Update session_analysis.py

session_analysis.py

@@ -317,66 +317,76 @@ def show_manual_input_form():
             analyze_session_content(st.session_state.current_transcript)
 
 
-def analyze_session_content(content):
+def analyze_session_content(transcript):
+    """Analyze the session transcript using Gemini"""
     try:
-        # Configure Gemini model
+        if not transcript:
+            st.warning("Please provide a transcript for analysis.")
+            return
+
+        # Configure the model
         model = genai.GenerativeModel('gemini-pro')
         
-        # Prepare analysis prompt
-        analysis_prompt = f"""
-        Analyze the following therapy session using MI principles and provide a comprehensive evaluation:
-
-        Session Content:
-        {content}
-
-        Please provide detailed analysis including:
-        1. MI Adherence Assessment:
-           - OARS implementation
-           - Change talk identification
-           - Resistance management
-           - MI spirit adherence
-
-        2. Technical Skills Evaluation:
-           - Reflection quality and frequency
-           - Question-to-reflection ratio
-           - Open vs. closed questions
-           - Affirmations and summaries
-
-        3. Client Language Analysis:
-           - Change talk instances
-           - Sustain talk patterns
-           - Commitment language
-           - Resistance patterns
-
-        4. Session Flow Analysis:
-           - Engagement level
-           - Focus maintenance
-           - Evocation quality
-           - Planning effectiveness
-
-        5. Recommendations:
-           - Strength areas
-           - Growth opportunities
-           - Suggested interventions
-           - Next session planning
-
-        Format the analysis with clear sections and specific examples from the session.
+        # Structured prompt for MI analysis
+        prompt = f"""
+        As an MI (Motivational Interviewing) expert, analyze this therapy session transcript and provide detailed feedback in the following format:
+
+        === MI Adherence ===
+        Score: [Provide a score from 0-100]
+        Strengths:
+        - [List 3 specific strengths with examples]
+        Areas for Growth:
+        - [List 3 specific areas needing improvement with examples]
+
+        === Technical Analysis ===
+        OARS Usage Count:
+        - Open Questions: [number]
+        - Affirmations: [number]
+        - Reflections: [number]
+        - Summaries: [number]
+
+        === Client Language Analysis ===
+        Change Talk Examples:
+        - [List 3-4 specific quotes showing change talk]
+        Sustain Talk Examples:
+        - [List 2-3 specific quotes showing sustain talk]
+        Change Talk/Sustain Talk Ratio: [X:Y]
+
+        === Session Flow ===
+        Key Moments:
+        1. [Describe key moment 1]
+        2. [Describe key moment 2]
+        3. [Describe key moment 3]
+
+        Therapeutic Process:
+        - [Describe how the session progressed]
+        - [Note any significant shifts]
+
+        === Recommendations ===
+        Priority Actions:
+        1. [Specific recommendation 1]
+        2. [Specific recommendation 2]
+        3. [Specific recommendation 3]
+
+        Development Strategies:
+        - [Practical strategy 1]
+        - [Practical strategy 2]
+
+        Analyze this transcript:
+        {transcript}
         """
+
+        # Generate response
+        response = model.generate_content(prompt)
         
-        # Generate analysis
-        response = model.generate_content(analysis_prompt)
-        
-        # Process and structure the analysis results
-        analysis_results = process_analysis_results(response.text)
-        
-        # Store results in session state
-        st.session_state.analysis_results = analysis_results
-        
-        # Show success message
-        st.success("Analysis completed successfully!")
+        # Store results
+        st.session_state.analysis_results = response.text
         
+        return True
+
     except Exception as e:
-        st.error(f"Error during analysis: {str(e)}")
+        st.error(f"Error in analysis: {str(e)}")
+        return False
 
 
 def generate_transcript(audio_content):
@@ -795,78 +805,16 @@ def format_session_data(session_data):
     """
     return formatted_text
 
-def analyze_session_content(transcript):
-    """Analyze the session transcript using Gemini"""
-    try:
-        if not transcript:
-            st.warning("Please provide a transcript for analysis.")
-            return
-
-        # Configure the model
-        model = genai.GenerativeModel('gemini-pro')
-        
-        # Prepare prompt with structured output request
-        prompt = """
-        Analyze this MI (Motivational Interviewing) session transcript and provide analysis in the following structured format:
-
-        MI Adherence Score: [0-100]
-
-        Key Themes:
-        - [Theme 1]
-        - [Theme 2]
-        - [Theme 3]
-
-        Technique Usage:
-        - Open Questions: [count]
-        - Reflections: [count]
-        - Affirmations: [count]
-        - Summaries: [count]
-
-        Strengths:
-        - [Strength 1]
-        - [Strength 2]
-        - [Strength 3]
-
-        Areas for Improvement:
-        - [Area 1]
-        - [Area 2]
-        - [Area 3]
-
-        Change Talk Instances:
-        - [Example 1]
-        - [Example 2]
-
-        Sustain Talk Instances:
-        - [Example 1]
-        - [Example 2]
-
-        Session Summary:
-        [Brief summary of the session flow and key moments]
-
-        Transcript to analyze:
-        {transcript}
-        """
-        
-        # Generate response
-        response = model.generate_content(prompt)
-        
-        # Store results in session state
-        st.session_state.analysis_results = response.text
-        
-        # Trigger results display
-        show_analysis_results()
-        
-    except Exception as e:
-        st.error(f"Error analyzing session: {str(e)}")
-
 
