Update components/ui_components.py

components/ui_components.py

@@ -1,313 +1,313 @@
-import streamlit as st
-from db.helpers import get_research_stats
-from generator import model_manager
-from export_training_data_from_db import export_training_data_from_db
-
-def render_header():
-    st.title("🧠 TailorED - AI-Powered Educational Content Generator")
-
-def render_sidebar():
-    with st.sidebar:
-        # === RESEARCH MODEL SELECTION ===
-        st.subheader("🔬 Research Model Selection")
-        
-        # Initialize model choice if not exists
-        if "selected_model" not in st.session_state:
-            st.session_state.selected_model = "groq"
-        
-        # Check if we have existing content and need to show regeneration prompt
-        if (st.session_state.get("generated_output") and 
-            st.session_state.get("current_page") == "generator" and
-            not st.session_state.get("showing_regeneration_prompt", False)):
-            
-            # Store current model before potential change
-            previous_model = st.session_state.selected_model
-            
-            # Model selection with regeneration logic
-            model_choice = st.radio(
-                "Select AI Model:",
-                options=[
-                    "🧪 Phi-3 (Research Model)", 
-                    "📊 Groq (Training Data)"
-                ],
-                index=0 if st.session_state.selected_model == "phi3" else 1,
-                key="research_model_selection"
-            )
-            
-            # Determine new model selection
-            new_model = "phi3" if model_choice == "🧪 Phi-3 (Research Model)" else "groq"
-            
-            # If model changed and we have content, show regeneration prompt
-            if new_model != previous_model:
-                st.session_state.pending_model_switch = new_model
-                st.session_state.previous_model = previous_model
-                st.session_state.showing_regeneration_prompt = True
-                st.rerun()
-                
-        else:
-            # Normal model selection (no content or prompt already shown)
-            model_choice = st.radio(
-                "Select AI Model:",
-                options=[
-                    "🧪 Phi-3 (Research Model)", 
-                    "📊 Groq (Training Data)"
-                ],
-                index=0 if st.session_state.selected_model == "phi3" else 1,
-                key="research_model_selection"
-            )
-            
-            # Update model selection
-            new_model = "phi3" if model_choice == "🧪 Phi-3 (Research Model)" else "groq"
-            if new_model != st.session_state.selected_model:
-                st.session_state.selected_model = new_model
-                if not st.session_state.get("generated_output"):
-                    st.success(f"✅ Switched to {new_model.upper()} model")
-        
-        # Show current model status
-        current_model = st.session_state.selected_model
-        if current_model == "phi3":
-            st.info("🧪 **Testing Phi-3** - Research model being evaluated")
-        else:
-            st.success("📊 **Generating Training Data** - Groq outputs will train Phi-3")
-        
-        # Render regeneration prompt if needed
-        if st.session_state.get("showing_regeneration_prompt", False):
-            render_regeneration_prompt()
-        
-        # Research context
-        st.markdown("---")
-        st.markdown("### 🎯 Research Mission")
-        st.markdown("""
-        We're **fine-tuning Phi-3 Mini** using Groq's high-quality outputs.
-        
-        **Your Role:** Compare both models to help improve Phi-3!
-        - Use **Groq** to create training examples
-        - Use **Phi-3** to test research progress  
-        - Switch models to compare outputs on the same content
-        """)
-        
-        st.markdown("---")
-        
-        st.header("🎓 Research Progress")
-        st.write("**Your feedback trains better educational AI**")
-
-        try:
-            stats = get_research_stats()
-            render_progress_metrics(stats)
-            render_quality_indicators(stats)
-            render_research_status(stats)
-            render_service_status()
-        except Exception as e:
-            st.error(f"Sidebar failed: {e}")
-            render_default_sidebar()
-
-def render_regeneration_prompt():
-    """Show prompt to regenerate content with new model"""
-    st.markdown("---")
-    st.warning("🔄 **Model Changed!**")
-    
-    previous_model = st.session_state.previous_model
-    new_model = st.session_state.pending_model_switch
-    
-    st.write(f"You switched from **{previous_model.upper()}** to **{new_model.upper()}**.")
-    st.write("Would you like to regenerate the same content with the new model?")
