Create app.py

app.py

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+import streamlit as st
+import json
+import pandas as pd
+import plotly.express as px
+import plotly.graph_objects as go
+from datetime import datetime
+import time
+from agent import ProcurementRLAgent
+from tools import (
+    InventoryTool, ExternalRiskTool, ContractTool, 
+    POExecutionTool, HumanNotificationTool
+)
+
+# Page configuration
+st.set_page_config(
+    page_title="Agentic AI Procurement Demo",
+    page_icon="🤖",
+    layout="wide",
+    initial_sidebar_state="expanded"
+)
+
+# Custom CSS for better styling
+st.markdown("""
+<style>
+    .main-header {
+        font-size: 2.5rem;
+        color: #1f77b4;
+        text-align: center;
+        margin-bottom: 2rem;
+    }
+    .decision-box {
+        background-color: #f0f8ff;
+        border-left: 5px solid #1f77b4;
+        padding: 1rem;
+        margin: 1rem 0;
+    }
+    .agent-thinking {
+        background-color: #fff3cd;
+        border: 1px solid #ffeaa7;
+        padding: 1rem;
+        border-radius: 5px;
+    }
+    .success-box {
+        background-color: #d4edda;
+        border-left: 5px solid #28a745;
+        padding: 1rem;
+        margin: 1rem 0;
+    }
+    .warning-box {
+        background-color: #f8d7da;
+        border-left: 5px solid #dc3545;
+        padding: 1rem;
+        margin: 1rem 0;
+    }
+</style>
+""", unsafe_allow_html=True)
+
+# Initialize tools
+@st.cache_resource
+def initialize_agent():
+    tools = [
+        InventoryTool(),
+        ExternalRiskTool(),
+        ContractTool(),
+        POExecutionTool(),
+        HumanNotificationTool()
+    ]
+    return ProcurementRLAgent(tools=tools)
+
+agent = initialize_agent()
+
+# Title
+st.markdown('<h1 class="main-header">🤖 Agentic AI Procurement Assistant</h1>', unsafe_allow_html=True)
+
+# Sidebar for demo controls
+with st.sidebar:
+    st.header("Demo Controls")
+    
+    # Sample scenarios
+    scenarios = {
+        "Standard Office Supplies": {
+            "PR_ID": "PR-12345",
+            "Item": "Ergonomic Office Chairs",
+            "Category": "Office Supplies",
+            "Quantity": 25,
+            "Urgency": "Medium",
+            "Budget": 12500.0,
+            "Current_Inventory": 5,
+            "Contract_Status": "Valid",
+            "External_Disruption": False,
+            "Supplier_History": "Good"
+        },
+        "Urgent IT Equipment": {
+            "PR_ID": "PR-67890",
+            "Item": "Enterprise Laptops",
+            "Category": "IT Equipment",
+            "Quantity": 50,
+            "Urgency": "High",
+            "Budget": 75000.0,
+            "Current_Inventory": 10,
+            "Contract_Status": "Expired",
+            "External_Disruption": False,
+            "Supplier_History": "Excellent"
+        },
+        "Disrupted Raw Materials": {
+            "PR_ID": "PR-11111",
+            "Item": "Steel Components",
+            "Category": "Raw Materials",
+            "Quantity": 100,
+            "Urgency": "High",
+            "Budget": 50000.0,
+            "Current_Inventory": 0,
+            "Contract_Status": "Valid",
+            "External_Disruption": True,
+            "Supplier_History": "Average"
+        }
+    }
+    
+    selected_scenario = st.selectbox("Select Demo Scenario:", list(scenarios.keys()))
+    pr_data = scenarios[selected_scenario]
+    
+    st.header("Model Performance")
+    
+    # Load historical data for metrics
+    try:
+        with open("demo_space/historical_procurement_data.json", "r") as f:
+            historical_data = json.load(f)
+        
+        df = pd.DataFrame(historical_data)
+        avg_delivery = df['delivery_performance'].mean()
+        avg_quality = df['quality_score'].mean()
+        
+        st.metric("Avg Delivery Performance", f"{avg_delivery:.2%}")
+        st.metric("Avg Quality Score", f"{avg_quality:.2%}")
+        st.metric("Training Samples", len(historical_data))
+    except FileNotFoundError:
+        st.warning("Historical data not found")
+
+# Main content area
+col1, col2 = st.columns([1, 1])
+
+with col1:
+    st.subheader("📋 Purchase Requisition Details")
+    
+    # Display PR information in a nice format
+    pr_display = {
+        "PR ID": pr_data["PR_ID"],
+        "Item": pr_data["Item"],
+        "Category": pr_data["Category"],
+        "Quantity": pr_data["Quantity"],
+        "Urgency": pr_data["Urgency"],
+        "Budget": f"${pr_data['Budget']:,.2f}",
+        "Current Inventory": pr_data["Current_Inventory"],
+        "Contract Status": pr_data["Contract_Status"],
+        "External Disruption": "⚠️ Yes" if pr_data["External_Disruption"] else "✅ No",
+        "Supplier History": pr_data["Supplier_History"]
+    }
+    
+    for key, value in pr_display.items():
+        st.write(f"**{key}:** {value}")
+
+with col2:
+    st.subheader("🎯 Agent Decision Engine")
+    
+    if st.button("🚀 Execute Agent Decision", type="primary"):
+        # Show thinking process
+        with st.spinner("Agent analyzing procurement request..."):
+            progress_bar = st.progress(0)
+            status_text = st.empty()
+            
+            # Simulate agent thinking process
+            thinking_steps = [
+                "Analyzing purchase requisition...",
+                "Checking inventory levels...",
+                "Assessing external risks...",
+                "Validating contract status...",
+                "Computing optimal action...",
+                "Preparing recommendation..."
