# Utility: Get installed Ollama LLMs as a list (for UI)
def get_installed_llms():
    """Returns a list of locally installed Ollama LLMs (model names)."""
    import subprocess
    try:
        result = subprocess.run(["ollama", "list"], capture_output=True, text=True)
        lines = result.stdout.splitlines()
        models = []
        for line in lines:
            if line.strip() and not line.startswith("NAME"):
                name = line.split()[0]
                models.append(name)
        return models
    except Exception as e:
        print(f"Error listing local LLMs: {e}")
        return []

# Utility: Display installed Ollama LLMs in terminal (for CLI use)
def display_installed_llms():
    """Prints a list of locally installed Ollama LLMs to the terminal."""
    models = get_installed_llms()
    if models:
        print("Available local LLMs:")
        for m in models:
            print(f"- {m}")
    else:
        print("No local LLMs found.")
"""
Local environment (Ollama) specific helpers for LLM operations.
Contains: Ollama/ChatOllama initialization, agent creation, and response generation.
"""

from helpers_SHARED import CONFIG, IS_HF_SPACE, AGENT_TOOLS

# ============================================================================
# OLLAMA LLM INITIALIZATION
# ============================================================================

ollama_llm = None
LLM_NAME = None

def init_ollama_llm(model_name=None):
    """Initialize Ollama (ChatOllama) for local LLM. Accepts a model name for dynamic selection."""
    global ollama_llm, LLM_NAME
    
    if IS_HF_SPACE:
        print("ℹ️ Running on HF Space, skipping Ollama initialization")
        return None, None
    
    from langchain_ollama import ChatOllama
    
    if model_name is None:
        model_name = CONFIG["ollama_model"]
    LLM_NAME = model_name
    try:
        ollama_llm = ChatOllama(model=model_name, base_url=CONFIG["ollama_base_url"])
        print(f"✓ Ollama (ChatOllama) initialized successfully with {LLM_NAME}")
        return ollama_llm, LLM_NAME
    except Exception as e:
        print(f"✗ Warning: Ollama not available: {e}")
        ollama_llm = None
        return None, LLM_NAME

# ============================================================================
# LANGCHAIN AGENT (LOCAL ONLY)
# ============================================================================

agent_executor = None

def create_langchain_agent():
    """Create a LangGraph ReAct agent with the available tools.
    
    Only works in local environment with Ollama.
    Returns None on HF Spaces.
    """
    global agent_executor, ollama_llm
    
    if IS_HF_SPACE:
        print("ℹ️ HF Space detected - using manual tool routing (HF InferenceClient doesn't support LangChain agents)")
        return None
    
    if ollama_llm is None:
        print("❌ Ollama LLM not initialized, cannot create agent")
        return None
    
    from langgraph.prebuilt import create_react_agent  # type: ignore
    
    try:
        agent_executor = create_react_agent(
            model=ollama_llm,
            tools=AGENT_TOOLS,
        )
        print("✅ LangGraph ReAct Agent created successfully with Ollama")
        return agent_executor
    except Exception as e:
        print(f"❌ Failed to create LangGraph agent: {e}")
        return None

# ============================================================================
# OLLAMA RESPONSE GENERATION
# ============================================================================

def ollama_generate_response(prompt: str, ollama_instance=None) -> str:
    """Generate a response using Ollama.
    
    Args:
        prompt: The prompt to send to the model
        ollama_instance: Optional Ollama instance, uses global if not provided
    
    Returns:
        Generated response string or None if failed
    """
    llm = ollama_instance or ollama_llm
    
    if llm is None:
        print("❌ Ollama not available")
        return None
    
    try:
        print(f"🧠 Generating response with Ollama ({LLM_NAME})...")
        response = llm.invoke(prompt)
        print(f"✓ Agent response generated.")
        return response
    except Exception as ollama_error:
        print(f"❌ Ollama error: {str(ollama_error)}")
        return None

def run_agent(query: str) -> str:
    """Run the LangGraph agent on a query.
    
    Args:
        query: User query to process
    
    Returns:
        Agent response string or None if agent unavailable
    """
    global agent_executor
    
    if agent_executor is None:
        return None
    
    try:
        print(f"🤖 Using LangGraph Agent to process: {query}")
        result = agent_executor.invoke({"messages": [{"role": "user", "content": query}]})
        # Extract the last AI message
        messages = result.get("messages", [])
        for msg in reversed(messages):
            if hasattr(msg, 'content') and msg.type == "ai":
                return msg.content
        return "No response from agent"
    except Exception as e:
        print(f"❌ Agent execution failed: {e}, falling back to manual routing")
        return None

def get_ollama_llm():
    """Get the Ollama LLM instance."""
    return ollama_llm

def get_local_llm_name():
    """Get the local LLM name."""
    return LLM_NAME

def get_agent_executor():
    """Get the agent executor instance."""
    return agent_executor