-    
-    col1, col2, col3 = st.columns([1, 1, 1])
-    
-    with col1:
-        if st.button("✅ Yes, Regenerate", use_container_width=True, key="confirm_regenerate"):
-            # Trigger regeneration with new model
-            st.session_state.selected_model = new_model
-            st.session_state.regenerate_with_new_model = True
-            st.session_state.showing_regeneration_prompt = False
-            st.session_state.pending_model_switch = None
-            st.session_state.previous_model = None
-            st.rerun()
-    
-    with col2:
-        if st.button("❌ No, Keep Current", use_container_width=True, key="keep_current"):
-            # Revert to previous model and keep current content
-            st.session_state.selected_model = st.session_state.previous_model
-            st.session_state.showing_regeneration_prompt = False
-            st.session_state.pending_model_switch = None
-            st.session_state.previous_model = None
-            st.rerun()
-    
-    with col3:
-        if st.button("🏠 Go to Home", use_container_width=True, key="go_home"):
-            # Clear content and go to home
-            from components.session_manager import clear_session
-            clear_session()
-            st.session_state.showing_regeneration_prompt = False
-            st.session_state.pending_model_switch = None
-            st.session_state.previous_model = None
-            st.rerun()
-
-def render_progress_metrics(stats):
-    col1, col2 = st.columns(2)
-    
-    with col1:
-        st.metric("Total Feedback", stats.get("total_feedback", 0))
-    
-    with col2:
-        st.metric("Content Generated", stats.get("total_content", 0))
-
-    if stats.get("total_feedback", 0) > 0:
-        render_progress_bar(stats)
-
-def render_progress_bar(stats):
-    """Render progress bar towards research goal"""
-    st.subheader("📈 Our Progress")
-    target_feedback = 100
-    total_feedback = stats.get("total_feedback", 0)
-    progress_percent = min((total_feedback / target_feedback) * 100, 100)
-    st.progress(progress_percent / 100)
-    st.caption(f"Goal: 100 feedback points • {total_feedback}/100")
-    
-    if total_feedback >= target_feedback:
-        st.balloons()
-        st.success("🎉 Amazing! We've reached our research goal!")
-
-def render_quality_indicators(stats):
-    st.subheader("✨ Model Quality Comparison")
-    
-    # Safely get model scores with fallbacks
-    groq_scores = stats.get("groq_scores", {})
-    phi3_scores = stats.get("phi3_scores", {})
-    
-    groq_clarity = groq_scores.get("clarity", 0)
-    groq_depth = groq_scores.get("depth", 0)
-    phi3_clarity = phi3_scores.get("clarity", 0)
-    phi3_depth = phi3_scores.get("depth", 0)
-    
-    # Groq metrics
-    st.markdown("**📊 Groq (Training Data)**")
-    col1, col2 = st.columns(2)
-    with col1:
-        # Show delta if we have both scores
-        delta_clarity = None
-        if groq_clarity > 0 and phi3_clarity > 0:
-            delta_clarity = f"+{groq_clarity - phi3_clarity:.1f}"
-        st.metric("Avg Clarity", f"{groq_clarity}/5", delta=delta_clarity)
-    with col2:
-        delta_depth = None
-        if groq_depth > 0 and phi3_depth > 0:
-            delta_depth = f"+{groq_depth - phi3_depth:.1f}"
-        st.metric("Avg Depth", f"{groq_depth}/5", delta=delta_depth)
-    
-    # Phi-3 metrics
-    st.markdown("**🧪 Phi-3 (Research Model)**")
-    col3, col4 = st.columns(2)
-    with col3:
-        delta_clarity_phi3 = None
-        if phi3_clarity > 0 and groq_clarity > 0:
-            delta_clarity_phi3 = f"{phi3_clarity - groq_clarity:.1f}"
-        st.metric("Avg Clarity", f"{phi3_clarity}/5", delta=delta_clarity_phi3)
-    with col4:
-        delta_depth_phi3 = None
-        if phi3_depth > 0 and groq_depth > 0:
-            delta_depth_phi3 = f"{phi3_depth - groq_depth:.1f}"
-        st.metric("Avg Depth", f"{phi3_depth}/5", delta=delta_depth_phi3)
-    
-    # Show quality gap analysis
