Update src/streamlit_app.py

src/streamlit_app.py

@@ -15,7 +15,6 @@ st.set_page_config(
 # 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!")
@@ -31,7 +30,12 @@ def get_model():
 # Tool 1: Query SAP Purchase Requisition
 @tool
 def query_sap_pr(pr_number: str) -> str:
-    """Query SAP purchase requisition details"""
+    """
+    Query SAP purchase requisition details
+    
+    Args:
+        pr_number: The purchase requisition number to query
+    """
     materials = ["Steel Pipes", "Cement", "Electrical Cables", "Safety Equipment"]
     mock_data = {
         "pr_number": pr_number,
@@ -48,7 +52,12 @@ def query_sap_pr(pr_number: str) -> str:
 # Tool 2: Check Vendor Financial Health
 @tool  
 def check_vendor_solvency(vendor_name: str) -> str:
-    """Check vendor financial solvency from external sources"""
+    """
+    Check vendor financial solvency from external sources
+    
+    Args:
+        vendor_name: Name of the vendor to check financial solvency for
+    """
     scores = ["A+", "A", "A-", "B+", "B"]
     risk_levels = ["Low", "Medium", "High"]
     mock_data = {
@@ -57,7 +66,7 @@ def check_vendor_solvency(vendor_name: str) -> str:
         "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
+        "risk_level": random.choice(risk_levels[:2]),
         "recommendation": "Approved for business"
     }
     return json.dumps(mock_data, indent=2)
@@ -65,7 +74,12 @@ def check_vendor_solvency(vendor_name: str) -> str:
 # Tool 3: Get Vendor Performance History
 @tool
 def get_vendor_performance(vendor_name: str) -> str:
-    """Get historical vendor performance metrics"""
+    """
+    Get historical vendor performance metrics
+    
+    Args:
+        vendor_name: Name of the vendor to get performance history for
+    """
     mock_data = {
         "vendor_name": vendor_name,
         "evaluation_period": "12 months",
@@ -82,7 +96,12 @@ def get_vendor_performance(vendor_name: str) -> str:
 # Tool 4: Compare Material Rates
 @tool
 def compare_material_rates(material: str) -> str:
-    """Compare current market rates for materials"""
+    """
+    Compare current market rates for materials
+    
+    Args:
+        material: Type of material to compare rates for
+    """
     vendors = ["Alpha Corp", "Beta Supplies", "Gamma Materials", "Delta Industries"]
     base_price = random.randint(80, 250)
     
@@ -107,7 +126,12 @@ def compare_material_rates(material: str) -> str:
 # Tool 5: Optimize Vendor Selection
 @tool
 def optimize_vendor_selection(requirements: str) -> str:
-    """Run optimization to find best vendor combination"""
+    """
+    Run optimization to find best vendor combination
+    
+    Args:
+        requirements: Procurement requirements and constraints to optimize for
+    """
     vendors = ["ABC Supplies", "XYZ Materials", "DEF Corp", "GHI Industries"]
     selected_vendor = random.choice(vendors)
     
@@ -126,7 +150,13 @@ def optimize_vendor_selection(requirements: str) -> str:
 # Tool 6: Create SAP Purchase Order
 @tool
 def create_purchase_order(vendor: str, amount: str) -> str:
-    """Create purchase order in SAP system"""
+    """
+    Create purchase order in SAP system
+    
+    Args:
+        vendor: Name of the vendor to create purchase order for
+        amount: Total amount for the purchase order
+    """
     po_number = f"PO{datetime.now().strftime('%Y%m%d%H%M%S')[-8:]}"
     
     mock_data = {
@@ -239,7 +269,6 @@ def main():
             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'])