Fix: Security Scan Vulnerabilities (#136)

Browser-based-agents/Notte-labs-agent/Nike-products-agent/agent.py

@@ -7,7 +7,6 @@
 
 import os
 from enum import Enum
-import asyncio
 
 from uagents import Agent, Context, Model
 from uagents.experimental.quota import QuotaProtocol, RateLimit
@@ -17,11 +16,7 @@
 from nike_scraper import get_nike_info_enhanced, NikeScrapeRequest, NikeScrapeResponse
 
 # Create the agent with mailbox=True for ASI1 discoverability
-agent = Agent(
-    name="nike_scraper",
-    port=8000,
-    mailbox=True        
-)
+agent = Agent(name="nike_scraper", port=8000, mailbox=True)
 
 # Print the agent's address for reference
 print(f"Your Nike Scraper agent's address is: {agent.address}")
@@ -34,29 +29,31 @@
     default_rate_limit=RateLimit(window_size_minutes=60, max_requests=30),
 )
 
+
 # Handle direct Nike scraper requests (without natural language processing)
-@proto.on_message(
-    NikeScrapeRequest, replies={NikeScrapeResponse, ErrorMessage}
-)
+@proto.on_message(NikeScrapeRequest, replies={NikeScrapeResponse, ErrorMessage})
 async def handle_request(ctx: Context, sender: str, msg: NikeScrapeRequest):
     """
     Handle direct structured requests for Nike product information.
     This allows other agents to communicate directly with this agent
     using the NikeScrapeRequest model.
     """
-    ctx.logger.info(f"Received Nike scrape request: action={msg.action}, category={msg.category_name}")
-
+    ctx.logger.info(
+        f"Received Nike scrape request: action={msg.action}, category={msg.category_name}"
+    )
+
     try:
         # Process the request using the nike_scraper module
         nike_response = await get_nike_info_enhanced(request=msg)
-        
-        ctx.logger.info(f"Successfully processed Nike request")
+
+        ctx.logger.info("Successfully processed Nike request")
         await ctx.send(sender, NikeScrapeResponse(results=nike_response.results))
-        
+
     except Exception as err:
         ctx.logger.error(f"Error in handle_request: {err}")
         await ctx.send(sender, ErrorMessage(error=str(err)))
 
+
 # Health check functionality
 def agent_is_healthy() -> bool:
     """
@@ -65,26 +62,32 @@ def agent_is_healthy() -> bool:
     """
     try:
         import requests
+
         response = requests.get("https://www.nike.com", timeout=5)
         return response.status_code == 200
     except Exception:
         return False
 
+
 class HealthCheck(Model):
     pass
 
+
 class HealthStatus(str, Enum):
     HEALTHY = "healthy"
     UNHEALTHY = "unhealthy"
 
+
 class AgentHealth(Model):
     agent_name: str
     status: HealthStatus
 
+
 health_protocol = QuotaProtocol(
     storage_reference=agent.storage, name="HealthProtocol", version="0.1.0"
 )
 
+
 @health_protocol.on_message(HealthCheck, replies={AgentHealth})
 async def handle_health_check(ctx: Context, sender: str, msg: HealthCheck):
     """Handle health check requests to monitor agent status."""
@@ -97,6 +100,7 @@ async def handle_health_check(ctx: Context, sender: str, msg: HealthCheck):
     finally:
         await ctx.send(sender, AgentHealth(agent_name="nike_scraper", status=status))
 
+
 # Register all protocols with the agent
 agent.include(proto, publish_manifest=True)
 agent.include(health_protocol, publish_manifest=True)
@@ -108,10 +112,12 @@ async def handle_health_check(ctx: Context, sender: str, msg: HealthCheck):
     if not os.environ.get("NOTTE_API_KEY"):
         print("Warning: NOTTE_API_KEY environment variable is not set.")
         print("The agent will run but scraping functionality will be limited.")
-    
+
     # Run the agent
     print("Starting Nike Scraper Agent...")
-    print("This agent can respond to natural language queries about Nike products via ASI1 LLM.")
+    print(
+        "This agent can respond to natural language queries about Nike products via ASI1 LLM."
+    )
     print("Example queries:")
     print("- Show me Nike categories")
     print("- List Nike basketball shoes")

Browser-based-agents/Notte-labs-agent/Nike-products-agent/chat_proto.py

@@ -18,12 +18,12 @@
     chat_protocol_spec,
 )
 
-from nike_scraper import get_nike_info_enhanced, NikeScrapeRequest, NikeScrapeResponse
+from nike_scraper import get_nike_info_enhanced, NikeScrapeRequest
 
 # Replace the AI Agent Address with one of the LLMs that support StructuredOutput
 # OpenAI Agent: agent1q0h70caed8ax769shpemapzkyk65uscw4xwk6dc4t3emvp5jdcvqs9xs32y
 # Claude.ai Agent: agent1qvk7q2av3e2y5gf5s90nfzkc8a48q3wdqeevwrtgqfdl0k78rspd6f2l4dx
-AI_AGENT_ADDRESS = 'agent1qtlpfshtlcxekgrfcpmv7m9zpajuwu7d5jfyachvpa4u3dkt6k0uwwp2lct'
+AI_AGENT_ADDRESS = "agent1qtlpfshtlcxekgrfcpmv7m9zpajuwu7d5jfyachvpa4u3dkt6k0uwwp2lct"
 
 if not AI_AGENT_ADDRESS:
     raise ValueError("AI_AGENT_ADDRESS not set")
@@ -69,7 +69,9 @@ async def handle_message(ctx: Context, sender: str, msg: ChatMessage):
     ctx.storage.set(str(ctx.session), sender)
     await ctx.send(
         sender,
-        ChatAcknowledgement(timestamp=datetime.utcnow(), acknowledged_msg_id=msg.msg_id),
+        ChatAcknowledgement(
+            timestamp=datetime.utcnow(), acknowledged_msg_id=msg.msg_id
+        ),
     )
 
     for item in msg.content:
@@ -110,7 +112,12 @@ async def handle_structured_output_response(
         return
 
     # Check for empty or clearly invalid output from LLM
-    if not msg.output or "<UNKNOWN>" in str(msg.output) or not isinstance(msg.output, dict) or not msg.output.get('action'):
+    if (
+        not msg.output
+        or "<UNKNOWN>" in str(msg.output)
+        or not isinstance(msg.output, dict)
+        or not msg.output.get("action")
+    ):
         await ctx.send(
             session_sender,
             create_text_chat(
@@ -123,7 +130,7 @@ async def handle_structured_output_response(
         # Parse the structured output from the LLM
         try:
             request = NikeScrapeRequest.parse_obj(msg.output)
-        except Exception as p_err: # Catch Pydantic validation errors or others
+        except Exception as p_err:  # Catch Pydantic validation errors or others
             ctx.logger.error(f"Error parsing LLM output: {msg.output}. Error: {p_err}")
             await ctx.send(
                 session_sender,
@@ -132,14 +139,14 @@ async def handle_structured_output_response(
                 ),
             )
             return
-        
+
         # Get the Nike information based on the structured request
         nike_response = await get_nike_info_enhanced(request=request)
-        
+
         # Send the response back to the user
         chat_message = create_text_chat(nike_response.results)
         await ctx.send(session_sender, chat_message)
-        
+
     except Exception as err:
         ctx.logger.error(f"Error processing Nike request: {err}")
         await ctx.send(