from langchain_core.utils.function_calling import convert_to_openai_function
from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder
from langchain.memory import ConversationBufferWindowMemory
from langchain.schema.runnable import RunnablePassthrough
from langchain.agents.format_scratchpad import format_to_openai_functions
from langchain.agents.output_parsers import OpenAIFunctionsAgentOutputParser
from langchain.agents import AgentExecutor
from langchain_groq import ChatGroq
from functions import book_slot , check_slots , suggest_specialization , reschedule_event,delete_event

def create_agent(general_prompt_template):
    # print("get user Id**********************",user_id)
    
    API_KEY = "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"
    tools = [book_slot , delete_event , check_slots , suggest_specialization , reschedule_event]
    functions = [convert_to_openai_function(f) for f in tools]

    llm = ChatGroq(
    model="llama-3.1-70b-versatile",
    temperature=0,
    max_tokens=None,
    timeout=None,
    max_retries=2,
    api_key=API_KEY
        ).bind_functions(functions=functions)
 
    prompt = ChatPromptTemplate.from_messages([("system", general_prompt_template),
                                               MessagesPlaceholder(variable_name="chat_history"), ("user", "{input}"),
                                               MessagesPlaceholder(variable_name="agent_scratchpad")])



    memory = ConversationBufferWindowMemory(memory_key="chat_history" , return_messages=True, k=5)

    chain = RunnablePassthrough.assign(agent_scratchpad=lambda x: format_to_openai_functions(x["intermediate_steps"])) | prompt | llm | OpenAIFunctionsAgentOutputParser()

    agent_executor = AgentExecutor(
        agent=chain, tools=tools, memory=memory, verbose=True)

    return agent_executor
# give me available slots at 17 september 2024 , book schedule at 10:30 AM 17 september 2024 ,