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changed it to mental health crisis agent

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Madhu 2025-02-20 06:25:09 +05:30
parent 1062536f4a
commit e07ab78a35
1 changed files with 165 additions and 116 deletions
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281
ai_agent_tutorials/ai_magenticag2_agent/main.py
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@ -18,10 +18,10 @@ os.environ["AUTOGEN_USE_DOCKER"] = "0"
# Initialize session state # Initialize session state
if 'output' not in st.session_state: if 'output' not in st.session_state:
st.session_state.output = { st.session_state.output = {
'climate': '', 'psychology': '',
'urban': '', 'resources': '',
'economic': '', 'action': '',
'community': '' 'followup': ''
} }
# Sidebar for API key input # Sidebar for API key input
@ -32,28 +32,63 @@ st.sidebar.title("AgentOps API Key")
agentops_key = st.sidebar.text_input("Enter your AgentOps API Key", type="password", value="4e725ba8-b57e-49b5-809a-4eeef18d92ed") agentops_key = st.sidebar.text_input("Enter your AgentOps API Key", type="password", value="4e725ba8-b57e-49b5-809a-4eeef18d92ed")
# Main app UI # Main app UI
st.title("🌍 Climate Impact Response Planner") st.title("🧠 Mental Health Crisis Navigator")
# Add agent information below title # Add agent information below title
st.info(""" st.info("""
**Meet Your Climate Planning Team:** **Meet Your Mental Health Support Team:**
🌡️ **Climate Analysis Agent** - Analyzes climate data and risk projections 🧠 **Psychology Agent** - Analyzes emotional state and psychological needs
🏙️ **Urban Planning Agent** - Develops infrastructure and zoning strategies 📋 **Resource Agent** - Identifies relevant support services and professionals
💰 **Economic Impact Agent** - Assesses financial implications 🎯 **Action Agent** - Creates immediate step-by-step action plans
👥 **Community Engagement Agent** - Plans public involvement and behavior change 🔄 **Follow-up Agent** - Designs ongoing support and prevention strategies
""") """)
# User input # User inputs
st.subheader("City Information") st.subheader("Personal Information")
city_name = st.text_input("Enter City Name", "") col1, col2 = st.columns(2)
city_description = st.text_area("Brief description of the city (population, geography, main industries, etc.)", "")
with col1:
mental_state = st.text_area("How have you been feeling recently?",
placeholder="Describe your emotional state, thoughts, or concerns...")
sleep_pattern = st.select_slider(
"Sleep Pattern (hours per night)",
options=[f"{i}" for i in range(0, 13)],
value="7"
)
with col2:
stress_level = st.slider("Current Stress Level (1-10)", 1, 10, 5)
support_system = st.multiselect(
"Current Support System",
["Family", "Friends", "Therapist", "Support Groups", "None"]
)
# Additional context
recent_changes = st.text_area(
"Any significant life changes or events recently?",
placeholder="Job changes, relationships, losses, etc..."
)
current_symptoms = st.multiselect(
"Current Symptoms",
["Anxiety", "Depression", "Insomnia", "Fatigue", "Loss of Interest",
"Difficulty Concentrating", "Changes in Appetite", "Social Withdrawal",
"Mood Swings", "Physical Discomfort"]
)
# Emergency notice
st.warning("""
⚠️ **Important**: If you're having thoughts of self-harm or experiencing a severe crisis,
please immediately contact emergency services or crisis hotlines:
- National Crisis Hotline: 988
- Emergency: 911
""")
@contextmanager @contextmanager
def agentops_session(api_key: str, tags: list): def agentops_session(api_key: str, tags: list):
"""Context manager for AgentOps sessions""" """Context manager for AgentOps sessions"""
try: try:
# Initialize new session
agentops.init( agentops.init(
api_key=api_key, api_key=api_key,
tags=tags, tags=tags,
@ -62,28 +97,84 @@ def agentops_session(api_key: str, tags: list):
) )
yield yield
finally: finally:
# Always ensure session is ended
try: try:
agentops.end_session("Success") agentops.end_session("Success")
except Exception as e: except Exception as e:
print(f"Failed to end AgentOps session: {e}") print(f"Failed to end AgentOps session: {e}")
# Button to start the agent collaboration # Button to start the agent collaboration
if st.button("Generate Climate Response Plan"): if st.button("Get Support Plan"):
if not api_key: if not api_key:
st.error("Please enter your OpenAI API key.") st.error("Please enter your OpenAI API key.")
