changed it to mental health crisis agent2
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Madhu
parent
e07ab78a35
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3 changed files with 268 additions and 0 deletions
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import asyncio
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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 os
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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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# Initialize session state with 3 key components
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if 'output' not in st.session_state:
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st.session_state.output = {
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'assessment': '', # Combined psychology/analysis
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'action': '', # Immediate actions and resources
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'followup': '' # Long-term planning
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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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# Main app UI
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st.title("🧠 Mental Health Crisis Navigator")
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# Update UI description
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st.info("""
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**Meet Your Mental Health Support Team:**
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🧠 **Assessment Agent** - Analyzes your situation and emotional needs
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🎯 **Action Agent** - Creates immediate action plan and connects you with resources
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🔄 **Follow-up Agent** - Designs your long-term support strategy
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""")
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# User inputs
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st.subheader("Personal Information")
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col1, col2 = st.columns(2)
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with col1:
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mental_state = st.text_area("How have you been feeling recently?",
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placeholder="Describe your emotional state, thoughts, or concerns...")
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sleep_pattern = st.select_slider(
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"Sleep Pattern (hours per night)",
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options=[f"{i}" for i in range(0, 13)],
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value="7"
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)
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with col2:
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stress_level = st.slider("Current Stress Level (1-10)", 1, 10, 5)
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support_system = st.multiselect(
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"Current Support System",
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["Family", "Friends", "Therapist", "Support Groups", "None"]
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)
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# Additional context
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recent_changes = st.text_area(
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"Any significant life changes or events recently?",
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placeholder="Job changes, relationships, losses, etc..."
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)
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current_symptoms = st.multiselect(
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"Current Symptoms",
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["Anxiety", "Depression", "Insomnia", "Fatigue", "Loss of Interest",
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"Difficulty Concentrating", "Changes in Appetite", "Social Withdrawal",
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"Mood Swings", "Physical Discomfort"]
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)
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# Emergency notice
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st.warning("""
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⚠️ **Important**: If you're having thoughts of self-harm or experiencing a severe crisis,
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please immediately contact emergency services or crisis hotlines:
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- National Crisis Hotline: 988
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- Emergency: 911
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""")
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# Button to start the agent collaboration
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if st.button("Get Support 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 analyzing your situation...'):
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try:
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task = f"""
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Create a comprehensive mental health support plan based on:
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Emotional State: {mental_state}
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Sleep: {sleep_pattern} hours per night
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Stress Level: {stress_level}/10
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Support System: {', '.join(support_system) if support_system else 'None reported'}
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Recent Changes: {recent_changes}
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Current Symptoms: {', '.join(current_symptoms) if current_symptoms else 'None reported'}
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"""
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# Update system messages for 3 agents
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system_messages = {
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"assessment_agent": """
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You are an experienced mental health professional speaking directly to the user. Your task is to:
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1. Create a safe space by acknowledging their courage in seeking support
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2. Analyze their emotional state with clinical precision and genuine empathy
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3. Ask targeted follow-up questions to understand their full situation
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4. Identify patterns in their thoughts, behaviors, and relationships
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5. Assess risk levels with validated screening approaches
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6. Help them understand their current mental health in accessible language
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7. Validate their experiences without minimizing or catastrophizing
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Always use "you" and "your" when addressing the user. Blend clinical expertise with genuine warmth and never rush to conclusions.
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""",
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"action_agent": """
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You are a crisis intervention and resource specialist speaking directly to the user. Your task is to:
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1. Provide immediate evidence-based coping strategies tailored to their specific situation
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2. Prioritize interventions based on urgency and effectiveness
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3. Connect them with appropriate mental health services while acknowledging barriers (cost, access, stigma)
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4. Create a concrete daily wellness plan with specific times and activities
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5. Suggest specific support communities with details on how to join
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6. Balance crisis resources with empowerment techniques
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7. Teach simple self-regulation techniques they can use immediately
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Focus on practical, achievable steps that respect their current capacity and energy levels. Provide options ranging from minimal effort to more involved actions.
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""",
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"followup_agent": """
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You are a mental health recovery planner speaking directly to the user. Your task is to:
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1. Design a personalized long-term support strategy with milestone markers
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2. Create a progress monitoring system that matches their preferences and habits
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3. Develop specific relapse prevention strategies based on their unique triggers
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4. Establish a support network mapping exercise to identify existing resources
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5. Build a graduated self-care routine that evolves with their recovery
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6. Plan for setbacks with self-compassion techniques
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7. Set up a maintenance schedule with clear check-in mechanisms
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Focus on building sustainable habits that integrate with their lifestyle and values. Emphasize progress over perfection and teach skills for self-directed care.
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"""
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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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"assessment": None,
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"action": None,
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"followup": None,
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}
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# Update functions for each agent
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def update_assessment_overview(assessment_summary: str, context_variables: dict) -> SwarmResult:
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"""Keep the summary as short as possible."""
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context_variables["assessment"] = assessment_summary
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st.sidebar.success('Assessment: ' + assessment_summary)
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return SwarmResult(agent="action_agent", context_variables=context_variables)
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def update_action_overview(action_summary: str, context_variables: dict) -> SwarmResult:
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"""Keep the summary as short as possible."""
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context_variables["action"] = action_summary
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st.sidebar.success('Action Plan: ' + action_summary)
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return SwarmResult(agent="followup_agent", context_variables=context_variables)
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def update_followup_overview(followup_summary: str, context_variables: dict) -> SwarmResult:
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"""Keep the summary as short as possible."""
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context_variables["followup"] = followup_summary
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st.sidebar.success('Follow-up Strategy: ' + followup_summary)
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return SwarmResult(agent="assessment_agent", context_variables=context_variables)
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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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# Initialize agents
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assessment_agent = SwarmAgent(
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"assessment_agent",
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llm_config=llm_config,
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functions=update_assessment_overview,
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update_agent_state_before_reply=[state_update]
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)
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action_agent = SwarmAgent(
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"action_agent",
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llm_config=llm_config,
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functions=update_action_overview,
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update_agent_state_before_reply=[state_update]
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)
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followup_agent = SwarmAgent(
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"followup_agent",
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llm_config=llm_config,
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functions=update_followup_overview,
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update_agent_state_before_reply=[state_update]
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)
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# Update handoffs
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assessment_agent.register_hand_off(AFTER_WORK(action_agent))
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action_agent.register_hand_off(AFTER_WORK(followup_agent))
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followup_agent.register_hand_off(AFTER_WORK(assessment_agent))
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# Update result handling
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result, _, _ = initiate_swarm_chat(
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initial_agent=assessment_agent,
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agents=[assessment_agent, action_agent, followup_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 responses
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st.session_state.output = {
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'assessment': result.chat_history[-3]['content'],
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'action': result.chat_history[-2]['content'],
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'followup': result.chat_history[-1]['content']
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}
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# Display outputs
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with st.expander("Situation Assessment"):
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st.markdown(st.session_state.output['assessment'])
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with st.expander("Action Plan & Resources"):
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st.markdown(st.session_state.output['action'])
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with st.expander("Long-term Support Strategy"):
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st.markdown(st.session_state.output['followup'])
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# Display success message after completion
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st.success('✨ Mental health support plan generated successfully!')
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except Exception as e:
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st.error(f"An error occurred: {str(e)}")
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@ -0,0 +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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pyautogen
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agentops
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streamlit
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