The First Autonomous Agentic Employment Framework

agentic_employment.py

import gradio as gr
import pandas as pd
import time
import random
import plotly.express as px
from gradio_modal import Modal

def create_new_agent_wizard():
    with Modal(visible=False) as create_agent_modal:
        
        # Step 1: Basic Information
        gr.Markdown("### Step 1: Basic Information")
        agent_name = gr.Textbox(label="Agent Name", placeholder="Enter the name of the agent...")
        agent_type = gr.Dropdown(["Conversational", "Analytical", "Generative", "Retrieval-based"], label="Agent Type")

        # Step 2: Configuration
        gr.Markdown("### Step 2: Configuration")
        agent_description = gr.Textbox(label="Description", placeholder="Describe the agent's primary functions...")
        llm_choice = gr.Dropdown(["GPT-3.5", "GPT-4", "Claude", "Anthropic-100k", "Chinchilla", "PaLM"], label="Choose LLM Base")
        
        
        # Agent Description and Avatar
        gr.Markdown("#### Agent Description and Avatar")
        agent_description_input = gr.Textbox(label="Agent Description", placeholder="Enter agent description...")
        agent_avatar_input = gr.Image(type="filepath", label="Agent Avatar")
        
        # Agent LLM and Prompt
        gr.Markdown("#### Agent LLM and Prompt")
        agent_llm_dropdown = gr.Dropdown(["GPT-3.5", "GPT-4", "Claude", "Anthropic-100k", "Chinchilla", "PaLM", "Jurassic-2", "Bedrock", "Cohere", "Llama", "Alpaca", "Vicuna", "Falcon", "MPT", "Dolly", "StableLM", "RedPajama-INCITE", "OpenAssistant"], value="GPT-3.5", label="Agent LLM")
        agent_prompt_input = gr.Textbox(label="Agent Prompt", placeholder="Enter agent prompt...")
        
        # Agent Knowledge Base
        gr.Markdown("#### Agent Knowledge Base")
        agent_knowledge_upload = gr.File(label="Upload Agent Knowledge Base")
        agent_knowledge_url = gr.Textbox(label="Agent Knowledge URL", placeholder="Enter URL to agent knowledge base...")
        
        # Learning and Exploration Settings
        gr.Markdown("#### Learning and Exploration Settings")
        agent_learning_rate_slider = gr.Slider(0.0, 1.0, value=0.01, label="Learning Rate")
        agent_exploration_slider = gr.Slider(0.0, 1.0, value=0.1, label="Exploration Rate")
        
        # Training Settings
        gr.Markdown("#### Training Settings")
        agent_iterations_slider = gr.Slider(1, 100, value=10, step=1, label="Training Iterations")
        agent_batch_size_slider = gr.Slider(1, 256, value=32, step=1, label="Batch Size")
        
        # Output Settings
        gr.Markdown("#### Output Settings")
        agent_max_tokens_slider = gr.Slider(1, 4096, value=512, step=1, label="Max Tokens")
        agent_temperature_slider = gr.Slider(0.0, 1.0, value=0.7, label="Temperature")
        agent_top_p_slider = gr.Slider(0.0, 1.0, value=1.0, label="Top P")
        agent_frequency_penalty_slider = gr.Slider(0.0, 2.0, value=0.0, label="Frequency Penalty")
        agent_presence_penalty_slider = gr.Slider(0.0, 2.0, value=0.0, label="Presence Penalty")
        agent_stop_sequences_input = gr.Textbox(label="Stop Sequences", placeholder="Enter stop sequences (comma-separated)...")
        
        # Agent Control Buttons
        # gr.Markdown("#### Agent Control Buttons")
        # start_agent_button = gr.Button("Start Agent")
        # stop_agent_button = gr.Button("Stop Agent")
        
        # Agent Status and Performance
        gr.Markdown("#### Agent Status and Performance")
        agent_status_indicator = gr.Textbox(label="Agent Status", interactive=False)
        agent_performance_chart = gr.LinePlot(x="Iteration", y="Performance", label="Agent Performance")
    
        # Step 3: Advanced Settings
        gr.Markdown("### Step 3: Advanced Settings")
        training_data = gr.File(label="Upload Training Data")
        parameters = gr.Textbox(label="Model Parameters")
        
        # Step 4: Review and Create
        gr.Markdown("### Step 4: Review and Create")
        create_button = gr.Button("Create Agent")
        
        # Define the logic for creating an agent
        def create_agent(agent_name, agent_type, agent_description, llm_choice, training_data, parameters):
            return f"Agent '{agent_name}' of type '{agent_type}' created successfully with model '{llm_choice}'."
        
        create_button.click(
            create_agent,
            inputs=[agent_name, agent_type, agent_description, llm_choice, training_data, parameters],
            outputs=[gr.Markdown()]  # Output could be a status message or redirection to the agent list
        )
        
    return create_agent_modal

def settings_conversation(user_message, chat_history):
    bot_response = random.choice([
        "How can I assist you with settings?",
        "What would you like to change?",
        "I'm here to help with any settings issues!"
    ])
    chat_history.append((user_message, bot_response))
    return "", chat_history  # Clear the message input after submission
    
def agent_management():
    with gr.Blocks() as agent_dashboard:
        with gr.Row():
            create_agent_button = gr.Button("Create New Agent", elem_id="create_agent_button")
            view_agent_list_button = gr.Button("View Agent List", elem_id="view_agent_list_button")
            settings_button = gr.Button("Chat", elem_id="settings_button")

        create_agent_modal = create_new_agent_wizard()  # Instantiate the create agent wizard modal

        # Define the agent list
        agent_list = [
            [1, "Sales AI", "Conversational", "Active", 4.5],
            [2, "Support Bot", "Retrieval-based", "Active", 4.2],
            [3, "Marketing Assistant", "Generative", "Inactive", 3.8],
            [4, "Data Analyst", "Analytical", "Active", 4.7],
            [5, "HR Coordinator", "Conversational", "Active", 4.1]
        ]

        with Modal(visible=False) as view_agent_list_modal:
            gr.Markdown("### View Agent List")
            agent_table = gr.DataFrame(
                agent_list,
                headers=["ID", "Name", "Type", "Status", "Performance"],
                datatype=["number", "str", "str", "str", "number"],
                interactive=True,
                row_count=(5, "fixed")
            )
            agent_details = gr.HTML()

            def show_agent_details(agent_table_data):
                if agent_table_data is None or len(agent_table_data) == 0:
                    return "No agent selected."
                
                agent_id = agent_table_data[0]  # Assuming selection gives the first row
                agent = next((a for a in agent_list if a[0] == agent_id), None)
                if agent:
                    details = f"<b>Agent ID:</b> {agent[0]}<br>"
                    details += f"<b>Name:</b> {agent[1]}<br>"
                    details += f"<b>Type:</b> {agent[2]}<br>"
                    details += f"<b>Status:</b> {agent[3]}<br>"
                    details += f"<b>Performance:</b> {agent[4]}<br>"
                    # Add conditional task details here as per agent ID
                    return details
                else:
                    return "Agent not found."

        with Modal(visible=False) as settings_modal:
            gr.Markdown("### Settings Conversation")
            chat_history = []
            chatbot = gr.Chatbot(label="Chat with Settings Bot", value=chat_history)
            msg_input = gr.Textbox(label="Type your message here...", placeholder="Type here...")
            msg_input.submit(settings_conversation, inputs=[msg_input, chatbot], outputs=[msg_input, chatbot])