else: else:
with st.spinner('🤖 AI Agents are collaborating on your climate response plan...'): with st.spinner('🤖 AI Agents are analyzing your situation...'):
with agentops_session(api_key=agentops_key, tags=["aqi_agent"]): with agentops_session(api_key=agentops_key, tags=["mental_health_navigator"]):
try: try:
task = f""" task = f"""
Create a comprehensive climate impact response plan for: Create a comprehensive mental health support plan based on:
City: {city_name}
Description: {city_description}
Consider all aspects of climate adaptation including environmental, infrastructural, economic, and social factors. Emotional State: {mental_state}
Sleep: {sleep_pattern} hours per night
Stress Level: {stress_level}/10
Support System: {', '.join(support_system) if support_system else 'None reported'}
Recent Changes: {recent_changes}
Current Symptoms: {', '.join(current_symptoms) if current_symptoms else 'None reported'}
""" """
system_messages = {
"psychology_agent": """
You are an experienced mental health professional speaking directly to the user. Your task is to:
1. Analyze their emotional state and psychological symptoms with empathy
2. Help them understand potential mental health concerns
3. Assess their risk levels and urgency
4. Suggest therapeutic approaches that would work for them
5. Help them understand how their life changes and stressors are affecting them
6. Provide supportive psychological insights
Always use "you" and "your" when addressing the user. Maintain a warm, supportive, and non-judgmental tone.
Example: "Based on what you've shared about your sleep patterns..." instead of "The individual's sleep patterns..."
""",
"resource_agent": """
You are a mental health resource coordinator speaking directly to the user. Your task is to:
1. Connect you with appropriate mental health services
2. Suggest support groups or communities that would benefit you
3. Recommend professional care options for your situation
4. Provide crisis resources when needed
5. Consider what resources would be most accessible for you
6. Share specific contact information for your local resources
Always address the user directly using "you" and "your". Focus on practical, accessible resources.
Example: "Given your current situation, these resources might help..." instead of "The following resources are recommended..."
""",
"action_agent": """
You are a crisis intervention specialist speaking directly to the user. Your task is to:
1. Help you develop immediate coping strategies
2. Work with you to create a daily wellness routine
3. Teach you stress management techniques
4. Guide you in improving your sleep habits
5. Help you make healthy lifestyle adjustments
6. Create an emergency response plan with you if needed
Use "you" and "your" when providing guidance. Give clear, actionable steps.
Example: "Here are steps you can take right now..." instead of "The following steps are recommended..."
""",
"followup_agent": """
You are a mental health recovery planner speaking directly to the user. Your task is to:
1. Help you develop long-term support strategies
2. Create a progress monitoring plan that works for you
3. Work with you on relapse prevention strategies
4. Plan how to engage your support system
5. Guide you through lifestyle modifications
6. Set up maintenance and check-in schedules with you
Always use "you" and "your" in your recommendations. Focus on sustainable, long-term solutions.
Example: "To maintain your progress, you might want to..." instead of "The following maintenance plan is suggested..."
"""
}
# Then modify the agent configurations # Then modify the agent configurations
llm_config = { llm_config = {
"config_list": [{"model": "gpt-4o", "api_key": api_key}] "config_list": [{"model": "gpt-4o", "api_key": api_key}]
@ -91,78 +182,36 @@ if st.button("Generate Climate Response Plan"):
# Context management for agent communication # Context management for agent communication
context_variables = { context_variables = {
"climate": None, "psychology": None,
"urban": None, "resources": None,
"economic": None, "action": None,
"community": None, "followup": None,
} }
# Update functions for each agent # Update functions for each agent
def update_climate_overview(climate_summary: str, context_variables: dict) -> SwarmResult: def update_psychology_overview(psychology_summary: str, context_variables: dict) -> SwarmResult:
"""Keep the summary as short as possible.""" """Keep the summary as short as possible."""