+            ]
+            
+            for i, step in enumerate(thinking_steps):
+                status_text.text(step)
+                progress_bar.progress((i + 1) / len(thinking_steps))
+                time.sleep(0.5)
+            
+            # Get agent decision
+            decision_result = agent.decide(pr_data)
+            
+            # Clear progress indicators
+            progress_bar.empty()
+            status_text.empty()
+        
+        # Display decision
+        if decision_result["action_type"] == "AUTO_EXECUTE":
+            st.markdown(f"""
+            <div class="success-box">
+                <h4>✅ Automatic Execution</h4>
+                <p><strong>Action:</strong> {decision_result['action']}</p>
+                <p><strong>Reason:</strong> {decision_result['reason']}</p>
+                <p><strong>Confidence:</strong> {decision_result['confidence']:.1%}</p>
+            </div>
+            """, unsafe_allow_html=True)
+        else:
+            st.markdown(f"""
+            <div class="warning-box">
+                <h4>⚠️ Human Review Required</h4>
+                <p><strong>Issue:</strong> {decision_result['action']}</p>
+                <p><strong>Reason:</strong> {decision_result['reason']}</p>
+                <p><strong>Recommended Action:</strong> {decision_result.get('recommendation', 'Review and approve manually')}</p>
+            </div>
+            """, unsafe_allow_html=True)
+        
+        # Show tool execution logs
+        st.subheader("🔍 Agent Analysis Log")
+        for log_entry in decision_result["logs"]:
+            st.text(f"[{log_entry['timestamp']}] {log_entry['tool']}: {log_entry['result']}")
+
+# Performance Dashboard
+st.subheader("📊 Model Performance Dashboard")
+
+col3, col4, col5 = st.columns(3)
+
+try:
+    with open("demo_space/historical_procurement_data.json", "r") as f:
+        historical_data = json.load(f)
+    
+    df = pd.DataFrame(historical_data)
+    
+    with col3:
+        # Delivery performance by urgency
+        delivery_by_urgency = df.groupby('urgency')['delivery_performance'].mean().reset_index()
+        fig1 = px.bar(delivery_by_urgency, x='urgency', y='delivery_performance',
+                     title='Delivery Performance by Urgency',
+                     color='delivery_performance', color_continuous_scale='RdYlGn')
+        st.plotly_chart(fig1, use_container_width=True)
+    
+    with col4:
+        # Cost distribution by category
+        fig2 = px.box(df, x='category', y='cost', title='Cost Distribution by Category')
+        fig2.update_xaxis(tickangle=45)
+        st.plotly_chart(fig2, use_container_width=True)
+    
+    with col5:
+        # Quality vs Delivery Performance
+        fig3 = px.scatter(df, x='delivery_performance', y='quality_score',
+                         color='urgency', size='cost',
+                         title='Quality vs Delivery Performance',
+                         hover_data=['supplier'])
+        st.plotly_chart(fig3, use_container_width=True)
+
+except FileNotFoundError:
+    st.info("Historical data not available for performance dashboard")
+
+# Footer
+st.markdown("---")
+st.markdown("**Powered by:** Reinforcement Learning, smolagents, and Streamlit | **Demo Version:** 1.0")