climate aqi script with swarms ag2
This commit is contained in:
Madhu
parent
431bb6fc48
commit
1062536f4a
3 changed files with 267 additions and 12 deletions
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@ -1,16 +1,266 @@
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import asyncio
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from autogen_ext.models.openai import OpenAIChatCompletionClient
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from autogen_ext.teams.magentic_one import MagenticOne
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from autogen_agentchat.ui import Console
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import streamlit as st
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from autogen import (
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SwarmAgent,
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SwarmResult,
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initiate_swarm_chat,
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OpenAIWrapper,
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AFTER_WORK,
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UPDATE_SYSTEM_MESSAGE
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)
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import agentops
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import os
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from contextlib import contextmanager
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# Add this at the top of the file, before any other code
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os.environ["AUTOGEN_USE_DOCKER"] = "0"
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async def example_usage():
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client = OpenAIChatCompletionClient(model="gpt-4o", api_key="")
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m1 = MagenticOne(client=client)
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task = "Write a Python script to fetch data from an API."
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result = await Console(m1.run_stream(task=task))
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print(result)
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# Initialize session state
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if 'output' not in st.session_state:
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st.session_state.output = {
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'climate': '',
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'urban': '',
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'economic': '',
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'community': ''
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}
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# Sidebar for API key input
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st.sidebar.title("OpenAI API Key")
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api_key = st.sidebar.text_input("Enter your OpenAI API Key", type="password")
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if __name__ == "__main__":
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asyncio.run(example_usage())
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st.sidebar.title("AgentOps API Key")
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agentops_key = st.sidebar.text_input("Enter your AgentOps API Key", type="password", value="4e725ba8-b57e-49b5-809a-4eeef18d92ed")
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# Main app UI
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st.title("🌍 Climate Impact Response Planner")
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# Add agent information below title
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st.info("""
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**Meet Your Climate Planning Team:**
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🌡️ **Climate Analysis Agent** - Analyzes climate data and risk projections
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🏙️ **Urban Planning Agent** - Develops infrastructure and zoning strategies
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💰 **Economic Impact Agent** - Assesses financial implications
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👥 **Community Engagement Agent** - Plans public involvement and behavior change
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""")
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# User input
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st.subheader("City Information")
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city_name = st.text_input("Enter City Name", "")
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city_description = st.text_area("Brief description of the city (population, geography, main industries, etc.)", "")
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@contextmanager
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def agentops_session(api_key: str, tags: list):
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"""Context manager for AgentOps sessions"""
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try:
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# Initialize new session
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agentops.init(
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api_key=api_key,
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tags=tags,
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instrument_llm_calls=True,
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auto_start_session=True
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)
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yield
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finally:
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# Always ensure session is ended
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try:
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agentops.end_session("Success")
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except Exception as e:
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print(f"Failed to end AgentOps session: {e}")
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# Button to start the agent collaboration
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if st.button("Generate Climate Response Plan"):
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if not api_key:
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st.error("Please enter your OpenAI API key.")
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else:
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with st.spinner('🤖 AI Agents are collaborating on your climate response plan...'):
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with agentops_session(api_key=agentops_key, tags=["aqi_agent"]):
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try:
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task = f"""
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Create a comprehensive climate impact response plan for:
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City: {city_name}
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Description: {city_description}
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Consider all aspects of climate adaptation including environmental, infrastructural, economic, and social factors.
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"""
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# Then modify the agent configurations
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llm_config = {
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"config_list": [{"model": "gpt-4o", "api_key": api_key}]
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}
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# Context management for agent communication
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context_variables = {
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"climate": None,
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"urban": None,
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"economic": None,
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"community": None,
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}
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# Update functions for each agent
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def update_climate_overview(climate_summary: str, context_variables: dict) -> SwarmResult:
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"""Keep the summary as short as possible."""
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context_variables["climate"] = climate_summary
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st.sidebar.success('Climate Analysis: ' + climate_summary)
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return SwarmResult(agent="urban_agent", context_variables=context_variables)
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def update_urban_overview(urban_summary: str, context_variables: dict) -> SwarmResult:
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"""Keep the summary as short as possible."""
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context_variables["urban"] = urban_summary
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st.sidebar.success('Urban Planning: ' + urban_summary)
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return SwarmResult(agent="economic_agent", context_variables=context_variables)
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def update_economic_overview(economic_summary: str, context_variables: dict) -> SwarmResult:
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"""Keep the summary as short as possible."""
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context_variables["economic"] = economic_summary
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st.sidebar.success('Economic Impact: ' + economic_summary)
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return SwarmResult(agent="community_agent", context_variables=context_variables)
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def update_community_overview(community_summary: str, context_variables: dict) -> SwarmResult:
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"""Keep the summary as short as possible."""
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context_variables["community"] = community_summary
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st.sidebar.success('Community Engagement: ' + community_summary)
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return SwarmResult(agent="climate_agent", context_variables=context_variables)
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system_messages = {
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"climate_agent": """
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You are an expert climate scientist and risk analyst. Your task is to:
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1. Analyze historical climate data and future projections for the specified city
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2. Identify key climate risks (flooding, heat waves, storms, etc.)