-    if groq_clarity > 0 and phi3_clarity > 0:
-        clarity_gap = groq_clarity - phi3_clarity
-        depth_gap = groq_depth - phi3_depth
-        
-        if clarity_gap > 0 or depth_gap > 0:
-            st.caption(f"🔍 Quality gap: Clarity +{clarity_gap:.1f}, Depth +{depth_gap:.1f}")
-        elif clarity_gap < 0 or depth_gap < 0:
-            st.caption(f"🎉 Phi-3 leads: Clarity {abs(clarity_gap):.1f}, Depth {abs(depth_gap):.1f}")
-        else:
-            st.caption("⚖️ Models performing equally")
-
-def render_research_status(stats):
-    st.subheader("🔬 Research Progress")
-    
-    col1, col2, col3 = st.columns(3)
-    
-    with col1:
-        st.metric("Groq Data", stats.get("groq_feedback_count", 0))
-        st.caption("For fine-tuning")
-    
-    with col2:
-        st.metric("High-Quality Groq", stats.get("high_quality_groq", 0))
-        st.caption("Fine-tuning ready")
-    
-    with col3:
-        st.metric("Phi-3 Data", stats.get("phi3_feedback_count", 0))
-        st.caption("For comparison")
-    
-    # Fine-tuning readiness
-    target_examples = 50
-    high_quality_groq = stats.get("high_quality_groq", 0)
-    
-    if high_quality_groq >= target_examples:
-        st.success("🎉 Ready to fine-tune Phi-3 with Groq data!")
-        if st.button("🚀 Export Fine-tuning Data", use_container_width=True, type="primary"):
-            from export_training_data_from_db import export_training_data_from_db
-            if export_training_data_from_db():
-                st.success("✅ Groq data exported for Phi-3 fine-tuning!")
-            else:
-                st.error("Export failed")
-    else:
-        needed = target_examples - high_quality_groq
-        st.info(f"📊 Need {needed} more high-quality Groq examples")
-        progress = high_quality_groq / target_examples if target_examples > 0 else 0
-        st.progress(progress)
-        st.caption(f"Progress: {high_quality_groq}/{target_examples} examples")
-
-def render_service_status():
-    st.markdown("---")
-    st.subheader("🛜 Platform Status")
-    
-    try:
-        status = model_manager.get_service_status()
-        
-        # Create status columns
-        col1, col2 = st.columns(2)
-        
-        with col1:
-            # Phi-3 Status
-            phi3_status = status["phi3"]
-            if phi3_status["server_healthy"] and phi3_status["model_available"]:
-                st.success("🧪 Phi-3 Mini")
-                st.caption("Research Model • Ready")
-            elif phi3_status["server_healthy"]:
-                st.warning("🧪 Phi-3 Mini") 
-                st.caption("Research Model • Needs Setup")
-            else:
-                st.error("🧪 Phi-3 Mini")
-                st.caption("Research Model • Offline")
-        
-        with col2:
-            # Groq Status
-            groq_status = status["groq"]
-            healthy_count = groq_status['healthy_providers']
-            total_providers = groq_status['total_providers']
-            
-            if healthy_count == total_providers:
-                st.success("📊 Groq API")
-                st.caption("Training Data • Fully Operational")
-            elif healthy_count > 0:
-                st.warning("📊 Groq API")
-                st.caption(f"Training Data • {healthy_count}/{total_providers} providers")
-            else:
-                st.error("📊 Groq API")
-                st.caption("Training Data • Offline")
-        
-        # Quick health indicator
-        if status["phi3"]["server_healthy"] and groq_status['healthy_providers'] > 0:
-            st.caption("💡 All systems operational - research ready!")
-        else:
-            st.caption("⚠️ Some services limited - research may be affected")
-                
-    except Exception as e:
-        st.error("❌ Status check failed")
-        st.caption("Research platform may have issues")
-
-def render_default_sidebar():
-    st.info("🌟 Start generating content to contribute to our research!")