 def show_analysis_results():
-    """Display the analysis results in the dashboard"""
+    """Display the analysis results in organized tabs"""
     if 'analysis_results' not in st.session_state or not st.session_state.analysis_results:
-        st.info("No analysis results available. Please analyze a transcript first.")
+        st.info("Please analyze a transcript first.")
         return
 
-    # Create tabs for different aspects of analysis
+    results = st.session_state.analysis_results
+
+    # Create tabs
     tabs = st.tabs([
         "MI Adherence",
         "Technical Skills",
@@ -877,10 +825,10 @@ def show_analysis_results():
 
     # MI Adherence Tab
     with tabs[0]:
-        st.subheader("MI Adherence Score")
+        st.subheader("MI Adherence Analysis")
         
-        # Extract score from analysis results
-        score_match = re.search(r'MI Adherence Score:\s*(\d+)', st.session_state.analysis_results)
+        # Extract score
+        score_match = re.search(r'Score:\s*(\d+)', results)
         if score_match:
             score = int(score_match.group(1))
             
@@ -889,120 +837,96 @@ def show_analysis_results():
                 mode="gauge+number",
                 value=score,
                 domain={'x': [0, 1], 'y': [0, 1]},
-                gauge={'axis': {'range': [0, 100]},
-                       'bar': {'color': "rgb(26, 118, 255)"},
-                       'steps': [
-                           {'range': [0, 33], 'color': "lightgray"},
-                           {'range': [33, 66], 'color': "gray"},
-                           {'range': [66, 100], 'color': "darkgray"}
-                       ]}))
+                gauge={
+                    'axis': {'range': [0, 100]},
+                    'bar': {'color': "rgb(26, 118, 255)"},
+                    'steps': [
+                        {'range': [0, 33], 'color': "lightgray"},
+                        {'range': [33, 66], 'color': "gray"},
+                        {'range': [66, 100], 'color': "darkgray"}
+                    ]
+                }
+            ))
             st.plotly_chart(fig)
 
-        # Extract and display strengths
-        strengths = re.findall(r'Strengths:\n((?:- .*\n)*)', st.session_state.analysis_results)
-        if strengths:
+        # Display strengths and areas for growth
+        col1, col2 = st.columns(2)
+        with col1:
             st.subheader("Strengths")
-            strength_list = strengths[0].strip().split('\n')
-            for strength in strength_list:
-                if strength.startswith('- '):
-                    st.markdown(f"✅ {strength[2:]}")
-
-        # Extract and display areas for improvement
-        improvements = re.findall(r'Areas for Improvement:\n((?:- .*\n)*)', st.session_state.analysis_results)
-        if improvements:
-            st.subheader("Areas for Improvement")
-            improvement_list = improvements[0].strip().split('\n')
-            for improvement in improvement_list:
-                if improvement.startswith('- '):
-                    st.markdown(f"🔄 {improvement[2:]}")
-
-    # Technical Skills Tab
-    with tabs[1]:
-        st.subheader("MI Technique Usage")
-        
-        # Extract technique counts
-        techniques = {
-            'Open Questions': 0,
-            'Reflections': 0,
-            'Affirmations': 0,
-            'Summaries': 0
-        }
-        
-        for technique in techniques.keys():
-            count_match = re.search(f'{technique}:\s*(\d+)', st.session_state.analysis_results)
-            if count_match:
-                techniques[technique] = int(count_match.group(1))
-        
-        # Create bar chart
-        fig = go.Figure(data=[
-            go.Bar(
-                x=list(techniques.keys()),
-                y=list(techniques.values()),
-                marker_color='rgb(26, 118, 255)'
-            )
-        ])
-        
-        fig.update_layout(
-            title="Technique Usage Frequency",
-            xaxis_title="Technique",
-            yaxis_title="Count",
-            template="plotly_white"
-        )
-        st.plotly_chart(fig)
+            strengths = re.findall(r'Strengths:\n((?:- .*\n)*)', results)
+            if strengths:
+                for strength in strengths[0].strip().split('\n'):
+                    if strength.startswith('- '):
+                        st.markdown(f"✅ {strength[2:]}")
+
+        with col2:
+            st.subheader("Areas for Growth")
+            growth = re.findall(r'Areas for Growth:\n((?:- .*\n)*)', results)
+            if growth:
+                for area in growth[0].strip().split('\n'):
+                    if area.startswith('- '):
+                        st.markdown(f"🔄 {area[2:]}")
 
