Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,322 @@
-import altair as alt
-import numpy as np
-import pandas as pd
 import streamlit as st
+import os
+from smolagents import CodeAgent, LiteLLMModel, tool
+import json
+from datetime import datetime
+import random
 
-"""
-# Welcome to Streamlit!
-
-Edit `/streamlit_app.py` to customize this app to your heart's desire :heart:.
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
-
-In the meantime, below is an example of what you can do with just a few lines of code:
-"""
-
-num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
-num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
-
-indices = np.linspace(0, 1, num_points)
-theta = 2 * np.pi * num_turns * indices
-radius = indices
-
-x = radius * np.cos(theta)
-y = radius * np.sin(theta)
-
-df = pd.DataFrame({
-    "x": x,
-    "y": y,
-    "idx": indices,
-    "rand": np.random.randn(num_points),
-})
-
-st.altair_chart(alt.Chart(df, height=700, width=700)
-    .mark_point(filled=True)
-    .encode(
-        x=alt.X("x", axis=None),
-        y=alt.Y("y", axis=None),
-        color=alt.Color("idx", legend=None, scale=alt.Scale()),
-        size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
-    ))
+# Hugging Face Spaces configuration
+st.set_page_config(
+    page_title="AI Procurement Agent", 
+    page_icon="🤖", 
+    layout="wide"
+)
+
+# Initialize OpenAI model for Spaces
+@st.cache_resource
+def get_model():
+    # Try to get API key from Spaces secrets or environment
+    api_key = os.environ.get("OPENAI_API_KEY")
+    if not api_key:
+        st.error("⚠️ OpenAI API Key not found in Spaces secrets!")
+        st.info("Add OPENAI_API_KEY to your Spaces secrets in Settings.")
+        st.stop()
+    
+    return LiteLLMModel(
+        model_id="gpt-4",
+        api_key=api_key,
+        temperature=0.2
+    )
+
+# Tool 1: Query SAP Purchase Requisition
+@tool
+def query_sap_pr(pr_number: str) -> str:
+    """Query SAP purchase requisition details"""
+    materials = ["Steel Pipes", "Cement", "Electrical Cables", "Safety Equipment"]
+    mock_data = {
+        "pr_number": pr_number,
+        "materials": f"{random.choice(materials)} - {random.randint(50, 200)} units",
+        "total_value": random.randint(30000, 80000),
+        "requestor": random.choice(["John Doe", "Jane Smith", "Mike Johnson"]),
+        "department": random.choice(["Construction", "Manufacturing", "Maintenance"]),
+        "urgency": random.choice(["High", "Medium", "Low"]),
+        "delivery_date": "2025-10-15",
+        "status": "Approved"
+    }
+    return json.dumps(mock_data, indent=2)
+
+# Tool 2: Check Vendor Financial Health
+@tool  
+def check_vendor_solvency(vendor_name: str) -> str:
+    """Check vendor financial solvency from external sources"""
+    scores = ["A+", "A", "A-", "B+", "B"]
+    risk_levels = ["Low", "Medium", "High"]
+    mock_data = {
+        "vendor_name": vendor_name,
+        "credit_rating": random.choice(scores),
+        "financial_stability": "Stable",
+        "solvency_score": random.randint(65, 95),
+        "debt_ratio": round(random.uniform(0.2, 0.6), 2),
+        "risk_level": random.choice(risk_levels[:2]),  # Mostly Low/Medium
+        "recommendation": "Approved for business"
+    }
+    return json.dumps(mock_data, indent=2)
+
+# Tool 3: Get Vendor Performance History
+@tool
+def get_vendor_performance(vendor_name: str) -> str:
+    """Get historical vendor performance metrics"""
+    mock_data = {
+        "vendor_name": vendor_name,
+        "evaluation_period": "12 months",
+        "total_orders": random.randint(15, 80),
+        "on_time_delivery": f"{random.randint(85, 98)}%",
+        "quality_score": round(random.uniform(3.5, 5.0), 1),
+        "order_accuracy": f"{random.randint(92, 99)}%",