-    st.caption("Your feedback on Groq content will train Phi-3 to become a better educational AI")
-    if st.button("🔄 Refresh Progress", use_container_width=True, key="refresh_progress"):
-        st.rerun()
+import streamlit as st
+from db.helpers import get_research_stats
+from generator import model_manager
+from export_training_data_from_db import export_training_data_from_db
+
+def render_header():
+    st.title("🧠 TailorED - AI-Powered Educational Content Generator")
+
+def render_sidebar():
+    with st.sidebar:
+        # === RESEARCH MODEL SELECTION ===
+        st.subheader("🔬 Research Model Selection")
+        
+        # Initialize model choice if not exists
+        if "selected_model" not in st.session_state:
+            st.session_state.selected_model = "groq"
+        
+        # Check if we have existing content and need to show regeneration prompt
+        if (st.session_state.get("generated_output") and 
+            st.session_state.get("current_page") == "generator" and
+            not st.session_state.get("showing_regeneration_prompt", False)):
+            
+            # Store current model before potential change
+            previous_model = st.session_state.selected_model
+            
+            # Model selection with regeneration logic
+            model_choice = st.radio(
+                "Select AI Model:",
+                options=[
+                    "🧪 Phi-3 (Research Model)", 
+                    "📊 Groq (Training Data)"
+                ],
+                index=0 if st.session_state.selected_model == "phi3" else 1,
+                key="research_model_selection"
+            )
+            
+            # Determine new model selection
+            new_model = "phi3" if model_choice == "🧪 Phi-3 (Research Model)" else "groq"
+            
+            # If model changed and we have content, show regeneration prompt
+            if new_model != previous_model:
+                st.session_state.pending_model_switch = new_model
+                st.session_state.previous_model = previous_model
+                st.session_state.showing_regeneration_prompt = True
+                st.rerun()
+                
+        else:
+            # Normal model selection (no content or prompt already shown)
+            model_choice = st.radio(
+                "Select AI Model:",
+                options=[
+                    "🧪 Phi-3 (Research Model)", 
+                    "📊 Groq (Training Data)"
+                ],
+                index=0 if st.session_state.selected_model == "phi3" else 1,
+                key="research_model_selection"
+            )
+            
+            # Update model selection
+            new_model = "phi3" if model_choice == "🧪 Phi-3 (Research Model)" else "groq"
+            if new_model != st.session_state.selected_model:
+                st.session_state.selected_model = new_model
+                if not st.session_state.get("generated_output"):
+                    st.success(f"✅ Switched to {new_model.upper()} model")
+        
+        # Show current model status
+        current_model = st.session_state.selected_model
+        if current_model == "phi3":
+            st.info("🧪 **Testing Phi-3** - Research model being evaluated")
+        else:
+            st.success("📊 **Generating Training Data** - Groq outputs will train Phi-3")
+        
+        # Render regeneration prompt if needed
+        if st.session_state.get("showing_regeneration_prompt", False):
+            render_regeneration_prompt()
+        
+        # Research context
+        st.markdown("---")
+        st.markdown("### 🎯 Research Mission")
+        st.markdown("""
+        We're **fine-tuning Phi-3 Mini** using Groq's high-quality outputs.
+        
+        **Your Role:** Compare both models to help improve Phi-3!
+        - Use **Groq** to create training examples
+        - Use **Phi-3** to test research progress  
+        - Switch models to compare outputs on the same content
+        """)
+        
+        st.markdown("---")
+        
+        st.header("🎓 Research Progress")
+        st.write("**Your feedback trains better educational AI**")
+
+        try:
+            stats = get_research_stats()
+            render_progress_metrics(stats)
+            render_quality_indicators(stats)
+            render_research_status(stats)
+            render_service_status()
+        except Exception as e:
+            st.error(f"Sidebar failed: {e}")
+            render_default_sidebar()
+
+def render_regeneration_prompt():
+    """Show prompt to regenerate content with new model"""
+    st.markdown("---")
+    st.warning("🔄 **Model Changed!**")
+    
+    previous_model = st.session_state.previous_model
+    new_model = st.session_state.pending_model_switch
+    
+    st.write(f"You switched from **{previous_model.upper()}** to **{new_model.upper()}**.")
+    st.write("Would you like to regenerate the same content with the new model?")
