Update app.py

app.py

@@ -1,19 +1,38 @@
 """
-RICA Agent - Hugging Face Spaces Compatible Version
+RICA Agent - Complete Fixed Version
 """
 
 import streamlit as st
 import os
-from pathlib import Path
 import sys
+from pathlib import Path
+import json
 
 # Add project root to path for imports
 if str(Path(__file__).parent) not in sys.path:
     sys.path.append(str(Path(__file__).parent))
 
 # Import modules
-from utils.model_trainer import EmbeddedChurnTrainer
-from agent.rica_agent import create_rica_agent_hf, execute_rica_analysis_hf
+try:
+    from utils.model_trainer import EmbeddedChurnTrainer
+    from agent.rica_agent import execute_rica_analysis_hf
+except ImportError as e:
+    st.error(f"Import error: {e}")
+    st.stop()
+
+# Initialize session state
+if 'api_key_valid' not in st.session_state:
+    st.session_state.api_key_valid = False
+if 'model_trained' not in st.session_state:
+    st.session_state.model_trained = False
+if 'trainer' not in st.session_state:
+    st.session_state.trainer = EmbeddedChurnTrainer()
+if 'analysis_type' not in st.session_state:
+    st.session_state.analysis_type = 'comprehensive'
+if 'risk_threshold' not in st.session_state:
+    st.session_state.risk_threshold = 0.6
+if 'max_customers' not in st.session_state:
+    st.session_state.max_customers = 200
 
 # Page configuration
 st.set_page_config(
@@ -51,27 +70,31 @@ st.markdown("""
 st.markdown('<h1 class="main-header">🤖 RICA - AI Revenue Intelligence Agent</h1>', unsafe_allow_html=True)
 st.markdown("### Enterprise Business Intelligence Powered by Machine Learning")
 
-# Initialize session state
-if 'model_trained' not in st.session_state:
-    st.session_state.model_trained = False
-if 'trainer' not in st.session_state:
-    st.session_state.trainer = EmbeddedChurnTrainer()
-
 # Sidebar configuration
 with st.sidebar:
     st.header("🔧 Configuration")
     
-    # API Key input
+    # API Key input with proper validation
     openai_key = st.text_input(
         "OpenAI API Key",
-        type="password", 
-        help="Required for AI agent operations"
+        type="password",
+        help="Required for AI agent operations",
+        key="openai_api_key",
+        placeholder="sk-..."
     )
     
-    if openai_key:
+    # Validate and store API key
+    if openai_key and openai_key.startswith('sk-') and len(openai_key) > 20:
         os.environ["OPENAI_API_KEY"] = openai_key
-        st.success("✅ API Key configured")
+        st.session_state.api_key_valid = True
+        st.success("✅ OpenAI API Key Configured")
+        st.caption("Key validated and ready for use")
+    elif openai_key:
+        st.session_state.api_key_valid = False
+        st.error("❌ Invalid API Key Format")
+        st.caption("Key should start with 'sk-' and be longer than 20 characters")
     else:
+        st.session_state.api_key_valid = False
         st.warning("⚠️ Enter API Key to enable AI features")
     
     st.divider()
@@ -79,53 +102,78 @@ with st.sidebar:
     # Model status
     st.header("🧠 ML Model Status")
     
-    model_exists = st.session_state.trainer.model_exists()
+    trainer = st.session_state.trainer
+    model_exists = trainer.model_exists()
     
     if model_exists:
         st.success("✅ Model Ready")
-        metadata = st.session_state.trainer.load_existing_metadata()
-        if metadata:
-            st.metric("Model Accuracy", f"{metadata['metrics'].get('test_accuracy', 0):.1%}")
-            st.metric("Training Date", metadata['training_date'][:10])
+        try:
+            metadata = trainer.load_existing_metadata()
+            if metadata and 'metrics' in metadata:
+                st.metric("Model Accuracy", f"{metadata['metrics'].get('test_accuracy', 0):.1%}")
+                st.metric("Training Date", metadata.get('training_date', 'Unknown')[:10])
+            st.session_state.model_trained = True
+        except:
+            st.info("Model exists but metadata unavailable")
+            st.session_state.model_trained = True
     else:
         st.warning("⚠️ Model Not Trained")
+        st.session_state.model_trained = False
         
