import os
from typing import List, Tuple

from langchain.agents import AgentExecutor
from langchain.agents.format_scratchpad import format_to_openai_function_messages
from langchain.agents.output_parsers import OpenAIFunctionsAgentOutputParser
from langchain.tools.render import format_tool_to_openai_function
from langchain_community.chat_models import ChatOpenAI
from langchain_community.tools.gmail import (
    GmailCreateDraft,
    GmailGetMessage,
    GmailGetThread,
    GmailSearch,
    GmailSendMessage,
)
from langchain_community.tools.gmail.utils import build_resource_service
from langchain_community.utilities.tavily_search import TavilySearchAPIWrapper
from langchain_core.messages import AIMessage, HumanMessage
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
from langchain_core.pydantic_v1 import BaseModel, Field
from langchain_core.tools import tool


@tool
def search_engine(query: str, max_results: int = 5) -> str:
    """"A search engine optimized for comprehensive, accurate, \
and trusted results. Useful for when you need to answer questions \
about current events or about recent information. \
Input should be a search query. \
If the user is asking about something that you don't know about, \
you should probably use this tool to see if that can provide any information."""
    return TavilySearchAPIWrapper().results(query, max_results=max_results)


# Create the tools
tools = [
    GmailCreateDraft(),
    GmailGetMessage(),
    GmailGetThread(),
    GmailSearch(),
    search_engine,
]
if os.environ.get("GMAIL_AGENT_ENABLE_SEND") == "true":
    tools.append(GmailSendMessage())
current_user = (
    build_resource_service().users().getProfile(userId="me").execute()["emailAddress"]
)
assistant_system_message = """You are a helpful assistant aiding a user with their \
emails. Use tools (only if necessary) to best answer \
the users questions.\n\nCurrent user: {user}"""
prompt = ChatPromptTemplate.from_messages(
    [
        ("system", assistant_system_message),
        MessagesPlaceholder(variable_name="chat_history"),
        ("user", "{input}"),
        MessagesPlaceholder(variable_name="agent_scratchpad"),
    ]
).partial(user=current_user)


llm = ChatOpenAI(model="gpt-4-1106-preview", temperature=0)
llm_with_tools = llm.bind(functions=[format_tool_to_openai_function(t) for t in tools])


def _format_chat_history(chat_history: List[Tuple[str, str]]):
    buffer = []
    for human, ai in chat_history:
        buffer.append(HumanMessage(content=human))
        buffer.append(AIMessage(content=ai))
    return buffer


agent = (
    {
        "input": lambda x: x["input"],
        "chat_history": lambda x: _format_chat_history(x["chat_history"]),
        "agent_scratchpad": lambda x: format_to_openai_function_messages(
            x["intermediate_steps"]
        ),
    }
    | prompt
    | llm_with_tools
    | OpenAIFunctionsAgentOutputParser()
)


class AgentInput(BaseModel):
    input: str
    chat_history: List[Tuple[str, str]] = Field(
        ..., extra={"widget": {"type": "chat", "input": "input", "output": "output"}}
    )


agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True).with_types(
    input_type=AgentInput
)