+    
+    col1, col2, col3 = st.columns([1, 1, 1])
+    
+    with col1:
+        if st.button("✅ Yes, Regenerate", use_container_width=True, key="confirm_regenerate"):
+            # Trigger regeneration with new model
+            st.session_state.selected_model = new_model
+            st.session_state.regenerate_with_new_model = True
+            st.session_state.showing_regeneration_prompt = False
+            st.session_state.pending_model_switch = None
+            st.session_state.previous_model = None
+            st.rerun()
+    
+    with col2:
+        if st.button("❌ No, Keep Current", use_container_width=True, key="keep_current"):
+            # Revert to previous model and keep current content
+            st.session_state.selected_model = st.session_state.previous_model
+            st.session_state.showing_regeneration_prompt = False
+            st.session_state.pending_model_switch = None
+            st.session_state.previous_model = None
+            st.rerun()
+    
+    with col3:
+        if st.button("🏠 Go to Home", use_container_width=True, key="go_home"):
+            # Clear content and go to home
+            from components.session_manager import clear_session
+            clear_session()
+            st.session_state.showing_regeneration_prompt = False
+            st.session_state.pending_model_switch = None
+            st.session_state.previous_model = None
+            st.rerun()
+
+def render_progress_metrics(stats):
+    col1, col2 = st.columns(2)
+    
+    with col1:
+        st.metric("Total Feedback", stats.get("total_feedback", 0))
+    
+    with col2:
+        st.metric("Content Generated", stats.get("total_content", 0))
+
+    if stats.get("total_feedback", 0) > 0:
+        render_progress_bar(stats)
+
+def render_progress_bar(stats):
+    """Render progress bar towards research goal"""
+    st.subheader("📈 Our Progress")
+    target_feedback = 1000
+    total_feedback = stats.get("total_feedback", 0)
+    progress_percent = min((total_feedback / target_feedback) * 100, 100)
+    st.progress(progress_percent / 100)
+    st.caption(f"Goal: 100 feedback points • {total_feedback}/100")
+    
+    if total_feedback >= target_feedback:
+        st.balloons()
+        st.success("🎉 Amazing! We've reached our research goal!")
+
+def render_quality_indicators(stats):
+    st.subheader("✨ Model Quality Comparison")
+    
+    # Safely get model scores with fallbacks
+    groq_scores = stats.get("groq_scores", {})
+    phi3_scores = stats.get("phi3_scores", {})
+    
+    groq_clarity = groq_scores.get("clarity", 0)
+    groq_depth = groq_scores.get("depth", 0)
+    phi3_clarity = phi3_scores.get("clarity", 0)
+    phi3_depth = phi3_scores.get("depth", 0)
+    
+    # Groq metrics
+    st.markdown("**📊 Groq (Training Data)**")
+    col1, col2 = st.columns(2)
+    with col1:
+        # Show delta if we have both scores
+        delta_clarity = None
+        if groq_clarity > 0 and phi3_clarity > 0:
+            delta_clarity = f"+{groq_clarity - phi3_clarity:.1f}"
+        st.metric("Avg Clarity", f"{groq_clarity}/5", delta=delta_clarity)
+    with col2:
+        delta_depth = None
+        if groq_depth > 0 and phi3_depth > 0:
+            delta_depth = f"+{groq_depth - phi3_depth:.1f}"
+        st.metric("Avg Depth", f"{groq_depth}/5", delta=delta_depth)
+    
+    # Phi-3 metrics
+    st.markdown("**🧪 Phi-3 (Research Model)**")
+    col3, col4 = st.columns(2)
+    with col3:
+        delta_clarity_phi3 = None
+        if phi3_clarity > 0 and groq_clarity > 0:
+            delta_clarity_phi3 = f"{phi3_clarity - groq_clarity:.1f}"
+        st.metric("Avg Clarity", f"{phi3_clarity}/5", delta=delta_clarity_phi3)
+    with col4:
+        delta_depth_phi3 = None
+        if phi3_depth > 0 and groq_depth > 0:
+            delta_depth_phi3 = f"{phi3_depth - groq_depth:.1f}"
+        st.metric("Avg Depth", f"{phi3_depth}/5", delta=delta_depth_phi3)
+    
+    # Show quality gap analysis
+    if groq_clarity > 0 and phi3_clarity > 0:
+        clarity_gap = groq_clarity - phi3_clarity