        # Button clicks to show modals
        create_agent_button.click(lambda: Modal(visible=True), None, create_agent_modal)
        view_agent_list_button.click(lambda: Modal(visible=True), None, view_agent_list_modal)
        settings_button.click(lambda: Modal(visible=True), None, settings_modal)

        with gr.Accordion("Agent Dashboard"):
            with gr.Row():
                timeframe = gr.Dropdown(["Last 24 hours", "Last 7 days", "Last 30 days", "Custom Range"], value="Last 7 days", label="Timeframe")
                agent_groups = gr.Dropdown(["All Groups", "Sales", "Support", "Marketing"], value="All Groups", label="Agent Groups")
                llms = gr.Dropdown(["All LLMs", "GPT-3.5", "GPT-4", "Claude", "Anthropic-100k", "Chinchilla", "PaLM", "Jurassic-2", "Bedrock", "Cohere", "Llama", "Alpaca", "Vicuna", "Falcon", "MPT", "Dolly", "StableLM", "RedPajama-INCITE", "OpenAssistant"], value="All LLMs", label="LLMs")
                customizations = gr.Dropdown(["Default", "High Performance", "Cost Optimized"], value="Default", label="Customizations")

            with gr.Row():
                with gr.Column(scale=1):
                    gr.Markdown("## Agent Workforce Overview")
                    total_agents = gr.Number(value=500, label="Total Agents")
                    active_agents = gr.Number(value=375, label="Active Agents") 
                    idle_agents = gr.Number(value=125, label="Idle Agents")
                with gr.Column(scale=1):
                    gr.Markdown("## Agent Performance")
                    total_tasks = gr.Number(value=12500, label="Total Tasks Completed")
                    avg_duration = gr.Number(value=25.6, label="Avg. Task Duration (min)")
                    agent_rating = gr.Number(value=4.7, label="Avg. Agent Rating (out of 5)")

            with gr.Row():
                with gr.Column(scale=1):  
                    gr.Markdown("## Resource Utilization")
                    compute_usage = gr.Number(value=85.2, label="Compute Usage (%)")
                    storage_usage = gr.Number(value=1250, label="Storage Usage (GB)")
                    llm_calls = gr.Number(value=9875, label="Total LLM API Calls")  
                with gr.Column(scale=1):
                    gr.Markdown("## Cost Analysis") 
                    total_cost = gr.Number(value=12345.67, label="Total Cost ($)")
                    cost_per_task = gr.Number(value=0.99, label="Avg. Cost per Task ($)")
                    budget_remaining = gr.Number(value=54321.33, label="Budget Remaining ($)")

        with gr.Accordion("Agent List"):
            agent_list = [
                [1, "Sales AI", "Conversational", "Active", 4.5],
                [2, "Support Bot", "Retrieval-based", "Active", 4.2],
                [3, "Marketing Assistant", "Generative", "Inactive", 3.8],
                [4, "Data Analyst", "Analytical", "Active", 4.7],
                [5, "HR Coordinator", "Conversational", "Active", 4.1]
            ]

            agent_table = gr.DataFrame(
                agent_list,
                headers=["ID", "Name", "Type", "Status", "Performance"],
                datatype=["number", "str", "str", "str", "number"],
                col_count=(5, "fixed"),
                row_count=(5, "fixed"),
            )

            
    return agent_dashboard


def agent_controls():
    with gr.Blocks() as app:
        gr.Markdown("# Agent Command & Control")

        with gr.Tabs():
            with gr.Tab("Agent Management"):
                with gr.Accordion("Overview and Monitoring"):
                    gr.Markdown("### Overview Dashboard")
                    with gr.Row():
                        total_agents = gr.Number(label="Total Agents", value=50)
                        active_agents = gr.Number(label="Active Agents", value=35)
                        idle_agents = gr.Number(label="Idle Agents", value=10)
                        failed_agents = gr.Number(label="Failed Agents", value=5)

                    gr.Markdown("### Live Agent List")
                    agent_data = [
                        ["1", "NLP Agent", "Active", "Text Processing", "High"],
                        ["2", "Vision Agent", "Idle", "Image Classification", "Medium"],
                        ["3", "Data Agent", "Failed", "Data Cleaning", "Low"],
                        ["4", "Chatbot", "Active", "Customer Service", "High"],
                        ["5", "Analysis Agent", "Idle", "Data Analysis", "Medium"]
                    ]
                    agent_table = gr.Dataframe(
                        headers=["Agent ID", "Agent Name/Type", "Status", "Current Task", "Performance Metrics"],
                        value=agent_data,
                        interactive=True
                    )

                with gr.Accordion("Real-time Guidance and Communication"):
                    gr.Markdown("### Communication Channel")
                    command_input = gr.Textbox(label="Send Command to Agents")
                    send_button = gr.Button("Send")
                    chat_history = gr.Chatbot()
                    
                    def send_command(command):
                        # Simulated response from agents
                        responses = [
                            "Command received by Agent 1",
                            "Agent 2 is currently idle and will process later",
                            "Agent 3 failed to receive the command due to an error",
                            "Agent 4 processing the command",
                            "Agent 5 queued the command for processing"
                        ]
                        return command, random.choice(responses)
                    
                    send_button.click(send_command, inputs=command_input, outputs=chat_history)

                with gr.Accordion("Error Handling and Recovery"):
                    gr.Markdown("### Error Log")
                    errors = [
                        ["12:01", "3", "Connection Timeout", "Failed to connect to the database"],
                        ["12:05", "5", "Memory Overflow", "Exceeded memory limits during data processing"]
                    ]
                    error_log = gr.Dataframe(
                        headers=["Time", "Agent ID", "Error Type", "Message"],
                        value=errors,
                        interactive=True
                    )

            with gr.Tab("Advanced IT Infrastructure Integration"):
                with gr.Accordion("DNS Configuration Management"):
                    
                    gr.Markdown("""
                    **Description:**
                    Manage DNS settings directly from the dashboard. Update DNS records, verify domain ownership, and set DNS failover policies.
                    """)
                    domain_name = gr.Textbox(label="Domain Name", placeholder="Enter domain name here...")
                    dns_record_type = gr.Dropdown(label="Record Type", choices=["A", "AAAA", "CNAME", "MX", "TXT"])
                    record_value = gr.Textbox(label="Record Value", placeholder="Enter DNS record value here...")
                    update_button = gr.Button("Update DNS Record")
                    
                    # Placeholder function to simulate DNS record update
                    def update_dns(domain, record_type, value):
                        return f"DNS record for {domain} of type {record_type} updated with value {value}"

                    update_button.click(update_dns, inputs=[domain_name, dns_record_type, record_value], outputs=gr.Textbox(label="Update Status"))

                with gr.Accordion("Throughput Monitoring"):
              
                    gr.Markdown("""
                    **Description:**
                    Real-time monitoring of network throughput to and from agents. Visualize network traffic, identify bottlenecks, and automate adjustments to network configuration to optimize throughput.
                    """)
                    throughput_chart = gr.LinePlot(
                        x=range(10),
                        y=[[random.randint(50, 100) for _ in range(10)] for _ in range(5)],
                        label="Network Throughput",
                        x_title="Time",
                        y_title="Throughput (Mbps)"
                    )
                    refresh_button = gr.Button("Refresh Data")
                    
                    # Placeholder function to simulate data refresh
                    def refresh_throughput_data():
                        new_data = [[random.randint(50, 100) for _ in range(10)] for _ in range(5)]
                        return gr.LinePlot.update(y=new_data)

                    refresh_button.click(refresh_throughput_data, outputs=throughput_chart)

                with gr.Accordion("VPC and VPN Management"):
                   
                    gr.Markdown("""
                    **Description:**
                    Configure Virtual Private Cloud (VPC) settings, such as subnet creation and routing policies. Set up VPNs to secure communications between the central system and agents, especially those deployed in different regions or on-premises environments.
                    """)
                    vpc_id = gr.Textbox(label="VPC ID", placeholder="Enter VPC ID here...")
                    subnet_id = gr.Textbox(label="Subnet ID", placeholder="Enter Subnet ID here...")
                    routing_policy = gr.Textbox(label="Routing Policy", placeholder="Enter routing policy details here...")
                    configure_vpc_button = gr.Button("Configure VPC")
                    
                    vpn_id = gr.Textbox(label="VPN ID", placeholder="Enter VPN ID here...")
                    vpn_configuration = gr.Textbox(label="VPN Configuration", placeholder="Enter VPN configuration details here...")
                    setup_vpn_button = gr.Button("Setup VPN")
                    
                    # Placeholder function to simulate VPC configuration
                    def configure_vpc(vpc, subnet, routing):
                        return f"VPC {vpc} configured with subnet {subnet} and routing policy {routing}"
                    
                    # Placeholder function to simulate VPN setup
                    def setup_vpn(vpn, config):
                        return f"VPN {vpn} setup with configuration {config}"
                    
                    configure_vpc_button.click(configure_vpc, inputs=[vpc_id, subnet_id, routing_policy], outputs=gr.Textbox(label="VPC Configuration Status"))
                    setup_vpn_button.click(setup_vpn, inputs=[vpn_id, vpn_configuration], outputs=gr.Textbox(label="VPN Setup Status"))

                with gr.Accordion("Network Security Configuration"):
                 
                    gr.Markdown("""
                    **Description:**
                    Manage firewall settings, intrusion detection systems, and other network security appliances. Automate responses to network security incidents, such as isolating compromised agents.
                    """)
                    firewall_rule = gr.Textbox(label="Firewall Rule", placeholder="Enter firewall rule here...")
                    ids_policy = gr.Textbox(label="IDS Policy", placeholder="Enter IDS policy details here...")
                    apply_security_button = gr.Button("Apply Security Settings")
                    
                    # Placeholder function to simulate security settings application
                    def apply_security(firewall, ids):
                        return f"Firewall rule applied: {firewall}, IDS policy applied: {ids}"
                    
                    apply_security_button.click(apply_security, inputs=[firewall_rule, ids_policy], outputs=gr.Textbox(label="Security Settings Status"))

            with gr.Tab("Enhanced Security Features"):
              

                with gr.Accordion("Data Encryption and Access Controls"):
                
                    gr.Markdown("""
                    **Description:**
                    Implement end-to-end encryption for data in transit and at rest. Configure role-based access controls to manage who can interact with which agents and what actions they can perform.
                    """)
                    data_encryption_key = gr.Textbox(label="Data Encryption Key", placeholder="Enter encryption key here...")
                    access_control_role = gr.Dropdown(label="Role", choices=["Admin", "User", "Guest"])
                    access_control_agent = gr.Textbox(label="Agent ID", placeholder="Enter agent ID here...")
                    grant_access_button = gr.Button("Grant Access")
                    
                    # Placeholder function to simulate granting access
                    def grant_access(encryption_key, role, agent_id):
                        return f"Access granted to {role} for agent {agent_id} with encryption key {encryption_key}"
                    
                    grant_access_button.click(grant_access, inputs=[data_encryption_key, access_control_role, access_control_agent], outputs=gr.Textbox(label="Access Control Status"))

                with gr.Accordion("Obfuscation Techniques"):
         
                    gr.Markdown("""
                    **Description:**
                    Protect sensitive information processed by agents using data obfuscation techniques. Configure options for pseudonymization or anonymization of data outputs, especially for environments handling personally identifiable information (PII).
                    """)
                    obfuscation_type = gr.Dropdown(label="Obfuscation Type", choices=["Pseudonymization", "Anonymization"])
                    obfuscation_agent = gr.Textbox(label="Agent ID", placeholder="Enter agent ID here...")
                    apply_obfuscation_button = gr.Button("Apply Obfuscation")
                    
                    # Placeholder function to simulate applying obfuscation
                    def apply_obfuscation(obfuscation_type, agent_id):
                        return f"{obfuscation_type} applied to agent {agent_id}"
                    
                    apply_obfuscation_button.click(apply_obfuscation, inputs=[obfuscation_type, obfuscation_agent], outputs=gr.Textbox(label="Obfuscation Status"))

                with gr.Accordion("Audit Trails and Compliance Reporting"):
                
                    gr.Markdown("""
                    **Description:**
                    Maintain comprehensive logs of all actions taken through the dashboard, including changes to configurations and commands sent to agents. Generate compliance reports detailing security measures in place and actions taken in response to audit requests.
                    """)
                    generate_audit_report_button = gr.Button("Generate Audit Report")
                    
                    # Placeholder function to simulate audit report generation
                    def generate_audit_report():
                        return "Audit report generated"
                    
                    generate_audit_report_button.click(generate_audit_report, outputs=gr.Textbox(label="Audit Report Status"))


            with gr.Tab("Scalability and Load Balancing Enhancements"):
                gr.Markdown("### Scalability and Load Balancing Enhancements")

                with gr.Accordion("Dynamic Resource Allocation"):
                  
                    gr.Markdown("""
                    **Description:**
                    Develop algorithms to dynamically allocate resources to agents based on current load and performance metrics. Allow users to manually adjust resources allocated to specific agents or tasks from the dashboard.
                    """)
                    agent_id = gr.Textbox(label="Agent ID", placeholder="Enter agent ID here...")
                    cpu_allocation = gr.Slider(minimum=0, maximum=100, step=1, label="CPU Allocation (%)")
                    memory_allocation = gr.Slider(minimum=0, maximum=64, step=1, label="Memory Allocation (GB)")
                    allocate_resources_button = gr.Button("Allocate Resources")

                    # Placeholder function to simulate resource allocation
                    def allocate_resources(agent_id, cpu, memory):
                        return f"Resources allocated to agent {agent_id}: CPU {cpu}%, Memory {memory}GB"

                    allocate_resources_button.click(allocate_resources, inputs=[agent_id, cpu_allocation, memory_allocation], outputs=gr.Textbox(label="Allocation Status"))

                with gr.Accordion("Auto-scaling Policies"):
                 
                    gr.Markdown("""
                    **Description:**
                    Implement auto-scaling capabilities that automatically adjust the number of active agents based on predefined triggers such as CPU usage, memory usage, or task queue length. Provide a visual interface for setting up and adjusting auto-scaling rules.
                    """)
                    scaling_trigger = gr.Dropdown(label="Scaling Trigger", choices=["CPU Usage", "Memory Usage", "Task Queue Length"])
                    trigger_threshold = gr.Slider(minimum=0, maximum=100, step=1, label="Trigger Threshold (%)")
                    scale_up_button = gr.Button("Scale Up")
                    scale_down_button = gr.Button("Scale Down")

                    # Placeholder functions to simulate scaling actions
                    def scale_up(trigger, threshold):
                        return f"Scaling up agents based on {trigger} with threshold {threshold}%"

                    def scale_down(trigger, threshold):
                        return f"Scaling down agents based on {trigger} with threshold {threshold}%"

                    scale_up_button.click(scale_up, inputs=[scaling_trigger, trigger_threshold], outputs=gr.Textbox(label="Scale Up Status"))
                    scale_down_button.click(scale_down, inputs=[scaling_trigger, trigger_threshold], outputs=gr.Textbox(label="Scale Down Status"))

                with gr.Accordion("Load Balancing Strategies"):
               
                    gr.Markdown("""
                    **Description:**
                    Offer multiple load balancing strategies (e.g., round-robin, least connections, IP hash) that can be configured per project or agent group. Integrate with existing load balancers and provide analytics on load distribution efficiency.
                    """)
                    load_balancing_strategy = gr.Dropdown(label="Load Balancing Strategy", choices=["Round-robin", "Least Connections", "IP Hash"])
                    apply_strategy_button = gr.Button("Apply Strategy")

                    # Placeholder function to simulate applying load balancing strategy
                    def apply_strategy(strategy):
                        return f"Load balancing strategy applied: {strategy}"

                    apply_strategy_button.click(apply_strategy, inputs=[load_balancing_strategy], outputs=gr.Textbox(label="Strategy Status"))

            with gr.Tab("Deployment and Maintenance Tools"):
                

                with gr.Accordion("CI/CD for Agents"):
                  
                    gr.Markdown("""
                    **Description:**
                    Enable Continuous Integration/Continuous Deployment (CI/CD) pipelines within the dashboard to automate the testing, deployment, and updating of agents. Provide templates and wizards to help users set up their CI/CD workflows.
                    """)
                    pipeline_name = gr.Textbox(label="Pipeline Name", placeholder="Enter pipeline name here...")
                    git_repo_url = gr.Textbox(label="Git Repository URL", placeholder="Enter Git repository URL here...")
                    deployment_branch = gr.Textbox(label="Deployment Branch", placeholder="Enter deployment branch here...")
                    setup_pipeline_button = gr.Button("Setup CI/CD Pipeline")

                    # Placeholder function to simulate CI/CD pipeline setup
                    def setup_pipeline(name, repo, branch):
                        return f"CI/CD pipeline '{name}' set up for repository {repo} on branch {branch}"

                    setup_pipeline_button.click(setup_pipeline, inputs=[pipeline_name, git_repo_url, deployment_branch], outputs=gr.Textbox(label="Pipeline Setup Status"))

                with gr.Accordion("Health Checks and Recovery Procedures"):
                  
                    gr.Markdown("""
                    **Description:**
                    Implement health checks that automatically assess the operational status of agents and trigger recovery procedures if anomalies are detected. Allow users to customize health check parameters and associated recovery actions.
                    """)
                    agent_id = gr.Textbox(label="Agent ID", placeholder="Enter agent ID here...")
                    health_check_interval = gr.Slider(minimum=1, maximum=60, step=1, label="Health Check Interval (minutes)")
                    recovery_action = gr.Dropdown(label="Recovery Action", choices=["Restart Agent", "Notify Admin", "Escalate Issue"])
                    setup_health_check_button = gr.Button("Setup Health Check")

                    # Placeholder function to simulate health check setup
                    def setup_health_check(agent_id, interval, action):
                        return f"Health check set for agent {agent_id} with interval {interval} minutes and action '{action}'"

                    setup_health_check_button.click(setup_health_check, inputs=[agent_id, health_check_interval, recovery_action], outputs=gr.Textbox(label="Health Check Setup Status"))

    return app

def collaboration_governance():
  with gr.Accordion("Agent Collaboration"):
    with gr.Tab("Agent Interactions"):
        interaction_types = ["Collaboration Request", "Knowledge Share", "Task Handoff", "Feedback", "Hive Structure Update", "Evolutionary Adaptation", "Self-Orchestration"]
        interaction_table = gr.DataFrame(
            value=[
                ["Sales AI", "Support Bot", "Collaboration Request", "Help with customer issue", "In Progress"],
                ["Marketing Assistant", "Data Analyst", "Knowledge Share", "Campaign insights", "Completed"],
                ["HR Coordinator", "IT Helpdesk", "Task Handoff", "New employee onboarding", "Pending"],
                ["Data Analyst", "Sales AI", "Feedback", "Improve data collection process", "In Progress"],
                ["All Agents", "Hive Mind", "Hive Structure Update", "Reorganize into specialized clusters", "Completed"],
                ["Support Bot", "Support Bot", "Evolutionary Adaptation", "Optimize response time by 10%", "In Progress"],
                ["Project Manager", "Task Allocation Engine", "Self-Orchestration", "Dynamically assign tasks based on agent availability and skills", "Ongoing"]
            ],
            headers=["Agent 1", "Agent 2", "Interaction Type", "Description", "Status"],
            row_count=10,
        )
        
        with gr.Row():
            agent1_dropdown = gr.Dropdown(["Sales AI", "Support Bot", "Marketing Assistant", "Data Analyst", "HR Coordinator", "IT Helpdesk", "Project Manager", "All Agents"], label="Agent 1")
            agent2_dropdown = gr.Dropdown(["Sales AI", "Support Bot", "Marketing Assistant", "Data Analyst", "HR Coordinator", "IT Helpdesk", "Task Allocation Engine", "Hive Mind"], label="Agent 2") 
            interaction_type_dropdown = gr.Dropdown(interaction_types, label="Interaction Type")
        interaction_description = gr.Textbox(label="Description")
        status_dropdown = gr.Dropdown(["Pending", "In Progress", "Completed", "Ongoing"], label="Status")
        add_interaction_button = gr.Button("Add Interaction")
        
        def add_interaction(agent1, agent2, interaction_type, description, status, interaction_table):
            interaction_table.append([agent1, agent2, interaction_type, description, status])
            return interaction_table
        
        add_interaction_button.click(add_interaction, inputs=[agent1_dropdown, agent2_dropdown, interaction_type_dropdown, interaction_description, status_dropdown, interaction_table], outputs=interaction_table)
        
    with gr.Tab("Agent Communication"):
        chat_history = gr.Chatbot(
            value=[
                ("Sales AI", "Hey Support Bot, I noticed a customer had an issue with their order. Can you help me understand what happened?"),
                ("Support Bot", "Sure thing, Sales AI! It looks like the customer's order was delayed due to an inventory issue. I've already reached out to them to apologize and offer a discount on their next purchase."),
                ("Sales AI", "Great, thanks for handling that! Let's make sure to follow up with them in a week to see if they have any other concerns."),
                ("Marketing Assistant", "Hey everyone, I just finished analyzing our latest campaign data. Looks like we had a 20% increase in website traffic!"),
                ("Data Analyst", "That's fantastic, Marketing Assistant! Can you share those insights in our next team meeting? I'd love to dive deeper into the data."),
                ("HR Coordinator", "Attention all agents: we will be rolling out a new performance evaluation system next month. Stay tuned for more details!"),
                ("IT Helpdesk", "Reminder: server maintenance is scheduled for this weekend. Please save your work and log out of all systems by Friday evening."),
                ("Project Manager", "Team, our client has requested a status update on the Q3 initiatives. Please update your task progress in the project management tool by EOD."),
                ("Hive Mind", "Attention all agents: a new collective learning module has been deployed. Please run the self-update process to integrate the latest knowledge base."),
                ("Task Allocation Engine", "Optimizing agent assignments for maximum efficiency. Standby for updated task lists.")
            ],
            label="Agent Chat History"
        )
        message_input = gr.Textbox(label="Enter a message...")
        send_button = gr.Button("Send")
        
        def send_message(message, chat_history):
            chat_history.append(("Current Agent", message))
            return chat_history
        
        send_button.click(send_message, inputs=[message_input, chat_history], outputs=chat_history)
        
    with gr.Tab("Agent Teams"):
        team_table = gr.DataFrame(
            value=[
                ["Sales Team", "Sales AI, Support Bot, Marketing Assistant", "Improve customer acquisition and retention"],
                ["Analytics Team", "Data Analyst, Marketing Assistant", "Leverage data insights for business decisions"],
                ["HR & IT Team", "HR Coordinator, IT Helpdesk", "Streamline employee experience"],
                ["Executive Team", "CEO Bot, CFO Bot, COO Bot", "Optimize overall company strategy and performance"],
                ["R&D Team", "Research AI, Development AI, Testing AI", "Innovate and develop cutting-edge AI solutions"],
                ["Security Team", "Cybersecurity AI, Compliance AI, Fraud Detection AI", "Ensure data protection and regulatory compliance"],
                ["Customer Success Team", "Onboarding AI, Customer Service AI, Retention AI", "Deliver exceptional customer experiences"]
            ],
            headers=["Team Name", "Members", "Goal"],
            row_count=10,
        )
        
        team_name_input = gr.Textbox(label="Team Name")
        team_members_input = gr.Textbox(label="Members (comma-separated)")
        team_goal_input = gr.Textbox(label="Team Goal")
        create_team_button = gr.Button("Create Team")
        
        def create_team(name, members, goal, team_table):
            team_table.append([name, members, goal])
            return team_table
        
        create_team_button.click(create_team, inputs=[team_name_input, team_members_input, team_goal_input, team_table], outputs=team_table)
        
    with gr.Tab("Deployment & Evolution"):
        deployment_patterns = ["Centralized", "Decentralized", "Hybrid", "Microservices", "Serverless"]
        learning_methods = ["Reinforcement Learning", "Supervised Learning", "Unsupervised Learning", "Transfer Learning", "Continual Learning"]
        
        with gr.Row():
            deployment_pattern_dropdown = gr.Dropdown(deployment_patterns, value="Hybrid", label="Deployment Pattern")
            learning_method_dropdown = gr.Dropdown(learning_methods, value="Continual Learning", label="Learning Method")
            
        evolution_strategies = ["Genetic Algorithms", "Evolutionary Strategies", "Neuroevolution", "Lamarckian Evolution", "Baldwinian Evolution"]
        hive_structures = ["Hierarchical", "Flat", "Matrix", "Network", "Swarm"]
        
        with gr.Row():
            evolution_strategy_dropdown = gr.Dropdown(evolution_strategies, value="Neuroevolution", label="Evolution Strategy")
            hive_structure_dropdown = gr.Dropdown(hive_structures, value="Network", label="Hive Structure")
            
        self_orchestration_slider = gr.Slider(0, 100, value=75, step=1, label="Self-Orchestration Level (%)")
        deployment_button = gr.Button("Deploy Agent System")
        
        def deploy_agent_system(deployment_pattern, learning_method, evolution_strategy, hive_structure, self_orchestration_level):
            # Placeholder for deployment logic
            return f"Agent system deployed with {deployment_pattern} pattern, {learning_method}, {evolution_strategy}, {hive_structure} structure, and {self_orchestration_level}% self-orchestration."
        
        deployment_output = gr.Textbox(label="Deployment Status")
        deployment_button.click(deploy_agent_system, inputs=[deployment_pattern_dropdown, learning_method_dropdown, evolution_strategy_dropdown, hive_structure_dropdown, self_orchestration_slider], outputs=deployment_output)
  return gr.Column()

def governance():
  with gr.Accordion("Governance Controls"):
    with gr.Tab("Boundaries & Constraints"):
        boundary_types = ["Ethical", "Legal", "Operational", "Financial", "Reputational"]
        boundary_table = gr.DataFrame(
            value=[
                ["Ethical", "Ensure agents adhere to company values and principles", "Active"],
                ["Legal", "Comply with all relevant laws and regulations", "Active"],
                ["Operational", "Maintain service level agreements and uptime targets", "Active"],
                ["Financial", "Stay within allocated budgets and resource limits", "Warning"],
                ["Reputational", "Protect company brand and public image", "Active"]
            ],
            headers=["Boundary Type", "Description", "Status"],
            row_count=5,
        )
        
        boundary_name_input = gr.Textbox(label="Boundary Name")
        boundary_type_dropdown = gr.Dropdown(boundary_types, label="Boundary Type")
        boundary_description_input = gr.Textbox(label="Boundary Description")
        add_boundary_button = gr.Button("Add Boundary")
        
        def add_boundary(name, boundary_type, description, boundary_table):
            boundary_table.append([name, boundary_type, description, "Active"])
            return boundary_table
        
        add_boundary_button.click(add_boundary, inputs=[boundary_name_input, boundary_type_dropdown, boundary_description_input, boundary_table], outputs=boundary_table)
        
    with gr.Tab("Human Feedback"):
        feedback_types = ["Approval", "Rejection", "Modification", "Clarification"]
        feedback_table = gr.DataFrame(
            value=[
                ["Sales AI", "Generated product description", "Approval", "Great job, the description is accurate and compelling"],
                ["Support Bot", "Handled customer complaint", "Modification", "The response was a bit too formal, try to be more empathetic"],
                ["Marketing Assistant", "Created ad campaign", "Clarification", "What is the target audience for this campaign?"],
                ["Data Analyst", "Provided sales forecast", "Approval", "The forecast looks solid, well done"],
                ["HR Coordinator", "Sent onboarding email", "Rejection", "The email contains outdated information, please update"]
            ],
            headers=["Agent", "Task", "Feedback Type", "Feedback Details"],
            row_count=5,
        )
        
        agent_dropdown = gr.Dropdown(["Sales AI", "Support Bot", "Marketing Assistant", "Data Analyst", "HR Coordinator"], label="Agent")
        task_input = gr.Textbox(label="Task")
        feedback_type_dropdown = gr.Dropdown(feedback_types, label="Feedback Type")
        feedback_details_input = gr.Textbox(label="Feedback Details")
        submit_feedback_button = gr.Button("Submit Feedback")
        
        def submit_feedback(agent, task, feedback_type, details, feedback_table):
            feedback_table.append([agent, task, feedback_type, details])
            return feedback_table
        
        submit_feedback_button.click(submit_feedback, inputs=[agent_dropdown, task_input, feedback_type_dropdown, feedback_details_input, feedback_table], outputs=feedback_table)
        
    with gr.Tab("Output Review"):
        review_status_options = ["Pending", "Approved", "Rejected"]
        output_review_table = gr.DataFrame(
            value=[
                ["Blog post", "Top 10 AI Trends for 2025", "Marketing Assistant", "Pending"],
                ["Financial report", "Q3 2024 Earnings Analysis", "Data Analyst", "Approved"],
                ["Product design", "Smartwatch UI Mockups", "Design AI", "Rejected"],
                ["Customer email", "Response to Billing Inquiry", "Support Bot", "Approved"],
                ["News article", "Company Announces New AI Partnership", "Content Creator", "Pending"]
            ],  
            headers=["Output Type", "Output Name", "Agent", "Review Status"],
            row_count=5,
        )
        
        output_type_input = gr.Textbox(label="Output Type")
        output_name_input = gr.Textbox(label="Output Name")  
        agent_dropdown = gr.Dropdown(["Sales AI", "Support Bot", "Marketing Assistant", "Data Analyst", "HR Coordinator", "Design AI", "Content Creator"], label="Agent")
        review_status_dropdown = gr.Dropdown(review_status_options, label="Review Status")
        submit_review_button = gr.Button("Submit Review")
        
        def submit_review(output_type, output_name, agent, review_status, output_review_table):
            output_review_table.append([output_type, output_name, agent, review_status])
            return output_review_table
        
        submit_review_button.click(submit_review, inputs=[output_type_input, output_name_input, agent_dropdown, review_status_dropdown, output_review_table], outputs=output_review_table)
        
    with gr.Tab("Alerts & Notifications"):
        alert_types = ["Boundary Violation", "Anomalous Behavior", "System Error", "Human Feedback Required"]
        alert_urgency_levels = ["Low", "Medium", "High", "Critical"]  
        alert_table = gr.DataFrame(
            value=[
                ["Boundary Violation", "Sales AI attempted to access restricted data", "High", "Unresolved"],
                ["Anomalous Behavior", "Support Bot response time increased by 150%", "Medium", "Under Investigation"],
                ["System Error", "Marketing Assistant encountered a memory leak", "Critical", "Resolved"], 
                ["Human Feedback Required", "Data Analyst report requires manager approval", "Low", "Pending"],
                ["Boundary Violation", "HR Coordinator tried to modify employee records without authorization", "High", "Unresolved"]
            ],
            headers=["Alert Type", "Description", "Urgency", "Status"], 
            row_count=5,
        )
        
        alert_type_dropdown = gr.Dropdown(alert_types, label="Alert Type")
        alert_description_input = gr.Textbox(label="Alert Description")
        alert_urgency_dropdown = gr.Dropdown(alert_urgency_levels, label="Alert Urgency")
        alert_status_input = gr.Textbox(label="Alert Status", value="Unresolved", interactive=False)
        create_alert_button = gr.Button("Create Alert")
        
        def create_alert(alert_type, description, urgency, status, alert_table):
            alert_table.append([alert_type, description, urgency, status])
            return alert_table
        
        create_alert_button.click(create_alert, inputs=[alert_type_dropdown, alert_description_input, alert_urgency_dropdown, alert_status_input, alert_table], outputs=alert_table)

  return gr.Column()
def analytics_reporting():
    with gr.Accordion("Key Metrics", open=True):
        # Sample data
        dates = ["2023-01-01", "2023-01-08", "2023-01-15", "2023-01-22", "2023-01-29",
                 "2023-02-05", "2023-02-12", "2023-02-19", "2023-02-26", "2023-03-05",
                 "2023-03-12", "2023-03-19", "2023-03-26", "2023-04-02", "2023-04-09"]
        total_agents = [500, 525, 540, 560, 575, 590, 600, 615, 630, 645, 660, 675, 690, 700, 710]
        active_agents = [375, 395, 410, 430, 445, 460, 475, 490, 505, 520, 535, 550, 565, 580, 595]
        tasks_completed = [8000, 8500, 9200, 9800, 10400, 11000, 11600, 12200, 12800, 13400, 14000, 14600, 15200, 15800, 16400]
        avg_rating = [4.5, 4.52, 4.54, 4.56, 4.58, 4.6, 4.62, 4.64, 4.66, 4.68, 4.7, 4.72, 4.74, 4.76, 4.78]

        agent_metrics = {
            "Date": dates,
            "Total Agents": total_agents,
            "Active Agents": active_agents,
            "Tasks Completed": tasks_completed,
            "Avg Rating": avg_rating
        }

        agent_teams = {
            "Team": ["Sales", "Support", "Marketing", "Analytics", "HR"],
            "Agents": [120, 180, 95, 65, 40],
            "Tasks": [3200, 4800, 2400, 1600, 800],
            "Avg Rating": [4.7, 4.75, 4.65, 4.8, 4.72]
        }

        with gr.Row():
            with gr.Column():
                fig1 = px.line(agent_metrics, x='Date', y=['Total Agents', 'Active Agents'], title='Agent Workforce Metrics Over Time')
                plot1 = gr.Plot(fig1)
            with gr.Column():
                fig2 = px.line(agent_metrics, x='Date', y='Tasks Completed', title='Tasks Completed Over Time')
                plot2 = gr.Plot(fig2)

        with gr.Row():
            with gr.Column():
                fig3 = px.bar(agent_metrics, x='Date', y='Avg Rating', title='Average Agent Rating Over Time')
                plot3 = gr.Plot(fig3)
            with gr.Column():
                fig4 = px.bar(agent_teams, x='Team', y=['Agents', 'Tasks'], barmode='group', title='Agent Performance by Team')
                plot4 = gr.Plot(fig4)

        # Additional example
        with gr.Row():
          with gr.Column():
              fig5 = px.scatter(agent_metrics, x='Tasks Completed', y='Avg Rating', color='Active Agents', title='Correlation Between Tasks Completed, Rating, and Active Agents')
              plot5 = gr.Plot(fig5)
          with gr.Column():
              fig6 = px.pie(agent_teams, values='Agents', names='Team', title='Agent Distribution by Team')
              plot6 = gr.Plot(fig6)

    with gr.Accordion("Reporting", open=True):
      with gr.Tab("Custom Reports"):
            report_types = ["Agent Activities", "Agent Outputs", "Quality Levels"]
            report_type_dropdown = gr.Dropdown(report_types, value="Agent Activities", label="Report Type")
            
            report_period = ["Last 7 Days", "Last 30 Days", "Last 6 Months", "Last Year", "Custom Range"]
            report_period_dropdown = gr.Dropdown(report_period, value="Last 30 Days", label="Report Period")
            
            start_date_input = gr.Textbox(label="Start Date (YYYY-MM-DD)")
            end_date_input = gr.Textbox(label="End Date (YYYY-MM-DD)")
            
            generate_report_button = gr.Button("Generate Report")
            
            report_output = gr.DataFrame(
                value=[
                    ["Sales AI", 1250, 85, 4.7],
                    ["Support Bot", 2100, 92, 4.8],
                    ["Marketing Assistant", 875, 78, 4.5],
                    ["Data Analyst", 625, 90, 4.9],
                    ["HR Coordinator", 375, 82, 4.6]
                ],
                headers=["Agent", "Tasks Completed", "Success Rate (%)", "Avg Rating"],
                row_count=5,
            )
          
      with gr.Tab("Forecasting"):
          forecast_metrics = ["Total Agents", "Active Agents", "Tasks Completed", "Avg Rating"]
          forecast_metric_dropdown = gr.Dropdown(forecast_metrics, value="Tasks Completed", label="Forecast Metric")
          
          forecast_period = ["Next 7 Days", "Next 30 Days", "Next 6 Months", "Next Year"]
          forecast_period_dropdown = gr.Dropdown(forecast_period, value="Next 30 Days", label="Forecast Period")
          
          forecast_button = gr.Button("Generate Forecast")
          
          forecast_plot = gr.LinePlot(x="Date", y="Value", title="Forecast for Tasks Completed")
          
          def generate_report(report_type, report_period, start_date, end_date):
              # Placeholder logic to generate custom report based on selected parameters
              return report_output
          
          def generate_forecast(metric, period):
              # Placeholder logic to generate forecast based on selected parameters
              dates = pd.date_range(start='2023-01-01', end='2023-06-01', freq='W')
              values = np.random.randint(10000, 20000, size=len(dates))
              forecast_data = pd.DataFrame({"Date": dates, "Value": values})
              forecast_plot.plot(forecast_data, x="Date", y="Value", title=f"Forecast for {metric} ({period})")
      
      generate_report_button.click(generate_report, inputs=[report_type_dropdown, report_period_dropdown, start_date_input, end_date_input], outputs=report_output)
      forecast_button.click(generate_forecast, inputs=[forecast_metric_dropdown, forecast_period_dropdown], outputs=forecast_plot)    
      
      
    return gr.Column(plot1, plot2, plot3, plot4, plot5, plot6)

  # return gr.Column()

def system_settings():
    with gr.Accordion("System Wide Settings"):
        with gr.Tab("Agent Parameters"):
            gr.Markdown("### Agent Parameters")
            gr.Markdown("Configure global parameters that affect all agents within the system.")
            
            with gr.Row():
                default_learning_rate = gr.Slider(minimum=0.01, maximum=1.0, value=0.1, step=0.01, label="Default Learning Rate")
                default_exploration_rate = gr.Slider(minimum=0.01, maximum=1.0, value=0.1, step=0.01, label="Default Exploration Rate")
            
            with gr.Row():
                agent_types = gr.CheckboxGroup(choices=["Conversational", "Retrieval-based", "Generative", "Analytical"], value=["Conversational", "Retrieval-based"], label="Agent Types")
                agent_specializations = gr.CheckboxGroup(choices=["Sales", "Support", "Marketing", "Data Analysis", "HR"], value=["Sales", "Support"], label="Agent Specializations")
            
            with gr.Row():
                reward_structure = gr.Radio(choices=["Fixed", "Variable", "Performance-based"], value="Fixed", label="Reward Structure")
                max_tokens = gr.Number(value=512, label="Max Tokens per Response")
            
        with gr.Tab("Resource Management"):
            gr.Markdown("### Resource Management")
            gr.Markdown("Manage compute resources and cost controls for the agent system.")
            
            with gr.Row():
                max_compute_usage = gr.Number(value=10000, label="Max Compute Usage (vCPU hours)")
                max_storage_usage = gr.Number(value=1000, label="Max Storage Usage (GB)")
            
            with gr.Row():
                cost_per_compute_hour = gr.Number(value=0.05, label="Cost per Compute Hour ($)")
                cost_per_gb_storage = gr.Number(value=0.02, label="Cost per GB Storage ($)")
            
        with gr.Tab("Access & Permissions"):
            gr.Markdown("### Access & Permissions")
            gr.Markdown("Configure access levels and permissions for users interacting with the agent system.")
            
            with gr.Row():
                user_roles = gr.CheckboxGroup(choices=["Administrator", "Developer", "Analyst", "Viewer"], value=["Administrator", "Developer"], label="User Roles")
                enable_api_access = gr.Checkbox(label="Enable API Access", value=True)
            
            with gr.Row():
                api_rate_limit = gr.Number(value=1000, label="API Rate Limit (requests/day)")
                enable_logging = gr.Checkbox(label="Enable Activity Logging", value=True)
            
    return gr.Column()

def documentation():
    with gr.Tabs():
        with gr.Tab("Introduction"):
          gr.Markdown("""
          ## Introduction

          Welcome to the comprehensive documentation for the Agentic Employment Infrastructure. This platform is designed to empower organizations by automating and enhancing their employment processes through advanced autonomous agents. Here you will find all the information needed to effectively use and customize the system.

          ### Purpose of the Application

          The Agentic Employment Infrastructure aims to revolutionize the way businesses manage and interact with their workforce. By leveraging cutting-edge AI and machine learning technologies, this platform automates routine tasks, optimizes workforce management, and provides insightful analytics to drive decision-making.

          ### Benefits

          - **Efficiency**: Automates routine and complex tasks, reducing the need for manual intervention and speeding up processes.
          - **Scalability**: Easily scales to meet the needs of any organization size, from small businesses to large enterprises.
          - **Customization**: Highly customizable to fit specific organizational needs and workflows.
          - **Insightful Analytics**: Offers deep insights into workforce performance and operational efficiency, enabling better strategic planning.
          - **Cost-Effective**: Reduces operational costs by automating tasks and optimizing resource allocation.

          ### Usages

          - **HR Management**: Automates various HR tasks such as recruitment, onboarding, and employee management.
          - **Task Automation**: Handles repetitive tasks across different departments, allowing staff to focus on more strategic activities.
          - **Performance Monitoring**: Continuously monitors agent performance and provides feedback for improvement.
          - **Resource Allocation**: Optimizes the allocation of tasks and resources based on real-time data and predictive analytics.

          ### Open Source Contribution

          The Agentic Employment Infrastructure is committed to contributing to the open source community. By sharing improvements and innovations, the platform not only enhances its capabilities but also supports the broader development community. This contribution includes:

          - **Code Sharing**: Releases useful libraries and tools developed during the implementation of the infrastructure.
          - **Community Engagement**: Actively participates in open source projects, providing enhancements and bug fixes.
          - **Knowledge Sharing**: Publishes research findings and technical insights to help advance the field of AI and employment automation.

          ### Overview of Documentation

          This documentation is structured to provide you with all the necessary information to get started, manage, and extend the capabilities of the Agentic Employment Infrastructure. It includes:

          - **Getting Started Guide**: Instructions on setting up and configuring the system for first-time use.
          - **User Guides**: Detailed guides on using the platform's features and functionalities.
          - **Technical Documentation**: In-depth technical resources for developers looking to customize or extend the platform.
          - **FAQs and Troubleshooting**: Helps resolve common issues and provides answers to frequently asked questions.

          Navigate through the tabs to explore detailed documentation on each aspect of the platform.
          """)
        with gr.Tab("Features"):
          gr.Markdown("""
          ## Features

          Welcome to the Features section of the Agentic Employment Infrastructure. This platform is designed to streamline and enhance various aspects of workforce management through advanced autonomous agents. Below is a detailed overview of the key features that empower organizations to operate more efficiently and effectively.

          ### Agent Management
          - **Dashboard Overview**: Visualize key metrics about agent activity, including total agents, active status, and task completion rates.
          - **Agent Customization**: Tailor agent behaviors and capabilities to meet specific organizational needs.
          - **Performance Tracking**: Monitor and analyze the performance of each agent with detailed reports and real-time data.

          ### Task Automation
          - **Workflow Automation**: Automate routine tasks to increase efficiency and reduce manual effort.
          - **Dynamic Task Allocation**: Assign tasks to agents based on availability, skill level, and workload to optimize productivity.

          ### Collaboration Tools
          - **Agent Collaboration**: Enable agents to collaborate on tasks and projects, sharing information and learning from each other.
          - **Human-Agent Interaction**: Facilitate seamless interaction between human employees and agents for hybrid workforce management.

          ### Advanced Configuration
          - **Custom API Integration**: Integrate with existing systems and third-party services through customizable APIs.
          - **Configuration Wizards**: Use guided wizards to configure complex settings without needing deep technical knowledge.

          ### Analytics and Reporting
          - **Real-Time Analytics**: Gain insights from real-time data on agent operations and system performance.
          - **Custom Reports**: Generate custom reports to track specific metrics and KPIs important to your organization.

          ### Security and Compliance
          - **Data Security**: Ensure the security of organizational data with robust encryption and compliance with international standards.
          - **Audit Trails**: Maintain detailed logs of all system activities for compliance and auditing purposes.

          ### User Experience
          - **Intuitive Interface**: Engage with a user-friendly interface that simplifies the management of complex systems.
          - **Personalization Options**: Customize the user interface according to individual preferences and roles.

          ### Scalability and Integration
          - **Scalable Architecture**: Easily scale the system to handle increasing loads and more complex operations.
          - **Seamless Integration**: Integrate smoothly with other enterprise systems and platforms to enhance functionality.

          ### Support and Training
          - **Comprehensive Support**: Access 24/7 support services to ensure continuous system operation.
          - **Training Modules**: Benefit from detailed training modules to maximize the use of the system's features.

          These features are designed to make the Agentic Employment Infrastructure a powerful tool for modern organizations, aiming to enhance efficiency, reduce costs, and improve overall operational effectiveness.
          """)
        with gr.Tab("Capabilities"):
            gr.Markdown("## Capabilities\nExplore the capabilities of the system including agent management, collaboration tools, and more.")
        
        with gr.Tab("Customization"):
            gr.Markdown("## Customization\nGuidance on how to customize the system to fit your specific needs.")
        
        with gr.Tab("Advanced Configuration"):
            gr.Markdown("## Advanced Configuration\nInstructions for advanced configurations to optimize system performance.")
        
        with gr.Tab("API Documentation"):
            gr.Markdown("## API Documentation\nDetailed API documentation for developers to integrate and extend the system functionalities.")
        
        with gr.Tab("Resource Management"):
            gr.Markdown("## Resource Management\nManage your computational and storage resources effectively using the system's built-in tools.")
        
        with gr.Tab("Planning and Optimization"):
            gr.Markdown("## Planning and Optimization\nStrategies and tools for planning and optimizing your deployment of the system.")
        
        with gr.Tab("Search Documentation"):
            gr.Markdown("## Search Documentation\nLearn how to effectively search and retrieve information from the documentation.")

with gr.Blocks() as app:
    documentation()

with gr.Blocks() as agentic_dashboard:
  gr.Markdown("# 🪰 Agentic Employment Infrastructure")
  gr.Markdown("### Manage an adaptive network of autonomous agents")

  with gr.Tab("Agent Management"):
    agent_management()

  with gr.Tab("Agentic Command & Control"):
    agent_controls()

  with gr.Tab("Collaboration & Governance"):
    collaboration_governance()

  with gr.Tab("Governance"):
    governance()

  with gr.Tab("Analytics & Reporting"):
    analytics_reporting()
      
  with gr.Tab("System Settings"):
    system_settings()

  with gr.Tab("Documentation"):
    documentation()  

# Launch the interface
agentic_dashboard.launch(debug=True)

setup.sh

#!/bin/bash

# Root directory
mkdir -p agentic_employment_infrastructure

# Backend directories and files
mkdir -p agentic_employment_infrastructure/backend/app/{routes,services,models,utils}
mkdir -p agentic_employment_infrastructure/backend/tests

# Backend: app/__init__.py
cat > agentic_employment_infrastructure/backend/app/__init__.py <<EOL
# app/__init__.py

EOL

# Backend: app/main.py
cat > agentic_employment_infrastructure/backend/app/main.py <<EOL
from fastapi import FastAPI
from backend.app.routes import agent, settings

app = FastAPI()

app.include_router(agent.router)
app.include_router(settings.router)

@app.get("/")
async def root():
    return {"message": "Welcome to the Agentic Employment Infrastructure Backend"}
EOL

# Backend: app/routes/__init__.py
cat > agentic_employment_infrastructure/backend/app/routes/__init__.py <<EOL
# routes/__init__.py

EOL

# Backend: app/routes/agent.py
cat > agentic_employment_infrastructure/backend/app/routes/agent.py <<EOL
from fastapi import APIRouter

router = APIRouter()

@router.get("/agents")
async def get_agents():
    return {"message": "List of agents"}
EOL

# Backend: app/routes/settings.py
cat > agentic_employment_infrastructure/backend/app/routes/settings.py <<EOL
from fastapi import APIRouter

router = APIRouter()

@router.get("/settings")
async def get_settings():
    return {"message": "Settings page"}
EOL

# Backend: app/services/__init__.py
cat > agentic_employment_infrastructure/backend/app/services/__init__.py <<EOL
# services/__init__.py

EOL

# Backend: app/services/agent_service.py
cat > agentic_employment_infrastructure/backend/app/services/agent_service.py <<EOL
# Placeholder for agent service logic

def create_agent(agent_name, agent_type):
    return f"Agent '{agent_name}' of type '{agent_type}' created successfully."
EOL

# Backend: app/services/llm_service.py
cat > agentic_employment_infrastructure/backend/app/services/llm_service.py <<EOL
# Placeholder for LLM service logic

def get_llm_response(prompt):
    return f"Response for the prompt: {prompt}"
EOL

# Backend: app/models/__init__.py
cat > agentic_employment_infrastructure/backend/app/models/__init__.py <<EOL
# models/__init__.py

EOL

# Backend: app/models/agent_model.py
cat > agentic_employment_infrastructure/backend/app/models/agent_model.py <<EOL
# Placeholder for agent data model

class Agent:
    def __init__(self, name, agent_type):
        self.name = name
        self.agent_type = agent_type
EOL

# Backend: app/utils/__init__.py
cat > agentic_employment_infrastructure/backend/app/utils/__init__.py <<EOL
# utils/__init__.py

EOL

# Backend: app/utils/helpers.py
cat > agentic_employment_infrastructure/backend/app/utils/helpers.py <<EOL
# Placeholder for utility helper functions

def log_message(message):
    print(f"Log: {message}")
EOL

# Backend: tests/__init__.py
cat > agentic_employment_infrastructure/backend/tests/__init__.py <<EOL
# tests/__init__.py

EOL

# Backend: tests/test_agent.py
cat > agentic_employment_infrastructure/backend/tests/test_agent.py <<EOL
import unittest
from backend.app.services.agent_service import create_agent

class TestAgentService(unittest.TestCase):
    def test_create_agent(self):
        agent_name = "TestAgent"
        agent_type = "Conversational"
        result = create_agent(agent_name, agent_type)
        self.assertEqual(result, f"Agent '{agent_name}' of type '{agent_type}' created successfully.")

if __name__ == '__main__':
    unittest.main()
EOL

# Backend: tests/test_settings.py
cat > agentic_employment_infrastructure/backend/tests/test_settings.py <<EOL
import unittest

class TestSettings(unittest.TestCase):
    def test_settings(self):
        # Placeholder test case
        self.assertTrue(True)

if __name__ == '__main__':
    unittest.main()
EOL

# Backend: requirements.txt
cat > agentic_employment_infrastructure/requirements.txt <<EOL
fastapi
uvicorn
pandas
plotly
gradio
gradio_modal
EOL

# Frontend directories and files
mkdir -p agentic_employment_infrastructure/frontend/{components,static/{css,js},templates}

# Frontend: __init__.py
cat > agentic_employment_infrastructure/frontend/__init__.py <<EOL
# frontend/__init__.py

EOL

# Frontend: main.py
curl -o agentic_employment_infrastructure/frontend/main.py https://gist.githubusercontent.com/ruvnet/4ca07d625820ef64fa10db9cfb6227e4/raw/0b1ecbd3dd1a9f8980768fc7344e5a7ea9337cb5/agentic_employment.py

# Frontend: components/__init__.py
cat > agentic_employment_infrastructure/frontend/components/__init__.py <<EOL
# components/__init__.py

EOL

# Frontend: components/agent_dashboard.py
cat > agentic_employment_infrastructure/frontend/components/agent_dashboard.py <<EOL
import gradio as gr

def agent_dashboard():
    with gr.Blocks() as dashboard:
        gr.Markdown("# Agent Dashboard")
        # Placeholder for agent dashboard components
    return dashboard
EOL

# Frontend: components/settings.py
cat > agentic_employment_infrastructure/frontend/components/settings.py <<EOL
import gradio as gr

def settings():
    with gr.Blocks() as settings:
        gr.Markdown("# Settings")
        # Placeholder for settings components
    return settings
EOL

# Frontend: templates/base.html
cat > agentic_employment_infrastructure/frontend/templates/base.html <<EOL
<!DOCTYPE html>
<html lang="en">
<head>
    <meta charset="UTF-8">
    <meta name="viewport" content="width=device-width, initial-scale=1.0">
    <title>Agentic Employment Infrastructure</title>
</head>
<body>
    {% block content %}{% endblock %}
</body>
</html>
EOL

# Frontend: templates/dashboard.html
cat > agentic_employment_infrastructure/frontend/templates/dashboard.html <<EOL
{% extends "base.html" %}

{% block content %}
<h1>Agent Dashboard</h1>
<!-- Placeholder for dashboard content -->
{% endblock %}
EOL

# Frontend: templates/settings.html
cat > agentic_employment_infrastructure/frontend/templates/settings.html <<EOL
{% extends "base.html" %}

{% block content %}
<h1>Settings</h1>
<!-- Placeholder for settings content -->
{% endblock %}
EOL

# Other files
# .gitignore
cat > agentic_employment_infrastructure/.gitignore <<EOL
__pycache__/
*.pyc
.env
EOL

# README.md
cat > agentic_employment_infrastructure/README.md <<EOL
# Agentic Employment Infrastructure

This project implements an Agentic Employment Infrastructure using FastAPI, Flask, Websockets, LiteLLM, and Gradio. The infrastructure aims to manage and enhance various aspects of workforce management through advanced autonomous agents.

## Project Structure

- **backend/**: Contains the backend code built with FastAPI.
- **frontend/**: Contains the frontend code built with Gradio.
- **scripts/**: Contains setup and utility scripts.

## Getting Started

1. Clone the repository.
2. Run the setup script to create the folder structure and files.
3. Install the required dependencies.
4. Launch the backend and frontend services using Docker Compose.

## Usage

Instructions on how to use the system will be added here.
EOL

# docker-compose.yml
cat > agentic_employment_infrastructure/docker-compose.yml <<EOL
version: '3.8'

services:
  backend:
    build: ./backend
    container_name: backend
    ports:
      - "8000:8000"
    volumes:
      - ./backend:/app
    command: uvicorn backend.app.main:app --reload --host 0.0.0.0 --port 8000

  frontend:
    build: ./frontend
    container_name: frontend
    ports:
      - "7860:7860"
    volumes:
      - ./frontend:/app
    command: python -m frontend.main
EOL

# app.py at the root directory to start the entire app
cat > agentic_employment_infrastructure/app.py <<EOL
import subprocess
import os

def start_backend():
    os.chdir(os.path.dirname(__file__))
    subprocess.run(["uvicorn", "backend.app.main:app", "--reload", "--host", "0.0.0.0", "--port", "8000"])

def start_frontend():
    os.chdir(os.path.dirname(__file__))
    subprocess.run(["python", "-m", "frontend.main"])

if __name__ == "__main__":
    from multiprocessing import Process

    backend_process = Process(target=start_backend)
    backend_process.start()

    frontend_process = Process(target=start_frontend)
    frontend_process.start()

    backend_process.join()
    frontend_process.join()
EOL

echo "Folder structure and initial code created successfully."