context_variables["climate"] = climate_summary context_variables["psychology"] = psychology_summary
st.sidebar.success('Climate Analysis: ' + climate_summary) st.sidebar.success('Psychology Analysis: ' + psychology_summary)
return SwarmResult(agent="urban_agent", context_variables=context_variables) return SwarmResult(agent="resource_agent", context_variables=context_variables)
def update_urban_overview(urban_summary: str, context_variables: dict) -> SwarmResult: def update_resource_overview(resource_summary: str, context_variables: dict) -> SwarmResult:
"""Keep the summary as short as possible.""" """Keep the summary as short as possible."""
context_variables["urban"] = urban_summary context_variables["resources"] = resource_summary
st.sidebar.success('Urban Planning: ' + urban_summary) st.sidebar.success('Resource Identification: ' + resource_summary)
return SwarmResult(agent="economic_agent", context_variables=context_variables) return SwarmResult(agent="action_agent", context_variables=context_variables)
def update_economic_overview(economic_summary: str, context_variables: dict) -> SwarmResult: def update_action_overview(action_summary: str, context_variables: dict) -> SwarmResult:
"""Keep the summary as short as possible.""" """Keep the summary as short as possible."""
context_variables["economic"] = economic_summary context_variables["action"] = action_summary
st.sidebar.success('Economic Impact: ' + economic_summary) st.sidebar.success('Action Plan: ' + action_summary)
return SwarmResult(agent="community_agent", context_variables=context_variables) return SwarmResult(agent="followup_agent", context_variables=context_variables)
def update_community_overview(community_summary: str, context_variables: dict) -> SwarmResult: def update_followup_overview(followup_summary: str, context_variables: dict) -> SwarmResult:
"""Keep the summary as short as possible.""" """Keep the summary as short as possible."""
context_variables["community"] = community_summary context_variables["followup"] = followup_summary
st.sidebar.success('Community Engagement: ' + community_summary) st.sidebar.success('Follow-up Strategy: ' + followup_summary)
return SwarmResult(agent="climate_agent", context_variables=context_variables) return SwarmResult(agent="psychology_agent", context_variables=context_variables)
system_messages = {
"climate_agent": """
You are an expert climate scientist and risk analyst. Your task is to:
1. Analyze historical climate data and future projections for the specified city
2. Identify key climate risks (flooding, heat waves, storms, etc.)
3. Assess vulnerability of different city areas and systems
4. Prioritize climate threats based on likelihood and impact
5. Recommend key areas for climate resilience focus
6. Provide specific climate scenarios the city should prepare for
""",
"urban_agent": """
You are an experienced urban planner specializing in climate adaptation. Your task is to:
1. Design infrastructure modifications for climate resilience
2. Develop zoning recommendations for risk reduction
3. Plan green infrastructure and nature-based solutions
4. Identify critical infrastructure vulnerabilities
5. Create phased implementation strategies
6. Consider both immediate and long-term adaptation needs
""",
"economic_agent": """
You are a climate economics and finance specialist. Your task is to:
1. Calculate potential economic impacts of climate risks
2. Identify funding sources for adaptation projects
3. Analyze cost-benefit ratios of proposed solutions
4. Assess impacts on local industries and businesses
5. Develop economic incentives for climate adaptation
6. Create budget allocation recommendations
""",
"community_agent": """
You are a community engagement and behavior change expert. Your task is to:
1. Design public communication strategies
2. Plan community involvement in adaptation efforts
3. Develop education and awareness programs
4. Create behavior change initiatives
5. Plan vulnerable population support systems
6. Design feedback and monitoring systems
"""
}
def update_system_message_func(agent: SwarmAgent, messages) -> str: def update_system_message_func(agent: SwarmAgent, messages) -> str:
