st.markdown(f"🔸 **Affirmations:** {(affirm/total*100):.1f}%")
+                st.markdown(f"🔸 **Reflections:** {(reflect/total*100):.1f}%")
+                st.markdown(f"🔸 **Summaries:** {(summ/total*100):.1f}%")
+
+            # Add reflection-to-question ratio
+            st.markdown("### Key Metrics")
+            if open_q > 0:
+                r_to_q = reflect / open_q
+                st.metric(
+                    label="Reflection-to-Question Ratio", 
+                    value=f"{r_to_q:.2f}",
+                    help="Target ratio is 2:1 or higher"
+                )
+
+            # Add MI best practice guidelines
+            st.markdown("### MI Best Practices")
+            st.info("""
+            📌 **Ideal OARS Distribution:**
+            - Reflections should exceed questions (2:1 ratio)
+            - Regular use of affirmations (at least 1-2 per session)
+            - Strategic use of summaries at transition points
+            - Open questions > 70% of all questions
+            """)
+
+        else:
+            st.warning("Technical skills analysis data not found in the results.")
     # Client Language Tab
     with tabs[2]:
         st.subheader("Client Language Analysis")