Create app.py

app.py

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+"""
+RICA Agent - Hugging Face Spaces Compatible Version
+"""
+
+import streamlit as st
+import os
+from pathlib import Path
+import sys
+
+# 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
+
+# Page configuration
+st.set_page_config(
+    page_title="RICA - AI Revenue Intelligence",
+    page_icon="🤖",
+    layout="wide",
+    initial_sidebar_state="expanded"
+)
+
+# Custom CSS
+st.markdown("""
+<style>
+    .main-header {
+        font-size: 2.5rem;
+        font-weight: bold;
+        text-align: center;
+        background: linear-gradient(90deg, #667eea 0%, #764ba2 100%);
+        -webkit-background-clip: text;
+        -webkit-text-fill-color: transparent;
+        margin-bottom: 1rem;
+    }
+    .stAlert > div {
+        padding: 0.5rem;
+    }
+    .metric-container {
+        background: #f0f2f6;
+        padding: 1rem;
+        border-radius: 0.5rem;
+        margin: 0.5rem 0;
+    }
+</style>
+""", unsafe_allow_html=True)
+
+# Header
+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
+    openai_key = st.text_input(
+        "OpenAI API Key",
+        type="password", 
+        help="Required for AI agent operations"
+    )
+    
+    if openai_key:
+        os.environ["OPENAI_API_KEY"] = openai_key
+        st.success("✅ API Key configured")
+    else:
+        st.warning("⚠️ Enter API Key to enable AI features")
+    
+    st.divider()
+    
+    # Model status
+    st.header("🧠 ML Model Status")
+    
+    model_exists = st.session_state.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])
+    else:
+        st.warning("⚠️ Model Not Trained")
+        
+        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)}")
+    
+    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)
+
+# Main content
+if not openai_key:
+    # Welcome screen
+    st.info("👈 Please enter your OpenAI API Key in the sidebar to begin")
+    
+    col1, col2 = st.columns(2)
+    
+    with col1:
+        st.markdown("""
+        ## 🚀 Capabilities
+        
+        **RICA** combines machine learning with autonomous AI to deliver:
+        
+        - 🎯 **Churn Prediction**: ML models identify at-risk customers
+        - 📊 **Real-time Analysis**: Direct SAP data integration
+        - 🤖 **Autonomous Insights**: LLM-powered recommendations
+        - 📈 **Business Impact**: Actionable revenue optimization
+        """)
+    
+    with col2:
+        st.markdown("""
+        ## 🏗️ Architecture
+        
+        - **Data Source**: Real SAP/SALT dataset
+        - **ML Engine**: Scikit-learn Random Forest
+        - **Agent Framework**: smolagents + OpenAI
+        - **Analytics**: DuckDB high-performance processing
+        - **UI**: Streamlit interactive interface
+        """)
+    
+    # Demo metrics
+    st.markdown("## 📊 Sample Analytics Preview")
+    
+    col1, col2, col3, col4 = st.columns(4)
+    
+    with col1:
+        st.metric("Customers", "2,847", delta="12%")
+    with col2:
+        st.metric("Churn Risk", "23 customers", delta="-8", delta_color="inverse")
+    with col3:
+        st.metric("Revenue at Risk", "$1.2M", delta="15%")
+    with col4:
+        st.metric("Model Accuracy", "87.3%", delta="2.1%")
+
+elif not model_exists and 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)")
+    
+    st.markdown("## 🔄 Training Process")
+    st.markdown("""
+    1. **Load SAP Data**: Customer and sales data from Hugging Face Hub
+    2. **Feature Engineering**: RFM analysis and behavioral patterns
+    3. **Model Training**: Random Forest classifier with cross-validation
+    4. **Performance Validation**: Accuracy testing and metrics calculation
+    5. **Model Persistence**: Save for future predictions
+    """)
+
+else:
+    # Main analysis interface
+    st.markdown("## 🎯 AI Business Intelligence")
+    
+    # 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!")
+                    
+                    # 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")
+                        st.info(str(result))
+                    
+                    # Raw response
+                    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 try again")
+    
+    # Quick stats
+    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%}")
+
+# Footer
+st.markdown("---")
+st.markdown("🤖 **RICA Agent** | ML + AI for Business Intelligence | Deployed on 🤗 Hugging Face Spaces")