import os
from line.llm_agent import LlmAgent, LlmConfig, end_call
from line.voice_agent_app import VoiceAgentApp

async def get_agent(env, call_request):
    return LlmAgent(
        model="gemini/gemini-2.5-flash-preview-09-2025",
        api_key=os.getenv("GEMINI_API_KEY"),
        tools=[end_call],
        config=LlmConfig(
            system_prompt="You are a helpful voice assistant.",
            introduction="Hello! How can I help you today?",
        ),
    )

app = VoiceAgentApp(get_agent=get_agent)

if __name__ == "__main__":
    app.run()

config = LlmConfig(
    system_prompt="You are a customer service agent for Acme Corp. Be friendly and concise.",
    introduction="Hi! Thanks for calling Acme. How can I help?",
)

async def get_agent(env: AgentEnv, call_request: CallRequest):
    # Prompts come from call_request.agent.system_prompt and call_request.agent.introduction
    # Falls back to your defaults if not provided
    return LlmAgent(
        model="gemini/gemini-2.5-flash-preview-09-2025",
        tools=[end_call],
        config=LlmConfig.from_call_request(
            call_request,
            fallback_system_prompt="You are a helpful assistant.",
            fallback_introduction="Hello! How can I help?",
        ),
    )

from line.llm_agent import LlmAgent, LlmConfig, end_call, knowledge_base, send_dtmf, transfer_call, voicemail, web_search

agent = LlmAgent(
    model="gemini/gemini-2.5-flash-preview-09-2025",
    tools=[end_call, send_dtmf, transfer_call, voicemail, web_search, knowledge_base],
    config=LlmConfig(...),
)

from line.llm_agent import LlmAgent, LlmConfig, end_call, voicemail

agent = LlmAgent(
    model="anthropic/claude-haiku-4-5-20251001",
    api_key=os.getenv("ANTHROPIC_API_KEY"),
    tools=[voicemail(message="Hi, please call us back when you can."), end_call],
    config=LlmConfig(system_prompt=SYSTEM_PROMPT, introduction=""),  # outbound: wait for the callee
)

voicemail                                  # silently end the call on a voicemail
voicemail(message="Sorry we missed you.")  # speak the message (uninterruptible), then end
voicemail(interruptible=True)              # allow the message/hangup to be interrupted
voicemail(description="…")                 # override the LLM-facing "when to call this" text

from line.llm_agent import http_server_tool

create_ticket = http_server_tool(
    name="create_ticket",
    description="Creates a support ticket for the caller.",
    url="https://api.example.com/v1/{tenant_id}/tickets",
    method="POST",
    request_body_schema={
        "type": "object",
        "required": ["subject", "priority"],
        "properties": {
            "subject": {"type": "string", "description": "Short summary of the issue."},
            "priority": {"type": "string", "enum": ["low", "medium", "high"]},
            # constant_value: hidden from the LLM, baked into every request
            "source": {"type": "string", "constant_value": "voice_agent"},
        },
    },
    # ${ENV_VAR} resolved from os.environ at build time
    auth={"Authorization": "Bearer ${SUPPORT_API_KEY}"},
)

agent = LlmAgent(tools=[create_ticket, end_call], ...)

search_orders = http_server_tool(
    name="search_orders",
    description="Search orders by status.",
    url="https://api.example.com/orders",
    method="GET",
    query_params_schema={
        "type": "object",
        "required": ["status"],
        "properties": {
            "status": {"type": "string", "enum": ["pending", "shipped", "delivered"]},
            "api_key": {"type": "string", "constant_value": "pk_live_abc123"},
        },
    },
)

from typing import Annotated
from line.llm_agent import loopback_tool

@loopback_tool
async def get_order_status(ctx, order_id: Annotated[str, "The order ID"]) -> str:
    """Look up order status."""
    order = await db.get_order(order_id)
    return f"Order {order_id}: {order.status}"

agent = LlmAgent(tools=[get_order_status, end_call], ...)