+        depth_gap = groq_depth - phi3_depth
+        
+        if clarity_gap > 0 or depth_gap > 0:
+            st.caption(f"🔍 Quality gap: Clarity +{clarity_gap:.1f}, Depth +{depth_gap:.1f}")
+        elif clarity_gap < 0 or depth_gap < 0:
+            st.caption(f"🎉 Phi-3 leads: Clarity {abs(clarity_gap):.1f}, Depth {abs(depth_gap):.1f}")
+        else:
+            st.caption("⚖️ Models performing equally")
+
+def render_research_status(stats):
+    st.subheader("🔬 Research Progress")
+    
+    col1, col2, col3 = st.columns(3)
+    
+    with col1:
+        st.metric("Groq Data", stats.get("groq_feedback_count", 0))
+        st.caption("For fine-tuning")
+    
+    with col2:
+        st.metric("High-Quality Groq", stats.get("high_quality_groq", 0))
+        st.caption("Fine-tuning ready")
+    
+    with col3:
+        st.metric("Phi-3 Data", stats.get("phi3_feedback_count", 0))
+        st.caption("For comparison")
+    
+    # Fine-tuning readiness
+    target_examples = 50
+    high_quality_groq = stats.get("high_quality_groq", 0)
+    
+    if high_quality_groq >= target_examples:
+        st.success("🎉 Ready to fine-tune Phi-3 with Groq data!")
+        if st.button("🚀 Export Fine-tuning Data", use_container_width=True, type="primary"):
+            from export_training_data_from_db import export_training_data_from_db
+            if export_training_data_from_db():
+                st.success("✅ Groq data exported for Phi-3 fine-tuning!")
+            else:
+                st.error("Export failed")
+    else:
+        needed = target_examples - high_quality_groq
+        st.info(f"📊 Need {needed} more high-quality Groq examples")
+        progress = high_quality_groq / target_examples if target_examples > 0 else 0
+        st.progress(progress)
+        st.caption(f"Progress: {high_quality_groq}/{target_examples} examples")
+
+def render_service_status():
+    st.markdown("---")
+    st.subheader("🛜 Platform Status")
+    
+    try:
+        status = model_manager.get_service_status()
+        
+        # Create status columns
+        col1, col2 = st.columns(2)
+        
+        with col1:
+            # Phi-3 Status
+            phi3_status = status["phi3"]
+            if phi3_status["server_healthy"] and phi3_status["model_available"]:
+                st.success("🧪 Phi-3 Mini")
+                st.caption("Research Model • Ready")
+            elif phi3_status["server_healthy"]:
+                st.warning("🧪 Phi-3 Mini") 
+                st.caption("Research Model • Needs Setup")
+            else:
+                st.error("🧪 Phi-3 Mini")
+                st.caption("Research Model • Offline")
+        
+        with col2:
+            # Groq Status
+            groq_status = status["groq"]
+            healthy_count = groq_status['healthy_providers']
+            total_providers = groq_status['total_providers']
+            
+            if healthy_count == total_providers:
+                st.success("📊 Groq API")
+                st.caption("Training Data • Fully Operational")
+            elif healthy_count > 0:
+                st.warning("📊 Groq API")
+                st.caption(f"Training Data • {healthy_count}/{total_providers} providers")
+            else:
+                st.error("📊 Groq API")
+                st.caption("Training Data • Offline")
+        
+        # Quick health indicator
+        if status["phi3"]["server_healthy"] and groq_status['healthy_providers'] > 0:
+            st.caption("💡 All systems operational - research ready!")
+        else:
+            st.caption("⚠️ Some services limited - research may be affected")
+                
+    except Exception as e:
+        st.error("❌ Status check failed")
+        st.caption("Research platform may have issues")
+
+def render_default_sidebar():
+    st.info("🌟 Start generating content to contribute to our research!")
+    st.caption("Your feedback on Groq content will train Phi-3 to become a better educational AI")
+    if st.button("🔄 Refresh Progress", use_container_width=True, key="refresh_progress"):
+        st.rerun()