-        if st.button("🏋️ Train Model Now", type="primary"):
-            with st.spinner("Training ML model... This may take 1-2 minutes"):
-                try:
-                    metrics = st.session_state.trainer.train_model_if_needed()
-                    if metrics:
-                        st.success("🎉 Model trained successfully!")
-                        st.session_state.model_trained = True
-                        st.rerun()
-                    else:
-                        st.error("Training failed. Please check the logs.")
-                except Exception as e:
-                    st.error(f"Training error: {str(e)}")
+        if st.button("🏋️ Train Model Now", type="primary", key="train_model_btn"):
+            if not st.session_state.api_key_valid:
+                st.error("Please configure API key first")
+            else:
+                with st.spinner("Training ML model... This may take 1-2 minutes"):
+                    try:
+                        metrics = trainer.train_model_if_needed()
+                        if metrics:
+                            st.success("🎉 Model trained successfully!")
+                            st.session_state.model_trained = True
+                            st.rerun()
+                        else:
+                            st.error("Training failed. Please check the logs.")
+                    except Exception as e:
+                        st.error(f"Training error: {str(e)}")
     
     st.divider()
     
-    # Analysis configuration
-    st.header("📊 Analysis Options")
-    
-    analysis_type = st.selectbox(
-        "Select Analysis Type",
-        ["comprehensive", "churn_focus", "quick_insights"],
-        format_func=lambda x: {
-            "comprehensive": "🎯 Comprehensive Review",
-            "churn_focus": "🚨 Churn Risk Analysis", 
-            "quick_insights": "⚡ Quick Insights"
-        }[x]
-    )
-    
-    # Advanced options
-    with st.expander("⚙️ Advanced Options"):
-        risk_threshold = st.slider("Churn Risk Threshold", 0.3, 0.9, 0.6)
-        max_customers = st.number_input("Max Customers to Analyze", 50, 500, 200)
+    # Analysis configuration (only if API key is valid)
+    if st.session_state.api_key_valid:
+        st.header("📊 Analysis Options")
+        
+        analysis_type = st.selectbox(
+            "Select Analysis Type",
+            ["comprehensive", "churn_focus", "quick_insights"],
+            format_func=lambda x: {
+                "comprehensive": "🎯 Comprehensive Review",
+                "churn_focus": "🚨 Churn Risk Analysis", 
+                "quick_insights": "⚡ Quick Insights"
+            }[x],
+            key="analysis_type_select"
+        )
+        
+        st.session_state.analysis_type = analysis_type
+        
+        # Advanced options
+        with st.expander("⚙️ Advanced Options"):
+            risk_threshold = st.slider(
+                "Churn Risk Threshold", 
+                0.3, 0.9, 0.6, 
+                key="risk_threshold_slider"
+            )
+            max_customers = st.number_input(
+                "Max Customers to Analyze", 
+                50, 500, 200,
+                key="max_customers_input"
+            )
+            
+            st.session_state.risk_threshold = risk_threshold
+            st.session_state.max_customers = max_customers
 
-# Main content
-if not openai_key:
-    # Welcome screen
+# Main content logic
+if not st.session_state.api_key_valid:
+    # Show welcome screen when API key not configured
     st.info("👈 Please enter your OpenAI API Key in the sidebar to begin")
     
     col1, col2 = st.columns(2)
@@ -167,7 +215,7 @@ if not openai_key:
     with col4:
         st.metric("Model Accuracy", "87.3%", delta="2.1%")
 
-elif not model_exists and not st.session_state.model_trained:
+elif not st.session_state.model_trained:
     # Model training required
     st.warning("🧠 Machine learning model needs to be trained before analysis")
     st.info("👈 Use the 'Train Model Now' button in the sidebar (takes 1-2 minutes)")
@@ -182,68 +230,68 @@ elif not model_exists and not st.session_state.model_trained:
     """)
 
 else:
-    # Main analysis interface
-    st.markdown("## 🎯 AI Business Intelligence")
+    # Main analysis interface - API key valid and model trained
+    st.markdown("## 🎯 AI Business Intelligence Dashboard")
     