from typing import Annotated
from line.events import AgentSendText, AgentTransferCall
from line.llm_agent import passthrough_tool

@passthrough_tool
async def transfer_to_support(ctx, reason: Annotated[str, "Why they need support"]):
    """Transfer to support team."""
    yield AgentSendText(text="Transferring you to support now.")
    yield AgentTransferCall(target_phone_number="+18005551234")

agent = LlmAgent(tools=[transfer_to_support, end_call], ...)

from line.llm_agent import LlmAgent, LlmConfig, agent_as_handoff, end_call

spanish_agent = LlmAgent(
    model="anthropic/claude-sonnet-4-5",
    tools=[end_call],
    config=LlmConfig(
        system_prompt="You speak only in Spanish.",
        introduction="¡Hola! ¿Cómo puedo ayudarte?",
    ),
)

main_agent = LlmAgent(
    model="gemini/gemini-2.5-flash-preview-09-2025",
    tools=[
        end_call,
        agent_as_handoff(
            spanish_agent,
            handoff_message="Transferring you to our Spanish-speaking agent...",
            name="transfer_to_spanish",
            description="Transfer when user wants to speak Spanish.",
        ),
    ],
    config=LlmConfig(system_prompt="Transfer to Spanish if requested."),
)

from typing import Annotated
from line.llm_agent import loopback_tool

@loopback_tool(is_background=True)
async def search_database(ctx, query: Annotated[str, "Search query"]) -> str:
    """Search that may take a while."""
    results = await slow_database_search(query)
    return results

from line.llm_agent import LlmAgent, LlmConfig, loopback_tool

agent = LlmAgent(
    model="gemini/gemini-2.5-flash-preview-09-2025",
    api_key=os.getenv("GEMINI_API_KEY"),
    config=LlmConfig(system_prompt="You are a helpful assistant."),
)

# Inject context before the conversation starts
agent.history.add_entry("The customer's name is Alice and she has a premium account.")

# Or inject context from within a tool call
@loopback_tool
async def lookup_customer(ctx, customer_id: str) -> str:
    """Look up customer details."""
    customer = await db.get_customer(customer_id)
    # Inject rich context that persists across turns
    agent.history.add_entry(f"Customer profile: {customer.summary}")
    return f"Found customer {customer.name}"

from line import CustomHistoryEntry

# Prefix the history with a reminder (neither anchor)
agent.history.update([CustomHistoryEntry(content="Remember: be concise and friendly.")])

# Replace everything from `marker` onward (start only)
agent.history.update(
    [CustomHistoryEntry(content="Conversation summarized.")],
    start=marker,
)

# Replace the inclusive segment between two known events (both anchors)
agent.history.update(
    [CustomHistoryEntry(content="(redacted)")],
    start=first_event,
    end=last_event,
)

from line.agent import TurnEnv
from line.events import InputEvent, OutputEvent, UserTurnEnded, AgentSendText
from line.llm_agent import LlmAgent, LlmConfig, end_call

class GuardedAgent:
    def __init__(self, inner_agent):
        self.inner = inner_agent
        self.blocked_words = ["competitor", "confidential"]

    async def process(self, env: TurnEnv, event: InputEvent):
        # Pre-process: check user input for blocked words
        if isinstance(event, UserTurnEnded):
            user_text = " ".join(
                item.content for item in event.content if hasattr(item, "content")
            )
            if any(word in user_text.lower() for word in self.blocked_words):
                yield AgentSendText(text="I can't discuss that topic.")
                return

        # Delegate to inner agent
        async for output in self.inner.process(env, event):
            yield output

async def get_agent(env, call_request):
    inner = LlmAgent(
        model="gemini/gemini-2.5-flash-preview-09-2025",
        tools=[end_call],
        config=LlmConfig(system_prompt="You are a helpful assistant."),
    )
    return GuardedAgent(inner)