#import os

#from langchain.agents import load_tools
#from openai import OpenAI
#from openinference.instrumentation.smolagents import SmolagentsInstrumentor
#from phoenix.otel import register
#from smolagents import (
#    CodeAgent,
#    ToolCallingAgent,
#    OpenAIServerModel,
#    Tool,
#    DuckDuckGoSearchTool,
#    WikipediaSearchTool
#)
#from tools import VisitWebpageTool

###
from crewai import Agent
from langchain_openai import ChatOpenAI
from tools import scrape_tool, search_tool, today_tool
###

#MODEL_ID_1 = "gpt-4o-mini"
#MODEL_ID_2 = "gpt-4o"
#MODEL_ID_3 = "o4-mini"

#PHOENIX_PROJECT_NAME = "gaia"

#os.environ["OTEL_EXPORTER_OTLP_HEADERS"] = "api_key = " + os.environ["PHOENIX_API_KEY"];
#os.environ["PHOENIX_CLIENT_HEADERS"] = "api_key = " + os.environ["PHOENIX_API_KEY"];
#os.environ["PHOENIX_COLLECTOR_ENDPOINT"] = "https://app.phoenix.arize.com";

#tracer_provider = register(
#    auto_instrument = True,
#    endpoint = "https://app.phoenix.arize.com/v1/traces",
#    project_name = PHOENIX_PROJECT_NAME
#)

#SmolagentsInstrumentor().instrument(tracer_provider = tracer_provider)

#def run_gaia(question, file_name):
    #search_tool = Tool.from_langchain(load_tools(["serpapi"])[0])
    #wikipedia_tool = Tool.from_langchain(load_tools(["wikipedia"])[0])
    
    #web_search_agent = ToolCallingAgent(
    #    description = "Runs web searches for you. Give it your query as an argument.",
    #    max_steps = 2,
    #    model = OpenAIServerModel(model_id = MODEL_ID_1),
    #    name = "web_search_agent",
    #    tools = [search_tool, VisitWebpageTool()],
    #    #tools = [DuckDuckGoSearchTool(), VisitWebpageTool()],
    #    verbosity_level = 1
    #)
    
    #wikipedia_search_agent = ToolCallingAgent(
    #    description = "Runs wikipedia searches for you. Give it your query as an argument.",
    #    max_steps = 2,
    #    model = OpenAIServerModel(model_id = MODEL_ID_1),
    #    name = "wikipedia_search_agent",
    #    tools = [wikipedia_tool],
    #    #tools = [WikipediaSearchTool()],
    #    verbosity_level = 1
    #)

    #manager_agent = CodeAgent(
    #    #add_base_tools = True,
    #    #additional_authorized_imports = ["json", "numpy", "pandas", "time"],
    #    #final_answer_checks = [get_final_answer],
    #    #managed_agents = [web_search_agent],
    #    #max_steps = 5,
    #    model = OpenAIServerModel(model_id = MODEL_ID_2),
    #    planning_interval=3,
    #    tools = [DuckDuckGoSearchTool(), VisitWebpageTool(), WikipediaSearchTool()],
    #    verbosity_level = 1
    #)

    #return manager_agent.run(question)

    #answer = manager_agent.run(question)

    #return get_final_answer(question, answer)
    
#def get_final_answer(question, answer):
#    prompt_template = """
#        You are an expert in precise question answering. You are given a question and context. You must **precisely** answer the question based on the context and then stop.
#        **Question:** """ + str(question) + """
#        **Context:** """ + str(answer) + """
#        **Example 1:** What is the capital of France? Paris
#        **Example 2:** What is the superlative of good? Best
#        **Example 3:** What is the opposite of left? Right
#        **Answer:**: 
#        """

#    client = OpenAI()
#    completion = client.chat.completions.create(
#        messages = [{"role": "user", "content": [{"type": "text", "text": prompt_template}]}],
#        model = MODEL_ID_1
#    )

#    final_answer = completion.choices[0].message.content
    
#    print(f"Question: {question}")
#    print(f"Answer: {answer}")
#    print(f"Final answer: {final_answer}")
    
#    return final_answer

###
def get_researcher_agent(model, verbose):
    return Agent(
        role="Researcher",
        goal="Research content on topic: {topic}.",
        backstory="You're working on researching content on topic: {topic}. "
                  "Your work is the basis for the Writer to write on this topic.",
        llm=ChatOpenAI(model=model),
        tools = [search_tool(), scrape_tool()],
        allow_delegation=False,
    	verbose=verbose
    )

def get_writer_agent(model, verbose):
    return Agent(
        role="Writer",
        goal="Write an article on topic: {topic}.",
        backstory="You're working on writing an article on topic: {topic}. "
                  "You base your writing on the work of the Researcher, who provides context on this topic.",
        llm=ChatOpenAI(model=model),
        tools = [today_tool()],
        allow_delegation=False,
        verbose=verbose
    )
###