Update app.py

app.py

@@ -6,16 +6,12 @@ import plotly.graph_objects as go
 from plotly.subplots import make_subplots
 import json
 import time
-import os
 from datetime import datetime
-import tempfile
-import pickle
 
-# Import your procurement agent code
+# Import from your fixed procurement agent file
 from agentic_sourcing_ppo_sap_colab import (
     suppliers_synthetic, market_signal, rl_recommend_tool, 
-    sap_create_po_mock, check_model_tool, get_model,
-    CodeAgent, VOL_MAP
+    sap_create_po_mock, check_model_tool
 )
 
 # Page config
@@ -47,13 +43,6 @@ st.markdown("""
         border-radius: 10px;
         margin: 0.5rem 0;
     }
-    .step-container {
-        background: #f8f9fa;
-        border-left: 4px solid #007bff;
-        padding: 1rem;
-        margin: 1rem 0;
-        border-radius: 5px;
-    }
     .success-box {
         background: #d4edda;
         border: 1px solid #c3e6cb;
@@ -61,39 +50,9 @@ st.markdown("""
         border-radius: 5px;
         margin: 1rem 0;
     }
-    .warning-box {
-        background: #fff3cd;
-        border: 1px solid #ffeaa7;
-        padding: 1rem;
-        border-radius: 5px;
-        margin: 1rem 0;
-    }
 </style>
 """, unsafe_allow_html=True)
 
-def create_gauge_chart(value, title, max_value=1.0):
-    """Create a gauge chart for metrics"""
-    fig = go.Figure(go.Indicator(
-        mode = "gauge+number+delta",
-        value = value,
-        domain = {'x': [0, 1], 'y': [0, 1]},
-        title = {'text': title},
-        delta = {'reference': max_value * 0.8},
-        gauge = {
-            'axis': {'range': [None, max_value]},
-            'bar': {'color': "#2E86AB"},
-            'steps': [
-                {'range': [0, max_value * 0.5], 'color': "#FFE5E5"},
-                {'range': [max_value * 0.5, max_value * 0.8], 'color': "#FFEFCC"},
-                {'range': [max_value * 0.8, max_value], 'color': "#E5F3E5"}],
-            'threshold': {
-                'line': {'color': "red", 'width': 4},
-                'thickness': 0.75,
-                'value': max_value * 0.9}}))
-    
-    fig.update_layout(height=300, margin=dict(l=20, r=20, t=40, b=20))
-    return fig
-
 def create_allocation_pie_chart(allocations):
     """Create pie chart for supplier allocations"""
     df = pd.DataFrame(allocations)
@@ -209,14 +168,6 @@ def main():
             help="Seed for reproducible supplier generation"
         )
         
-        # Model Configuration
-        st.subheader("AI Model Settings")
-        use_random_model = st.checkbox(
-            "Use Random Model (Demo Mode)",
-            value=True,
-            help="Use random model when PPO model is not available"
-        )
-        
     with col2:
         st.markdown('<div class="sub-header">📊 Real-time Dashboard</div>', unsafe_allow_html=True)
         
@@ -227,163 +178,148 @@ def main():
             progress_bar = st.progress(0)
             status_text = st.empty()
             
-            # Step 1: Generate suppliers
-            status_text.text("Step 1/5: Generating supplier data...")
-            progress_bar.progress(20)
-            
-            suppliers_result = suppliers_synthetic(n=num_suppliers, seed=seed)
-            suppliers_data = suppliers_result["suppliers"]
-            
-            # Display suppliers table
-            st.subheader("Generated Suppliers")
-            df_suppliers = pd.DataFrame(suppliers_data)
-            st.dataframe(df_suppliers.round(3), use_container_width=True)
-            
-            # Step 2: Market signals
-            status_text.text("Step 2/5: Analyzing market conditions...")
-            progress_bar.progress(40)
-            
-            market_data = market_signal(volatility, price_mult, demand_mult)
-            
-            # Display market metrics
-            col_m1, col_m2, col_m3 = st.columns(3)
-            with col_m1:
-                st.metric("Volatility", volatility.upper(), 
-                         delta="High Risk" if volatility == "high" else "Normal")
-            with col_m2:
-                st.metric("Demand Change", f"{demand_mult:.1%}", 
-                         delta=f"{(demand_mult-1)*100:+.1f}%")
-            with col_m3:
-                st.metric("Price Change", f"{price_mult:.1%}", 
-                         delta=f"{(price_mult-1)*100:+.1f}%")
-            
-            # Step 3: Check model
-            status_text.text("Step 3/5: Checking AI model availability...")
-            progress_bar.progress(60)
-            
-            # Create a mock model file for demo
-            model_path = "/tmp/mock_ppo_model.pkl"
-            if not os.path.exists(model_path):
-                # Create a simple mock model for demo
-                class MockPPOModel:
-                    def predict(self, obs, deterministic=True):
-                        # Simple allocation logic for demo
-                        np.random.seed(42)
-                        action = np.random.normal(0, 1, num_suppliers)
-                        return action, None
+            try:
+                # Step 1: Generate suppliers
+                status_text.text("Step 1/5: Generating supplier data...")
+                progress_bar.progress(20)
                 