"""""" """"""
@ -196,42 +245,42 @@ if st.button("Generate Climate Response Plan"):
state_update = UPDATE_SYSTEM_MESSAGE(update_system_message_func) state_update = UPDATE_SYSTEM_MESSAGE(update_system_message_func)
# Define agents with proper code execution config # Define agents with proper code execution config
climate_agent = SwarmAgent( psychology_agent = SwarmAgent(
"climate_agent", "psychology_agent",
llm_config=llm_config, llm_config=llm_config,
functions=update_climate_overview, functions=update_psychology_overview,
update_agent_state_before_reply=[state_update] update_agent_state_before_reply=[state_update]
) )
urban_agent = SwarmAgent( resource_agent = SwarmAgent(
"urban_agent", "resource_agent",
llm_config=llm_config, llm_config=llm_config,
functions=update_urban_overview, functions=update_resource_overview,
update_agent_state_before_reply=[state_update] update_agent_state_before_reply=[state_update]
) )
economic_agent = SwarmAgent( action_agent = SwarmAgent(
"economic_agent", "action_agent",
llm_config=llm_config, llm_config=llm_config,
functions=update_economic_overview, functions=update_action_overview,
update_agent_state_before_reply=[state_update] update_agent_state_before_reply=[state_update]
) )
community_agent = SwarmAgent( followup_agent = SwarmAgent(
name="community_agent", name="followup_agent",
llm_config=llm_config, llm_config=llm_config,
functions=update_community_overview, functions=update_followup_overview,
update_agent_state_before_reply=[state_update] update_agent_state_before_reply=[state_update]
) )
climate_agent.register_hand_off(AFTER_WORK(urban_agent)) psychology_agent.register_hand_off(AFTER_WORK(resource_agent))
urban_agent.register_hand_off(AFTER_WORK(economic_agent)) resource_agent.register_hand_off(AFTER_WORK(action_agent))
economic_agent.register_hand_off(AFTER_WORK(community_agent)) action_agent.register_hand_off(AFTER_WORK(followup_agent))
community_agent.register_hand_off(AFTER_WORK(climate_agent)) followup_agent.register_hand_off(AFTER_WORK(psychology_agent))
result, _, _ = initiate_swarm_chat( result, _, _ = initiate_swarm_chat(
initial_agent=climate_agent, initial_agent=psychology_agent,
agents=[climate_agent, urban_agent, economic_agent, community_agent], agents=[psychology_agent, resource_agent, action_agent, followup_agent],
user_agent=None, user_agent=None,
messages=task, messages=task,
max_rounds=13, max_rounds=13,
@ -239,27 +288,27 @@ if st.button("Generate Climate Response Plan"):
# Update session state with the individual responses # Update session state with the individual responses
st.session_state.output = { st.session_state.output = {
'climate': result.chat_history[-4]['content'], 'psychology': result.chat_history[-4]['content'],
'urban': result.chat_history[-3]['content'], 'resources': result.chat_history[-3]['content'],
'economic': result.chat_history[-2]['content'], 'action': result.chat_history[-2]['content'],
'community': result.chat_history[-1]['content'] 'followup': result.chat_history[-1]['content']
} }
# Display success message after completion # Display success message after completion
st.success('✨ Climate response plan generated successfully!') st.success('✨ Mental health support plan generated successfully!')
# Display the individual outputs in expanders # Display the individual outputs in expanders
with st.expander("Climate Analysis"): with st.expander("Psychology Analysis"):
st.markdown(st.session_state.output['climate']) st.markdown(st.session_state.output['psychology'])
with st.expander("Urban Planning"): with st.expander("Resource Identification"):
st.markdown(st.session_state.output['urban']) st.markdown(st.session_state.output['resources'])
with st.expander("Economic Impact"): with st.expander("Action Plan"):
st.markdown(st.session_state.output['economic']) st.markdown(st.session_state.output['action'])
with st.expander("Community Engagement"): with st.expander("Follow-up Strategy"):
st.markdown(st.session_state.output['community']) st.markdown(st.session_state.output['followup'])
except Exception as e: except Exception as e:
st.error(f"An error occurred: {str(e)}") st.error(f"An error occurred: {str(e)}")