some code and README update based on PR

recipes/use_cases/gmail_agent/README.md

@@ -301,7 +301,7 @@ Tool calling returned: [{'message_id': '1936ef72ad3f30e8', 'sender': 'gmagent_te
 # TODOs
 
 1. Port the app to using [Llama Stack](https://github.com/meta-llama/llama-stack) Agents API.
-2. Improve the search, reply, forward, create email draft, and query about attachments to cover all listed and other examples in `functions_prompt.py`.
+2. Improve the search, reply, forward, create email draft, and query about types of attachments.
 3. Improve the fallback and error handling mechanism when the user asks don't lead to a correct function calling spec or the function calling fails. 
 4. Improve the user experience by showing progress when some Gmail search API calls take long (minutes) to complete.
 5. Implement the async behavior of Gmagent - schedule an email to be sent later.
@@ -311,17 +311,17 @@ Tool calling returned: [{'message_id': '1936ef72ad3f30e8', 'sender': 'gmagent_te
 9. Introduce multiple-agent collaboration.
 10. Implement the agent observability. 
 11. Compare different agent frameworks using Gmagent as the case study.
-12. Productionize Gmagent.
+12. Add and implement a test plan and productionize Gmagent.
 
 
 # Resources
 1. Lilian Weng's blog [LLM Powered Autonomous Agents](https://lilianweng.github.io/posts/2023-06-23-agent/) 
-2. Andrew Ng's posts [Agentic Design Patterns](https://www.deeplearning.ai/the-batch/how-agents-can-improve-llm-performance/)
+2. Andrew Ng's posts [Agentic Design Patterns](https://www.deeplearning.ai/the-batch/how-agents-can-improve-llm-performance/) with basic [implementations from scratch](https://github.com/neural-maze/agentic_patterns).
 3. LangChain's survey [State of AI Agents](https://www.langchain.com/stateofaiagents)
 4. Deloitte's report [AI agents and multiagent systems](https://www2.deloitte.com/content/dam/Deloitte/us/Documents/consulting/us-ai-institute-generative-ai-agents-multiagent-systems.pdf)
 5. Letta's blog [The AI agents stack](https://www.letta.com/blog/ai-agents-stack)
 6. Microsoft's multi-agent system [Magentic-One](https://www.microsoft.com/en-us/research/articles/magentic-one-a-generalist-multi-agent-system-for-solving-complex-tasks)
 7. Amazon's [Multi-Agent Orchestrator framework](https://awslabs.github.io/multi-agent-orchestrator/)
-8. Deeplearning.ai's [agent related courses](https://www.deeplearning.ai/courses/?courses_date_desc%5Bquery%5D=agents) (Meta, AWS, Microsoft, LangChain, LlamaIndex, crewAI, AutoGen) and some [lessons ported to using Llama](https://github.com/meta-llama/llama-recipes/tree/main/recipes/quickstart/agents/DeepLearningai_Course_Notebooks). 
+8. Deeplearning.ai's [agent related courses](https://www.deeplearning.ai/courses/?courses_date_desc%5Bquery%5D=agents) (Meta, AWS, Microsoft, LangChain, LlamaIndex, crewAI, AutoGen, Letta) and some [lessons ported to using Llama](https://github.com/meta-llama/llama-recipes/tree/main/recipes/quickstart/agents/DeepLearningai_Course_Notebooks). 
 9. Felicis's [The Agentic Web](https://www.felicis.com/insight/the-agentic-web)
 10. A pretty complete [list of AI agents](https://github.com/e2b-dev/awesome-ai-agents), not including [/dev/agents](https://sdsa.ai/), a very new startup building the next-gen OS for AI agents, though.

recipes/use_cases/gmail_agent/gmagent.py

@@ -491,11 +491,11 @@ user_email = None
 service = None
 user_id = 'me'
 
-def set_email_service(email):
+def set_email_service(gmail):
     global user_email
     global service
 
-    user_email = email
+    user_email = gmail
     service = authenticate_gmail(user_email)
 
 class Agent:

recipes/use_cases/gmail_agent/main.py

@@ -6,10 +6,12 @@ from functions_prompt import system_prompt
 
 def main():
     parser = argparse.ArgumentParser(description="Set email address")
-    parser.add_argument("--user_email", type=str, required=True, help="Your Gmail address")
+    parser.add_argument("--gmail", type=str, required=True, help="Your Gmail address")
     args = parser.parse_args()
 
-    gmagent.set_email_service(args.user_email)
+    print(f"{args.gmail=}")
+
+    gmagent.set_email_service(args.gmail)
 
     greeting = llama31("hello", "Your name is Gmagent, an assistant that can perform all Gmail related tasks for your user.")
     agent_response = f"{greeting}\n\nYour ask: "