+        "avg_lead_time": f"{random.randint(7, 21)} days",
+        "overall_rating": round(random.uniform(3.5, 4.8), 1),
+        "issues_resolved": random.randint(0, 3)
+    }
+    return json.dumps(mock_data, indent=2)
+
+# Tool 4: Compare Material Rates
+@tool
+def compare_material_rates(material: str) -> str:
+    """Compare current market rates for materials"""
+    vendors = ["Alpha Corp", "Beta Supplies", "Gamma Materials", "Delta Industries"]
+    base_price = random.randint(80, 250)
+    
+    quotes = []
+    for vendor in random.sample(vendors, 3):
+        quotes.append({
+            "vendor": vendor,
+            "price_per_unit": base_price + random.randint(-25, 30),
+            "lead_time": f"{random.randint(5, 20)} days",
+            "min_order": random.randint(10, 50)
+        })
+    
+    mock_data = {
+        "material": material,
+        "market_average": base_price,
+        "vendor_quotes": quotes,
+        "best_value_vendor": min(quotes, key=lambda x: x["price_per_unit"])["vendor"],
+        "price_trend": random.choice(["Stable", "Increasing", "Decreasing"])
+    }
+    return json.dumps(mock_data, indent=2)
+
+# Tool 5: Optimize Vendor Selection
+@tool
+def optimize_vendor_selection(requirements: str) -> str:
+    """Run optimization to find best vendor combination"""
+    vendors = ["ABC Supplies", "XYZ Materials", "DEF Corp", "GHI Industries"]
+    selected_vendor = random.choice(vendors)
+    
+    mock_data = {
+        "requirements_analyzed": requirements,
+        "recommended_vendor": selected_vendor,
+        "optimization_score": round(random.uniform(7.5, 9.5), 1),
+        "total_estimated_cost": random.randint(40000, 70000),
+        "potential_savings": f"{random.randint(8, 18)}%",
+        "risk_assessment": "Low to Medium",
+        "confidence_level": f"{random.randint(85, 96)}%",
+        "key_factors": ["Cost efficiency", "Quality track record", "Delivery reliability"]
+    }
+    return json.dumps(mock_data, indent=2)
+
+# Tool 6: Create SAP Purchase Order
+@tool
+def create_purchase_order(vendor: str, amount: str) -> str:
+    """Create purchase order in SAP system"""
+    po_number = f"PO{datetime.now().strftime('%Y%m%d%H%M%S')[-8:]}"
+    
+    mock_data = {
+        "po_number": po_number,
+        "vendor": vendor,
+        "total_amount": amount,
+        "currency": "USD",
+        "status": "Created Successfully",
+        "payment_terms": "NET 30",
+        "delivery_terms": "FOB Destination",
+        "created_by": "AI_Procurement_Agent",
+        "created_at": datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
+        "approval_required": random.choice([True, False])
+    }
+    return json.dumps(mock_data, indent=2)
+
+# Initialize Agent
+@st.cache_resource
+def get_agent():
+    tools = [
+        query_sap_pr,
+        check_vendor_solvency, 
+        get_vendor_performance,
+        compare_material_rates,
+        optimize_vendor_selection,
+        create_purchase_order
+    ]
+    
+    return CodeAgent(
+        tools=tools,
+        model=get_model(),
+        max_iterations=8
+    )
+
+def main():
+    # Header
+    st.title("🤖 AI Procurement Agent")
+    st.markdown("*Demo powered by Hugging Face SmolAgents + OpenAI GPT-4*")
+    
+    # Add info about the demo
+    with st.expander("ℹ️ About this Demo"):
+        st.markdown("""
+        This AI agent can help automate procurement workflows by:
+        - Querying purchase requisitions
+        - Evaluating vendor financial health
+        - Analyzing vendor performance history
+        - Comparing material market rates
+        - Optimizing vendor selection
+        - Creating purchase orders
+        
+        **Note:** This uses mock data for demonstration purposes.