     # Analysis execution
     if st.button("🚀 Run RICA Analysis", type="primary", use_container_width=True):
-        if not st.session_state.trainer.model_exists():
-            st.error("Please train the model first using the sidebar button")
-        else:
-            with st.spinner("🤖 RICA is analyzing your business data..."):
-                try:
-                    # Execute analysis
-                    parameters = {
-                        "risk_threshold": risk_threshold,
-                        "max_customers": max_customers
-                    }
-                    
-                    result = execute_rica_analysis_hf(analysis_type, parameters)
-                    
-                    # Display results
-                    st.success("✅ Analysis Complete!")
+        with st.spinner("🤖 RICA is analyzing your business data..."):
+            try:
+                # Execute analysis
+                parameters = {
+                    "risk_threshold": st.session_state.risk_threshold,
+                    "max_customers": st.session_state.max_customers
+                }
+                
+                result = execute_rica_analysis_hf(st.session_state.analysis_type, parameters)
+                
+                # Display results
+                st.success("✅ Analysis Complete!")
+                
+                # Create tabs for different result views
+                if st.session_state.analysis_type == "comprehensive":
+                    tab1, tab2, tab3 = st.tabs(["📊 Executive Summary", "🚨 Risk Analysis", "💡 Recommendations"])
                     
-                    # Create tabs for results
-                    if analysis_type == "comprehensive":
-                        tab1, tab2, tab3 = st.tabs(["📊 Executive Summary", "🚨 Risk Analysis", "💡 Recommendations"])
-                        
-                        with tab1:
-                            st.markdown("### Executive Dashboard")
-                            st.info(str(result))
-                        
-                        with tab2:
-                            st.markdown("### Customer Risk Analysis")
-                            st.write("Detailed churn risk breakdown and customer segmentation")
-                        
-                        with tab3:
-                            st.markdown("### AI Recommendations")
-                            st.write("Specific actions prioritized by business impact")
-                    
-                    else:
-                        st.markdown(f"### {analysis_type.replace('_', ' ').title()} Results")
+                    with tab1:
+                        st.markdown("### Executive Dashboard")
                         st.info(str(result))
                     
-                    # Raw response
-                    with st.expander("🔍 Detailed Analysis Response"):
-                        st.code(str(result), language="text")
+                    with tab2:
+                        st.markdown("### Customer Risk Analysis")
+                        st.write("Detailed churn risk breakdown and customer segmentation")
                     
-                except Exception as e:
-                    st.error(f"Analysis failed: {str(e)}")
-                    st.info("Please check your API key and try again")
+                    with tab3:
+                        st.markdown("### AI Recommendations")
+                        st.write("Specific actions prioritized by business impact")
+                
+                else:
+                    st.markdown(f"### {st.session_state.analysis_type.replace('_', ' ').title()} Results")
+                    st.info(str(result))
+                
+                # Raw response in expander
+                with st.expander("🔍 Detailed Analysis Response"):
+                    st.code(str(result), language="text")
+                
+            except Exception as e:
+                st.error(f"❌ Analysis failed: {str(e)}")
+                st.info("Please check your API key and model status")
     
-    # Quick stats
+    # Show model performance metrics
     if st.session_state.trainer.model_exists():
         st.markdown("## 📈 Model Performance")
-        metadata = st.session_state.trainer.load_existing_metadata()
-        if metadata and 'metrics' in metadata:
-            col1, col2, col3 = st.columns(3)
-            
-            with col1:
-                st.metric("Model Accuracy", f"{metadata['metrics'].get('test_accuracy', 0):.1%}")
-            with col2:
-                st.metric("Training Samples", f"{metadata['metrics'].get('training_samples', 0):,}")
-            with col3:
-                st.metric("Churn Rate", f"{metadata['metrics'].get('churn_rate', 0):.1%}")
+        try:
+            metadata = st.session_state.trainer.load_existing_metadata()
+            if metadata and 'metrics' in metadata:
+                col1, col2, col3 = st.columns(3)
+                
+                with col1:
+                    st.metric("Model Accuracy", f"{metadata['metrics'].get('test_accuracy', 0):.1%}")
+                with col2:
+                    st.metric("Training Samples", f"{metadata['metrics'].get('training_samples', 0):,}")
+                with col3:
+                    st.metric("Churn Rate", f"{metadata['metrics'].get('churn_rate', 0):.1%}")
+        except:
+            st.info("Model performance metrics unavailable")
 
 # Footer
 st.markdown("---")