-                with open(model_path, 'wb') as f:
-                    pickle.dump(MockPPOModel(), f)
-            
-            # Step 4: Get recommendations
-            status_text.text("Step 4/5: Getting AI recommendations...")
-            progress_bar.progress(80)
-            
-            recommendation_input = {
-                "volatility": market_data["volatility"],
-                "price_multiplier": market_data["price_multiplier"],
-                "demand_multiplier": market_data["demand_multiplier"],
-                "baseline_demand": baseline_demand,
-                "suppliers": suppliers_data,
-                "auto_align_actions": True
-            }
-            
-            # For demo purposes, create mock recommendations
-            np.random.seed(42)
-            weights = np.random.exponential(1, num_suppliers)
-            weights = weights / weights.sum()
-            
-            recommendations = {
-                "strategy": "multi" if (weights > 0.1).sum() > 2 else "dual",
-                "allocations": [
-                    {"supplier": suppliers_data[i]["name"], "share": float(weights[i])}
-                    for i in range(num_suppliers)
-                ],
-                "demand_units": float(baseline_demand * demand_mult)
-            }
-            
-            # Step 5: Create PO
-            status_text.text("Step 5/5: Creating purchase order...")
-            progress_bar.progress(100)
-            
-            po_data = {
-                "lines": [
-                    {
-                        "supplier": alloc["supplier"],
-                        "quantity": round(recommendations["demand_units"] * alloc["share"], 2)
-                    }
-                    for alloc in recommendations["allocations"]
-                    if alloc["share"] > 0.01
-                ]
-            }
-            
-            po_result = sap_create_po_mock(po_data)
-            
-            # Display results
-            status_text.text("✅ Procurement process completed!")
-            time.sleep(0.5)
-            progress_bar.empty()
-            status_text.empty()
-            
-            # Results section
-            st.markdown("---")
-            st.subheader("🎯 Procurement Results")
-            
-            # Key metrics
-            col_r1, col_r2, col_r3, col_r4 = st.columns(4)
-            with col_r1:
-                st.metric("Strategy", recommendations["strategy"].title())
-            with col_r2:
-                active_suppliers = len([a for a in recommendations["allocations"] if a["share"] > 0.01])
-                st.metric("Active Suppliers", active_suppliers)
-            with col_r3:
-                st.metric("Total Units", f"{recommendations['demand_units']:,.0f}")
-            with col_r4:
-                st.metric("PO Number", po_result["PurchaseOrder"])
-            
-            # Visualizations
-            col_v1, col_v2 = st.columns(2)
-            
-            with col_v1:
-                # Allocation pie chart
-                fig_pie = create_allocation_pie_chart(recommendations["allocations"])
-                st.plotly_chart(fig_pie, use_container_width=True)
-            
-            with col_v2:
-                # Supplier comparison radar
-                fig_radar = create_supplier_comparison_chart(suppliers_data)
-                st.plotly_chart(fig_radar, use_container_width=True)
-            
-            # Detailed allocation table
-            st.subheader("📋 Detailed Allocation")
-            allocation_df = pd.DataFrame(recommendations["allocations"])
-            allocation_df["quantity"] = allocation_df["share"] * recommendations["demand_units"]
-            allocation_df["percentage"] = allocation_df["share"] * 100
-            
-            # Merge with supplier data for additional context
-            supplier_df = pd.DataFrame(suppliers_data)
-            detailed_df = allocation_df.merge(
-                supplier_df[["name", "base_cost_per_unit", "current_quality", "financial_risk"]], 
-                left_on="supplier", right_on="name"
-            )
-            
-            st.dataframe(
-                detailed_df[["supplier", "percentage", "quantity", "base_cost_per_unit", "current_quality", "financial_risk"]]
-                .round(2), 
-                use_container_width=True
-            )
-            
-            # Purchase Order JSON
-            with st.expander("📄 View Purchase Order JSON"):
-                st.json(po_result)
-            
-            # Success message
-            st.markdown(f"""
-            <div class="success-box">
-                <strong>✅ Success!</strong> Purchase Order {po_result["PurchaseOrder"]} has been created successfully!
-                <br><em>Note: This is a demonstration. No actual SAP system was contacted.</em>
-            </div>
-            """, unsafe_allow_html=True)
+                suppliers_result = suppliers_synthetic(n=num_suppliers, seed=seed)
+                suppliers_data = suppliers_result["suppliers"]
+                
+                # Display suppliers table
+                st.subheader("Generated Suppliers")
+                df_suppliers = pd.DataFrame(suppliers_data)
+                st.dataframe(df_suppliers.round(3), use_container_width=True)
+                
+                # Step 2: Market signals
+                status_text.text("Step 2/5: Analyzing market conditions...")
+                progress_bar.progress(40)
+                
+                market_data = market_signal(volatility, price_mult, demand_mult)
+                
+                # Display market metrics
+                col_m1, col_m2, col_m3 = st.columns(3)
+                with col_m1:
+                    st.metric("Volatility", volatility.upper(), 