+        """)
+    
+    # Sidebar
+    with st.sidebar:
+        st.header("🎯 Demo Scenarios")
+        scenario = st.selectbox(
+            "Choose a workflow:",
+            [
+                "🔄 Full Procurement Workflow",
+                "📊 Vendor Evaluation", 
+                "💰 Price Analysis",
+                "🤖 Custom AI Query"
+            ]
+        )
+        
+        st.markdown("---")
+        st.markdown("**🚀 Deployed on Hugging Face Spaces**")
+        
+    # Main content
+    col1, col2 = st.columns([1, 1])
+    
+    with col1:
+        st.header("📝 Input")
+        
+        if scenario == "🔄 Full Procurement Workflow":
+            pr_number = st.text_input("Enter PR Number:", value="PR-2025-001")
+            
+            if st.button("🚀 Start Full Workflow", type="primary", use_container_width=True):
+                process_full_workflow(pr_number)
+                
+        elif scenario == "📊 Vendor Evaluation":
+            vendor_name = st.text_input("Enter Vendor Name:", value="ABC Supplies")
+            
+            if st.button("📊 Evaluate This Vendor", use_container_width=True):
+                evaluate_vendor(vendor_name)
+                
+        elif scenario == "💰 Price Analysis":
+            material = st.text_input("Enter Material Type:", value="Steel Pipes")
+            
+            if st.button("💰 Analyze Market Prices", use_container_width=True):
+                analyze_prices(material)
+                
+        else:  # Custom Query
+            custom_query = st.text_area(
+                "Enter your procurement query:",
+                value="Evaluate vendor XYZ Corp and check if they're suitable for a 50K USD steel pipes order",
+                height=80
+            )
+            
+            if st.button("🤖 Ask AI Agent", use_container_width=True):
+                run_custom_query(custom_query)
+    
+    with col2:
+        st.header("🤖 AI Agent Response")
+        
+        if 'agent_response' not in st.session_state:
+            st.info("👈 Select a scenario and click a button to see the AI agent in action!")
+        else:
+            with st.container():
+                st.success("✅ Agent completed successfully!")
+                
+                # Display response in an expandable section
+                with st.expander("📋 Full Agent Response", expanded=True):
+                    st.markdown(st.session_state['agent_response'])
+
+def process_full_workflow(pr_number):
+    """Process complete procurement workflow"""
+    with st.spinner("🤖 AI Agent is working on your procurement request..."):
+        prompt = f"""
+        Execute a complete procurement workflow for PR {pr_number}:
+        
+        1. Query the SAP purchase requisition details
+        2. For any vendors found, check their financial solvency
+        3. Review their historical performance 
+        4. Compare current market rates for the materials
+        5. Run optimization to select the best vendor option
+        6. Create a purchase order for the recommended vendor
+        
+        Provide a clear summary with your final recommendation and reasoning.
+        """
+        
+        try:
+            agent = get_agent()
+            result = agent.run(prompt)
+            st.session_state['agent_response'] = result
+            st.rerun()
+        except Exception as e:
+            st.error(f"Error: {str(e)}")
+
+def evaluate_vendor(vendor_name):
+    """Evaluate specific vendor"""
+    with st.spinner(f"🤖 Evaluating {vendor_name}..."):
+        prompt = f"""
+        Conduct a comprehensive evaluation of vendor '{vendor_name}':
+        
+        1. Check their financial solvency and credit rating
+        2. Analyze their historical performance metrics
+        3. Provide a clear recommendation on working with them
+        4. Highlight any risks or benefits
+        """
+        
+        try:
+            agent = get_agent()
+            result = agent.run(prompt)
+            st.session_state['agent_response'] = result
+            st.rerun()
+        except Exception as e:
+            st.error(f"Error: {str(e)}")
+
+def analyze_prices(material):
+    """Analyze material prices"""
+    with st.spinner(f"🤖 Analyzing prices for {material}..."):
+        prompt = f"""
+        Perform price analysis for '{material}':
+        
+        1. Get current market rates from multiple vendors
+        2. Compare pricing and identify the best value option
+        3. Consider factors like lead time and minimum orders
+        4. Provide pricing recommendations
+        """
+        
+        try:
+            agent = get_agent()
+            result = agent.run(prompt)
+            st.session_state['agent_response'] = result
+            st.rerun()
+        except Exception as e:
+            st.error(f"Error: {str(e)}")
+
+def run_custom_query(query):
+    """Run custom user query"""
+    with st.spinner("🤖 Processing your custom query..."):
+        try:
+            agent = get_agent()
+            result = agent.run(query)
+            st.session_state['agent_response'] = result
+            st.rerun()
+        except Exception as e:
+            st.error(f"Error: {str(e)}")
+
+if __name__ == "__main__":
+    main()