import logging
import os

import requests
import yaml
from dotenv import find_dotenv, load_dotenv
from litellm._logging import _disable_debugging
from openinference.instrumentation.smolagents import SmolagentsInstrumentor
from phoenix.otel import register

# from smolagents import CodeAgent, LiteLLMModel, LiteLLMRouterModel
from smolagents import CodeAgent, LiteLLMModel
from smolagents.monitoring import LogLevel

from tools.smart_search.tool import SmartSearchTool
from utils import extract_final_answer

_disable_debugging()

# Configure OpenTelemetry with Phoenix
register()
SmolagentsInstrumentor().instrument()

logging.basicConfig(
    level=logging.INFO, format="%(asctime)s - %(name)s - %(levelname)s - %(message)s"
)
logger = logging.getLogger(__name__)

load_dotenv(find_dotenv())

API_BASE = os.getenv("API_BASE")
API_KEY = os.getenv("API_KEY")
MODEL_ID = os.getenv("MODEL_ID")

model = LiteLLMModel(
    api_base=API_BASE,
    api_key=API_KEY,
    model_id=MODEL_ID,
)

# data_agent = create_data_agent(model)
# media_agent = create_media_agent(model)
# web_agent = create_web_agent(model)

# search_agent = ToolCallingAgent(
#     tools=[DuckDuckGoSearchTool(), VisitWebpageTool()],
#     model=model,
#     name="search_agent",
#     description="This is an agent that can do web search.",
# )

prompt_templates = yaml.safe_load(open("prompts/code_agent_modified.yaml", "r"))

agent = CodeAgent(
    # add_base_tools=True,
    # additional_authorized_imports=[
    #     "json",
    #     "pandas",
    #     "numpy",
    #     "re",
    #     # "requests"
    #     # "urllib.request",
    # ],
    # max_steps=10,
    # managed_agents=[web_agent, data_agent, media_agent],
    # managed_agents=[search_agent],
    model=model,
    prompt_templates=prompt_templates,
    tools=[
        SmartSearchTool(),
        # VisitWebpageTool(max_output_length=1024),
    ],
    step_callbacks=None,
    verbosity_level=LogLevel.ERROR,
)

agent.visualize()


def main(task: str):
    # Format the task with GAIA-style instructions
#     gaia_task = f"""Instructions:
# 1. Your response must contain ONLY the answer to the question, nothing else
# 2. Do not repeat the question or any part of it
# 3. Do not include any explanations, reasoning, or context
# 4. Do not include source attribution or references
# 5. Do not use phrases like "The answer is" or "I found that"
# 6. Do not include any formatting, bullet points, or line breaks
# 7. If the answer is a number, return only the number
# 8. If the answer requires multiple items, separate them with commas
# 9. If the answer requires ordering, maintain the specified order
# 10. Use the most direct and succinct form possible

# {task}"""

    result = agent.run(
        additional_args=None,
        images=None,
        max_steps=3,
        reset=True,
        stream=False,
        task=task,
        # task=gaia_task,
    )

    logger.info(f"Result: {result}")

    return extract_final_answer(result)


if __name__ == "__main__":
    DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"

    api_url = DEFAULT_API_URL
    questions_url = f"{api_url}/questions"
    submit_url = f"{api_url}/submit"

    response = requests.get(questions_url, timeout=15)
    response.raise_for_status()
    questions_data = response.json()

    for question_data in questions_data[:1]:
        file_name = question_data["file_name"]
        level = question_data["Level"]
        question = question_data["question"]
        task_id = question_data["task_id"]

        logger.info(f"Question: {question}")
        # logger.info(f"Level: {level}")
        if file_name:
            logger.info(f"File Name: {file_name}")
        # logger.info(f"Task ID: {task_id}")

        final_answer = main(question)
        logger.info(f"Final Answer: {final_answer}")
        logger.info("--------------------------------")