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3. Assess vulnerability of different city areas and systems
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4. Prioritize climate threats based on likelihood and impact
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5. Recommend key areas for climate resilience focus
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6. Provide specific climate scenarios the city should prepare for
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""",
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"urban_agent": """
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You are an experienced urban planner specializing in climate adaptation. Your task is to:
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1. Design infrastructure modifications for climate resilience
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2. Develop zoning recommendations for risk reduction
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3. Plan green infrastructure and nature-based solutions
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4. Identify critical infrastructure vulnerabilities
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5. Create phased implementation strategies
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6. Consider both immediate and long-term adaptation needs
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""",
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"economic_agent": """
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You are a climate economics and finance specialist. Your task is to:
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1. Calculate potential economic impacts of climate risks
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2. Identify funding sources for adaptation projects
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3. Analyze cost-benefit ratios of proposed solutions
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4. Assess impacts on local industries and businesses
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5. Develop economic incentives for climate adaptation
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6. Create budget allocation recommendations
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""",
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"community_agent": """
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You are a community engagement and behavior change expert. Your task is to:
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1. Design public communication strategies
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2. Plan community involvement in adaptation efforts
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3. Develop education and awareness programs
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4. Create behavior change initiatives
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5. Plan vulnerable population support systems
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6. Design feedback and monitoring systems
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"""
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}
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def update_system_message_func(agent: SwarmAgent, messages) -> str:
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""""""
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system_prompt = system_messages[agent.name]
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current_gen = agent.name.split("_")[0]
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if agent._context_variables.get(current_gen) is None:
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system_prompt += f"Call the update function provided to first provide a 2-3 sentence summary of your ideas on {current_gen.upper()} based on the context provided."
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agent.llm_config['tool_choice'] = {"type": "function", "function": {"name": f"update_{current_gen}_overview"}}
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agent.client = OpenAIWrapper(**agent.llm_config)
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else:
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# remove the tools to avoid the agent from using it and reduce cost
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agent.llm_config["tools"] = None
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agent.llm_config['tool_choice'] = None
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agent.client = OpenAIWrapper(**agent.llm_config)
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# the agent has given a summary, now it should generate a detailed response
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system_prompt += f"\n\nYour task\nYou task is write the {current_gen} part of the report. Do not include any other parts. Do not use XML tags.\nStart your reponse with: '## {current_gen.capitalize()} Design'."
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# Remove all messages except the first one with less cost
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k = list(agent._oai_messages.keys())[-1]
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agent._oai_messages[k] = agent._oai_messages[k][:1]
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system_prompt += f"\n\n\nBelow are some context for you to refer to:"
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# Add context variables to the prompt
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for k, v in agent._context_variables.items():
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if v is not None:
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system_prompt += f"\n{k.capitalize()} Summary:\n{v}"
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return system_prompt
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state_update = UPDATE_SYSTEM_MESSAGE(update_system_message_func)
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# Define agents with proper code execution config
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climate_agent = SwarmAgent(
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"climate_agent",
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llm_config=llm_config,
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functions=update_climate_overview,
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update_agent_state_before_reply=[state_update]
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)
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urban_agent = SwarmAgent(
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"urban_agent",
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llm_config=llm_config,
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functions=update_urban_overview,
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update_agent_state_before_reply=[state_update]
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)
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economic_agent = SwarmAgent(
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"economic_agent",
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llm_config=llm_config,
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functions=update_economic_overview,
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update_agent_state_before_reply=[state_update]
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)
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community_agent = SwarmAgent(
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name="community_agent",
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llm_config=llm_config,
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functions=update_community_overview,
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update_agent_state_before_reply=[state_update]
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)
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climate_agent.register_hand_off(AFTER_WORK(urban_agent))
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urban_agent.register_hand_off(AFTER_WORK(economic_agent))
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economic_agent.register_hand_off(AFTER_WORK(community_agent))
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community_agent.register_hand_off(AFTER_WORK(climate_agent))
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result, _, _ = initiate_swarm_chat(
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initial_agent=climate_agent,
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agents=[climate_agent, urban_agent, economic_agent, community_agent],
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user_agent=None,
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messages=task,
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max_rounds=13,
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)
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# Update session state with the individual responses
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st.session_state.output = {
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'climate': result.chat_history[-4]['content'],
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'urban': result.chat_history[-3]['content'],
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'economic': result.chat_history[-2]['content'],
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'community': result.chat_history[-1]['content']
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}
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# Display success message after completion
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st.success('✨ Climate response plan generated successfully!')
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# Display the individual outputs in expanders
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with st.expander("Climate Analysis"):
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st.markdown(st.session_state.output['climate'])
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with st.expander("Urban Planning"):
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st.markdown(st.session_state.output['urban'])
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with st.expander("Economic Impact"):
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st.markdown(st.session_state.output['economic'])
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with st.expander("Community Engagement"):
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st.markdown(st.session_state.output['community'])
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except Exception as e:
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st.error(f"An error occurred: {str(e)}")
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raise # Re-raise to trigger session end with error
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@ -1,3 +1,8 @@
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autogen-agentchat
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autogen-ext
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playwright install --with-deps chromium
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playwright install --with-deps chromium
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pyautogen
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agentops
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streamlit
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