     # Client Language Tab
     with tabs[2]:
         st.subheader("Client Language Analysis")
         
-        # Extract change talk
-        change_talk = re.findall(r'Change Talk Instances:\n((?:- .*\n)*)', st.session_state.analysis_results)
-        if change_talk:
+        col1, col2 = st.columns(2)
+        with col1:
             st.markdown("### Change Talk 🌱")
-            changes = change_talk[0].strip().split('\n')
-            for change in changes:
-                if change.startswith('- '):
-                    st.markdown(f"- {change[2:]}")
-        
-        # Extract sustain talk
-        sustain_talk = re.findall(r'Sustain Talk Instances:\n((?:- .*\n)*)', st.session_state.analysis_results)
-        if sustain_talk:
+            change_talk = re.findall(r'Change Talk Examples:\n((?:- .*\n)*)', results)
+            if change_talk:
+                for talk in change_talk[0].strip().split('\n'):
+                    if talk.startswith('- '):
+                        st.markdown(f"- {talk[2:]}")
+
+        with col2:
             st.markdown("### Sustain Talk 🔄")
-            sustains = sustain_talk[0].strip().split('\n')
-            for sustain in sustains:
-                if sustain.startswith('- '):
-                    st.markdown(f"- {sustain[2:]}")
+            sustain_talk = re.findall(r'Sustain Talk Examples:\n((?:- .*\n)*)', results)
+            if sustain_talk:
+                for talk in sustain_talk[0].strip().split('\n'):
+                    if talk.startswith('- '):
+                        st.markdown(f"- {talk[2:]}")
 
     # Session Flow Tab
     with tabs[3]:
         st.subheader("Session Flow Analysis")
         
-        # Extract key themes
-        themes = re.findall(r'Key Themes:\n((?:- .*\n)*)', st.session_state.analysis_results)
-        if themes:
-            st.markdown("### Key Themes 🎯")
-            theme_list = themes[0].strip().split('\n')
-            for theme in theme_list:
-                if theme.startswith('- '):
-                    st.markdown(f"- {theme[2:]}")
-        
-        # Extract session summary
-        summary_match = re.search(r'Session Summary:\n(.*?)(?=\n\n|\Z)', st.session_state.analysis_results, re.DOTALL)
-        if summary_match:
-            st.markdown("### Session Summary")
-            st.write(summary_match.group(1).strip())
+        # Key Moments
+        st.markdown("### Key Moments")
+        key_moments = re.findall(r'Key Moments:\n((?:\d\. .*\n)*)', results)
+        if key_moments:
+            for moment in key_moments[0].strip().split('\n'):
+                if moment.strip():
+                    st.markdown(f"{moment}")
+
+        # Therapeutic Process
+        st.markdown("### Therapeutic Process")
+        process = re.findall(r'Therapeutic Process:\n((?:- .*\n)*)', results)
+        if process:
+            for item in process[0].strip().split('\n'):
+                if item.startswith('- '):
+                    st.markdown(f"- {item[2:]}")
 
     # Recommendations Tab
     with tabs[4]:
-        st.subheader("Recommendations for Improvement")
+        st.subheader("Recommendations")
         
-        # Display improvement areas as recommendations
-        if improvements:
-            st.markdown("### Priority Areas 🎯")
-            improvement_list = improvements[0].strip().split('\n')
-            for i, improvement in enumerate(improvement_list[:3], 1):
-                if improvement.startswith('- '):
-                    st.markdown(f"**{i}. {improvement[2:]}**")
-                    st.markdown(get_improvement_suggestion(improvement[2:]))
+        # Priority Actions
+        st.markdown("### Priority Actions 🎯")
+        priorities = re.findall(r'Priority Actions:\n((?:\d\. .*\n)*)', results)
+        if priorities:
+            for priority in priorities[0].strip().split('\n'):
+                if priority.strip():
+                    st.markdown(f"{priority}")
+
+        # Development Strategies
+        st.markdown("### Development Strategies 📈")
+        strategies = re.findall(r'Development Strategies:\n((?:- .*\n)*)', results)
+        if strategies:
+            for strategy in strategies[0].strip().split('\n'):
+                if strategy.startswith('- '):
+                    st.markdown(f"- {strategy[2:]}")
 
 
 def get_technique_description(technique):