+                             delta="High Risk" if volatility == "high" else "Normal")
+                with col_m2:
+                    st.metric("Demand Change", f"{demand_mult:.1%}", 
+                             delta=f"{(demand_mult-1)*100:+.1f}%")
+                with col_m3:
+                    st.metric("Price Change", f"{price_mult:.1%}", 
+                             delta=f"{(price_mult-1)*100:+.1f}%")
+                
+                # Step 3: Check model
+                status_text.text("Step 3/5: Checking AI model availability...")
+                progress_bar.progress(60)
+                
+                model_check = check_model_tool("./supplier_selection_ppo_gymnasium.pkl")
+                
+                # Step 4: Get recommendations
+                status_text.text("Step 4/5: Getting AI recommendations...")
+                progress_bar.progress(80)
+                
+                recommendation_input = {
+                    "volatility": market_data["volatility"],
+                    "price_multiplier": market_data["price_multiplier"],
+                    "demand_multiplier": market_data["demand_multiplier"],
+                    "baseline_demand": baseline_demand,
+                    "suppliers": suppliers_data,
+                    "auto_align_actions": True
+                }
+                
+                recommendations = rl_recommend_tool(recommendation_input)
+                
+                if recommendations.get("strategy") == "error":
+                    st.error(f"AI recommendation failed: {recommendations.get('error', 'Unknown error')}")
+                    return
+                
+                # Step 5: Create PO
+                status_text.text("Step 5/5: Creating purchase order...")
+                progress_bar.progress(100)
+                
+                po_data = {
+                    "lines": [
+                        {
+                            "supplier": alloc["supplier"],
+                            "quantity": round(recommendations["demand_units"] * alloc["share"], 2)
+                        }
+                        for alloc in recommendations["allocations"]
+                        if alloc["share"] > 0.01
+                    ]
+                }
+                
+                po_result = sap_create_po_mock(po_data)
+                
+                # Clear progress indicators
+                status_text.text("✅ Procurement process completed!")
+                time.sleep(0.5)
+                progress_bar.empty()
+                status_text.empty()
+                
+                # Display results
+                st.markdown("---")
+                st.subheader("🎯 Procurement Results")
+                
+                # Key metrics
+                col_r1, col_r2, col_r3, col_r4 = st.columns(4)
+                with col_r1:
+                    st.metric("Strategy", recommendations["strategy"].title())
+                with col_r2:
+                    active_suppliers = len([a for a in recommendations["allocations"] if a["share"] > 0.01])
+                    st.metric("Active Suppliers", active_suppliers)
+                with col_r3:
+                    st.metric("Total Units", f"{recommendations['demand_units']:,.0f}")
+                with col_r4:
+                    st.metric("PO Number", po_result["PurchaseOrder"])
+                
+                # Visualizations
+                col_v1, col_v2 = st.columns(2)
+                
+                with col_v1:
+                    # Allocation pie chart
+                    fig_pie = create_allocation_pie_chart(recommendations["allocations"])
+                    st.plotly_chart(fig_pie, use_container_width=True)
+                
+                with col_v2:
+                    # Supplier comparison radar
+                    fig_radar = create_supplier_comparison_chart(suppliers_data)
+                    st.plotly_chart(fig_radar, use_container_width=True)
+                
+                # Detailed allocation table
+                st.subheader("📋 Detailed Allocation")
+                allocation_df = pd.DataFrame(recommendations["allocations"])
+                allocation_df["quantity"] = allocation_df["share"] * recommendations["demand_units"]
+                allocation_df["percentage"] = allocation_df["share"] * 100
+                
+                # Merge with supplier data for additional context
+                supplier_df = pd.DataFrame(suppliers_data)
+                detailed_df = allocation_df.merge(
+                    supplier_df[["name", "base_cost_per_unit", "current_quality", "financial_risk"]], 
+                    left_on="supplier", right_on="name"
+                )
+                
+                st.dataframe(
+                    detailed_df[["supplier", "percentage", "quantity", "base_cost_per_unit", "current_quality", "financial_risk"]]
+                    .round(2), 
+                    use_container_width=True
+                )
+                
+                # Purchase Order JSON
+                with st.expander("📄 View Purchase Order JSON"):
+                    st.json(po_result)
+                
+                # Success message
+                st.markdown(f"""
+                <div class="success-box">
+                    <strong>✅ Success!</strong> Purchase Order {po_result["PurchaseOrder"]} has been created successfully!
+                    <br><em>Note: This is a demonstration. No actual SAP system was contacted.</em>
+                </div>
+                """, unsafe_allow_html=True)
+                
+            except Exception as e:
+                st.error(f"Error during execution: {str(e)}")
+                st.exception(e)
 
     # Sidebar with information
     with st.sidebar: