"""
app_optimized.py - Memory-Optimized ID Agents
----------------------------------------------

Streamlined version of the complex multi-agent system optimized for HF Spaces memory limits.
Includes core agent builder functionality while reducing memory footprint.
"""

# --- Core Imports ---
import gradio as gr
import json
import re
import os
import asyncio
import logging
from typing import Dict, cast

# Essential imports with graceful fallbacks
try:
    import openai
    from openai import RateLimitError, APIError, APIConnectionError, OpenAI
    OPENAI_AVAILABLE = True
except ImportError:
    OPENAI_AVAILABLE = False

try:
    from core.agents.agent_utils import linkify_citations, build_agent, load_prefilled, prepare_download
    from config import agents_config, skills_library, prefilled_agents
    from core.agents.chat_orchestrator import simulate_agent_response_stream
    CORE_MODULES_AVAILABLE = True
except ImportError:
    CORE_MODULES_AVAILABLE = False
    # Fallback configurations
    agents_config = {}
    skills_library = {
        "🛡️ Antimicrobial Stewardship": ["recommend_deescalation", "alert_prolonged_antibiotic_use"],
        "🦠 Infection Prevention and Control": ["calculate_infection_rate", "assess_outbreak_risk"],
        "🔬 Research Assistant": ["search_pubmed", "summarize_guidelines"],
        "🏥 Clinical Assistant": ["differential_diagnosis", "treatment_recommendations"],
        "📚 Education Assistant": ["create_quiz", "explain_concepts"],
        "🎼 Orchestrator": ["coordinate_agents", "synthesize_responses"]
    }
    prefilled_agents = {
        "Stewardship Expert": {
            "agent_type": "🛡️ Antimicrobial Stewardship",
            "agent_name": "StewardshipBot",
            "agent_mission": "Optimize antibiotic use and prevent resistance",
            "skills": ["recommend_deescalation", "alert_prolonged_antibiotic_use"]
        }
    }

# Set up logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

# OpenAI setup
OPENAI_API_KEY = os.environ.get("OPENAI_API_KEY")
if OPENAI_API_KEY and OPENAI_AVAILABLE:
    client = OpenAI(api_key=OPENAI_API_KEY)
else:
    client = None

def simple_chat_response(user_message, history):
    """Simple chat response for fallback mode"""
    if not client:
        return history + [[user_message, "⚠️ OpenAI not configured. Please check API keys."]], ""
    
    # Convert to OpenAI format
    messages = []
    for user_msg, assistant_msg in history:
        if user_msg:
            messages.append({"role": "user", "content": user_msg})
        if assistant_msg:
            messages.append({"role": "assistant", "content": assistant_msg})
    
    messages.append({"role": "user", "content": user_message})
    
    try:
        completion = client.chat.completions.create(
            model="gpt-3.5-turbo",
            messages=messages,
            temperature=0.7,
        )
        reply = completion.choices[0].message.content.strip()
        return history + [[user_message, reply]], ""
    except Exception as e:
        error_msg = f"Error: {str(e)}"
        return history + [[user_message, error_msg]], ""

def convert_messages_to_gradio(messages):
    """Convert OpenAI messages to Gradio format"""
    if not messages:
        return []
    
    result = []
    for i, msg in enumerate(messages):
        if msg["role"] == "user":
            # Look ahead for assistant response
            assistant_response = ""
            if i + 1 < len(messages) and messages[i + 1]["role"] == "assistant":
                assistant_response = messages[i + 1]["content"]
            result.append([msg["content"], assistant_response])
        elif msg["role"] == "assistant" and (i == 0 or messages[i-1]["role"] != "user"):
            # Standalone assistant message (like greeting)
            result.append([None, msg["content"]])
    
    return result

def safe_agent_chat(agent_name, user_text, histories):
    """Safe agent chat with fallback"""
    if not CORE_MODULES_AVAILABLE:
        response = f"🔧 **Demo Mode**: Agent '{agent_name}' would normally provide specialized infectious disease assistance here."
        history = histories.get(agent_name, [])
        new_history = history + [[user_text, response]]
        histories[agent_name] = new_history
        return new_history, histories, ""
    
    try:
        # Use existing chat logic if available
        from app import chatpanel_handle
        return chatpanel_handle(agent_name, user_text, histories)
    except:
        # Fallback to simple response
        response = f"🤖 Agent '{agent_name}' received: {user_text}\n\nThis is a simplified response due to system limitations."
        history = histories.get(agent_name, [])
        new_history = history + [[user_text, response]]
        histories[agent_name] = new_history
        return new_history, histories, ""

def build_optimized_ui():
    """Build memory-optimized UI"""
    
    # Streamlined CSS - reduced from original
    css = """
    :root {
      --id-primary: #1e40af;
      --id-secondary: #3b82f6;
      --id-accent: #06d6a0;
      --id-bg: #f8fafc;
      --id-surface: #ffffff;
      --id-text: #1e293b;
    }
    
    .gradio-container { background: var(--id-bg) !important; }
    
    .id-header {
      background: linear-gradient(90deg, var(--id-primary), var(--id-secondary));
      color: white;
      padding: 1.5rem;
      border-radius: 12px;
      margin-bottom: 1.5rem;
    }
    
    .generate-btn {
      background: linear-gradient(90deg, var(--id-secondary), var(--id-accent));
      color: white !important;
      border: none !important;
      font-weight: 600;
    }
    """
    
    with gr.Blocks(title="🦠 ID Agents - Optimized", css=css, theme=gr.themes.Soft()) as app:
        
        # State management
        builder_chat_histories = gr.State({})
        
        # Header
        gr.HTML("""
        <div class="id-header">
            <h1 style="margin: 0;">🦠 ID Agents - Multi-Agent Builder</h1>
            <p style="margin: 8px 0 0 0;">Optimized for HF Spaces - Build specialized infectious disease agents</p>
        </div>
        """)
        
        with gr.Tabs():
            # Tab 1: Simple Chat
            with gr.TabItem("💬 Simple Chat"):
                gr.Markdown("### Quick GPT-3.5 Chat")
                simple_chatbot = gr.Chatbot()
                simple_input = gr.Textbox(placeholder="Ask anything...", show_label=False)
                simple_send = gr.Button("Send")
                simple_reset = gr.Button("Reset")
                
                # Wire up simple chat
                simple_send.click(
                    fn=simple_chat_response,
                    inputs=[simple_input, simple_chatbot],
                    outputs=[simple_chatbot, simple_input]
                )
                simple_input.submit(
                    fn=simple_chat_response,
                    inputs=[simple_input, simple_chatbot],
                    outputs=[simple_chatbot, simple_input]
                )
                simple_reset.click(lambda: ([], ""), outputs=[simple_chatbot, simple_input])
            
            # Tab 2: Agent Builder (Streamlined)
            with gr.TabItem("🛠️ Agent Builder"):
                gr.Markdown("### Build Your ID Agent")
                
                with gr.Row():
                    with gr.Column(scale=1):
                        # Simplified form
                        prefilled = gr.Dropdown(
                            choices=["None"] + list(prefilled_agents.keys()), 
                            label="Template", 
                            value="None"
                        )
                        agent_type = gr.Radio(
                            choices=list(skills_library.keys()),
                            label="Agent Type"
                        )
                        agent_name = gr.Textbox(label="Agent Name", placeholder="e.g., StewardBot")
                        agent_mission = gr.Textbox(label="Mission", lines=3)
                        skills = gr.CheckboxGroup(choices=[], label="Skills")
                        generate_button = gr.Button("✨ Generate Agent", elem_classes="generate-btn")
                        
                        # Generated config
                        agent_output = gr.Code(label="Agent Config", language="json")
                    
                    with gr.Column(scale=2):
                        # Agent management
                        gr.Markdown("### Active Agents")
                        active_agents = gr.Markdown("_(None yet)_")
                        
                        agent_dropdown = gr.Dropdown(label="Select Agent", choices=[])
                        
                        with gr.Row():
                            chat_button = gr.Button("💬 Chat")
                            remove_button = gr.Button("❌ Remove")
                        
                        # Chat interface
                        agent_chatbot = gr.Chatbot(label="Agent Chat")
                        chat_input = gr.Textbox(placeholder="Chat with your agent...", show_label=False)
                        chat_send = gr.Button("Send")
                        reset_chat = gr.Button("🔄 Reset")
                
                # Update skills when agent type changes
                def update_skills(agent_type):
                    if agent_type:
                        return gr.update(choices=skills_library.get(agent_type, []))
                    return gr.update(choices=[])
                
                agent_type.change(update_skills, inputs=[agent_type], outputs=[skills])
                
                # Generate agent
                def generate_agent(prefilled_choice, agent_type, agent_name, agent_mission, skills):
                    if not agent_name.strip():
                        return "❌ Please provide an agent name", "", "### Active Agents\n_(None yet)_", gr.update()
                    
                    agent_data = {
                        "agent_type": agent_type,
                        "agent_name": agent_name,
                        "agent_mission": agent_mission,
                        "skills": skills,
                        "web_access": True,
                        "allow_fallback": True
                    }
                    
                    agents_config[agent_name] = json.dumps(agent_data)
                    
                    # Update UI
                    config_json = json.dumps(agent_data, indent=2)
                    active_list = "### Active Agents\n" + "\n".join(f"- {name}" for name in agents_config.keys())
                    choices = list(agents_config.keys())
                    
                    return config_json, agent_data, active_list, gr.update(choices=choices)
                
                generate_button.click(
                    generate_agent,
                    inputs=[prefilled, agent_type, agent_name, agent_mission, skills],
                    outputs=[agent_output, gr.State(), active_agents, agent_dropdown]
                )
                
                # Chat with agent
                def start_agent_chat(agent_name):
                    if agent_name:
                        greeting = f"👋 Hello! I'm {agent_name}. How can I assist you with infectious diseases?"
                        return [[None, greeting]], ""
                    return [], ""
                
                chat_button.click(
                    start_agent_chat,
                    inputs=[agent_dropdown],
                    outputs=[agent_chatbot, chat_input]
                )
                
                # Send message to agent
                def send_to_agent(agent_name, message, history, histories):
                    if not agent_name or not message.strip():
                        return history, "", histories
                    
                    return safe_agent_chat(agent_name, message, histories)
                
                chat_send.click(
                    send_to_agent,
                    inputs=[agent_dropdown, chat_input, agent_chatbot, builder_chat_histories],
                    outputs=[agent_chatbot, chat_input, builder_chat_histories]
                )
                
                chat_input.submit(
                    send_to_agent,
                    inputs=[agent_dropdown, chat_input, agent_chatbot, builder_chat_histories],
                    outputs=[agent_chatbot, chat_input, builder_chat_histories]
                )
                
                # Reset chat
                reset_chat.click(lambda: ([], ""), outputs=[agent_chatbot, chat_input])
                
                # Remove agent
                def remove_agent(agent_name):
                    if agent_name in agents_config:
                        del agents_config[agent_name]
                    
                    active_list = "### Active Agents\n" + "\n".join(f"- {name}" for name in agents_config.keys()) if agents_config else "### Active Agents\n_(None yet)_"
                    choices = list(agents_config.keys())
                    
                    return active_list, gr.update(choices=choices, value=None)
                
                remove_button.click(
                    remove_agent,
                    inputs=[agent_dropdown],
                    outputs=[active_agents, agent_dropdown]
                )
        
        # Status info
        status_md = "✅ **Optimized Mode**: Core functionality available"
        if CORE_MODULES_AVAILABLE:
            status_md += "\n✅ **Full modules loaded**"
        else:
            status_md += "\n⚠️ **Demo mode**: Limited functionality"
        
        gr.Markdown(f"### System Status\n{status_md}")
        
    return app

# --- Main Application Entry Point ---
if __name__ == "__main__":
    try:
        print("🚀 Launching Optimized ID Agents...")
        
        # Authentication credentials
        auth_credentials = [
            ("dr_smith", "idweek2025"),
            ("id_fellow", "hello"), 
            ("pharmacist", "stewardship"),
            ("ipc_nurse", "infection"),
            ("researcher", "research"),
            ("educator", "education"),
            ("student", "learning"),
            ("admin", "idagents2025"),
            ("guest1", "guest123"),
            ("guest2", "guest456")
        ]
        
        auth_message = """
🦠 **ID Agents Beta Testing - Optimized Version**

Welcome to the memory-optimized ID Agents environment!

**Available Test Accounts:**
• **dr_smith** / idweek2025 (ID Physician)
• **id_fellow** / hello (ID Fellow) 
• **pharmacist** / stewardship (Clinical Pharmacist)
• **ipc_nurse** / infection (IPC Coordinator)
• **researcher** / research (Clinical Researcher)
• **educator** / education (Medical Educator)
• **student** / learning (Medical Student)
• **admin** / idagents2025 (Administrator)
• **guest1** / guest123 (Guest Access)
• **guest2** / guest456 (Guest Access)

This optimized version provides core agent building functionality 
while staying within HF Spaces memory limits.
        """
        
        # Configure launch
        try:
            from hf_config import configure_hf_environment, get_hf_launch_config
            if configure_hf_environment():
                launch_config = get_hf_launch_config()
                launch_config["auth"] = auth_credentials
                launch_config["auth_message"] = auth_message
                print("🔐 HF Spaces optimized deployment")
            else:
                launch_config = {
                    "auth": auth_credentials,
                    "auth_message": auth_message,
                    "share": False,
                    "server_name": "127.0.0.1",
                    "server_port": 7860
                }
                print("🔐 Local optimized deployment")
        except ImportError:
            launch_config = {
                "auth": auth_credentials,
                "auth_message": auth_message,
                "share": False,
                "server_name": "127.0.0.1", 
                "server_port": 7860
            }
            print("🔐 Fallback optimized deployment")
        
        # Launch optimized app
        build_optimized_ui().launch(**launch_config)
        
    except Exception as e:
        logger.error(f"Failed to launch optimized ID Agents: {e}")
        print(f"❌ Launch failed: {e}")
        import traceback
        traceback.print_exc()