# pylint: disable=line-too-long,missing-module-docstring,missing-class-docstring,missing-function-docstring,broad-exception-caught, unused-variable, too-many-statements,too-many-return-statements,too-many-locals,redefined-builtin,unused-import
# ruff: noqa: F401

import os
import typing
from dataclasses import dataclass, field

import pandas as pd
import requests
import rich
import smolagents
from get_model import get_model
from loguru import logger
from smolagents import CodeAgent, DuckDuckGoSearchTool, FinalAnswerTool, HfApiModel, VisitWebpageTool

print = rich.get_console().print
DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
SPACE_ID = os.getenv("SPACE_ID", "mikeee/final-assignment")

AUTHORIZED_IMPORTS = [
    "requests",
    "zipfile",
    "pandas",
    "numpy",
    "sympy",
    "json",
    "bs4",
    "pubchempy",
    "xml",
    "yahoo_finance",
    "Bio",
    "sklearn",
    "scipy",
    "pydub",
    "PIL",
    "chess",
    "PyPDF2",
    "pptx",
    "torch",
    "datetime",
    "fractions",
    "csv",
    "io",
    "glob",
]


@dataclass
class BasicAgent:
    model: smolagents.models.Model = HfApiModel()
    tools: list = field(default_factory=lambda: [])
    # def __init__(self):
    def __post_init__(self):
        """Run post_init."""
        logger.debug("BasicAgent initialized.")
        self.agent = CodeAgent(
            tools=self.tools,
            model=self.model,
            verbosity_level=3,
            additional_authorized_imports=AUTHORIZED_IMPORTS,
            planning_interval=4,
        )

    def get_answer(self, question: str):
        return f"ans to {question[:220]}..."

    def __call__(self, question: str) -> str:
        # print(f"Agent received question (first 50 chars): {question[:50]}...")
        print(f"Agent received question: {question}...")

        # fixed_answer = "This is a default answer."
        # print(f"Agent returning fixed answer: {fixed_answer}")
        # return fixed_answer
        try:
            # answer = self.get_answer(question)
            answer = self.agent(question)
        except Exception as e:
            logger.error(e)
            answer = str(e)[:10] + "..."

        return answer


def main():
    api_url = DEFAULT_API_URL
    questions_url = f"{api_url}/questions"
    submit_url = f"{api_url}/submit"  # noqa

    # username = "mikeee"
    # repo_name = "final-assignment"

    username, _, repo_name = SPACE_ID.partition("/")

    space_id = f"{username}/{repo_name}"

    model = get_model(cat="gemini")
    # 1. Instantiate Agent ( modify this part to create your agent)
    try:
        # agent = BasicAgent()
        agent = BasicAgent(
            model=model,
            tools=[
                DuckDuckGoSearchTool(),
                VisitWebpageTool(),
                FinalAnswerTool(),
            ]
        )
        agent.agent.visualize()
    except Exception as e:
        print(f"Error instantiating agent: {e}")
        return f"Error initializing agent: {e}", None

    # In the case of an app running as a hugging Face space, this link points toward your codebase ( usefull for others so please keep it public)
    agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"
    print(agent_code)

    # 2. Fetch Questions
    print(f"Fetching questions from: {questions_url}")
    try:
        response = requests.get(questions_url, timeout=30)
        response.raise_for_status()
        questions_data = response.json()
        if not questions_data:
            print("Fetched questions list is empty.")
            return "Fetched questions list is empty or invalid format.", None
        print(f"Fetched {len(questions_data)} questions.")
    except requests.exceptions.JSONDecodeError as e:
        print(f"Error decoding JSON response from questions endpoint: {e}")
        print(f"Response text: {response.text[:500]}")
        return f"Error decoding server response for questions: {e}", None
    except requests.exceptions.RequestException as e:
        print(f"Error fetching questions: {e}")
        return f"Error fetching questions: {e}", None
    except Exception as e:
        print(f"An unexpected error occurred fetching questions: {e}")
        return f"An unexpected error occurred fetching questions: {e}", None

    # 3. Run your Agent
    results_log = []
    answers_payload = []

    print(f"Running agent on {len(questions_data)} questions...")

    # for item in questions_data:
    for item in questions_data[-1:]:
        task_id = item.get("task_id")
        question_text = item.get("question")
        if not task_id or question_text is None:
            print(f"Skipping item with missing task_id or question: {item}")
            continue
        try:
            submitted_answer = agent(question_text)
            answers_payload.append({"task_id": task_id, "submitted_answer": submitted_answer})
            results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": submitted_answer})
        except Exception as e:
            print(f"Error running agent on task {task_id}: {e}")
            results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": f"AGENT ERROR: {e}"})

    if not answers_payload:
        print("Agent did not produce any answers to submit.")
        return "Agent did not produce any answers to submit.", pd.DataFrame(results_log)

    # 4. Prepare Submission
    submission_data = {"username": username.strip(), "agent_code": agent_code, "answers": answers_payload}  # noqa
    status_update = f"Agent finished. Submitting {len(answers_payload)} answers for user '{username}'..."
    print(status_update)
    print(answers_payload)

    agent.agent.visualize()
    return None, None

if __name__ == "__main__":
    main()