"""Custom tools go in here"""

from google import genai
from se_agents.tools import Tool
from se_agents.agent import Agent
from se_agents.runner import Runner
from openai import OpenAI
import types
import requests
from config import Config
from langchain_community.document_loaders import (
    UnstructuredExcelLoader,
    TextLoader,
    PyPDFLoader,
)
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_openai import OpenAIEmbeddings
from langchain_community.vectorstores import Chroma
from openai import Client
from pathlib import Path
from utils import read_file_content
from google.genai import types


class YoutubeVideoInterpreter(Tool):
    def __init__(self):
        super().__init__(
            name="youtube_video_interpreter",
            description="Given a certain youtube video url, it analyzes the video and response any question asked by the user",
            parameters={
                "query": {
                    "type": "string",
                    "description": "question about the video",
                    "required": True,
                },
                "video_url": {
                    "type": "string",
                    "description": "the url of the video",
                    "required": True,
                },
            },
        )

    def execute(self, **kwargs) -> str:
        params = self._process_parameters(**kwargs)
        query = params.get("query")
        video_url = params.get("video_url")
        client = genai.Client(api_key=Config.get_gemini_api_key())
        response = client.models.generate_content(
            model="models/gemini-2.0-flash",
            contents=types.Content(
                parts=[
                    types.Part(file_data=types.FileData(file_uri=video_url)),
                    types.Part(text=query),
                ]
            ),
        )
        return response.text


class TaskFileDownloader(Tool):
    def __init__(self):
        super().__init__(
            name="task_file_downloader",
            description="Given a certain Taks id, it downloads the complementary file, outputs the path of the file",
            parameters={
                "task_id": {
                    "type": "string",
                    "description": "the id of the task",
                    "required": True,
                },
                "complementary_file": {
                    "type": "string",
                    "description": "the name with extension of the file",
                    "required": True,
                },
            },
        )

    def execute(self, **kwargs) -> str:
        params = self._process_parameters(**kwargs)
        task_id = params.get("task_id")
        complementary_file_ext = params.get("complementary_file").split(".")[-1]
        response = requests.get(f"{Config.get_default_api_url()}/files/{task_id}")
        # Verify the request was successful
        if response.status_code == 200:
            # Save the file
            with open(
                f"{Config.get_task_file_folder()}/{task_id}_complementary_file.{complementary_file_ext}",
                "wb",
            ) as file:
                file.write(response.content)
            return f"{Config.get_task_file_folder()}/{task_id}_complementary_file.{complementary_file_ext}"
        else:
            return f"Failed to retrieve file. Status code: {response.status_code}"


class RetriveInfoTaskFile(Tool):

    def __init__(self):
        super().__init__(
            name="retrive_info_task_file",
            description="Given a certain Taks file path, outputs info related to the file",
            parameters={
                "complementary_file": {
                    "type": "string",
                    "description": "the path with extension of the file",
                    "required": True,
                },
                "query": {
                    "type": "string",
                    "description": "question about the file",
                    "required": True,
                },
            },
        )
        self.retriver = {
            ".xlsx": UnstructuredExcelLoader,
            ".pdf": PyPDFLoader,
            ".txt": TextLoader,
        }

    def execute(self, **kwargs) -> str:
        params = self._process_parameters(**kwargs)
        complementary_file = params.get("complementary_file")
        query = params.get("query")
        complementary_file_path = Path(complementary_file)

        # Validate file format
        if complementary_file_path.suffix not in self.retriver:
            return f"Unsupported file format: {complementary_file_path.suffix}"

        # Load and process the document
        loader = self.retriver[complementary_file_path.suffix](complementary_file_path)
        documents = loader.load()
        text_splitter = RecursiveCharacterTextSplitter(
            chunk_size=1000, chunk_overlap=100
        )
        docs = text_splitter.split_documents(documents)

        # Embed and retrieve relevant information
        embeddings = OpenAIEmbeddings()
        vectorstore = Chroma.from_documents(docs, embeddings)
        retriever = vectorstore.as_retriever()
        results = retriever.invoke(query)

        # Return formatted results or a fallback message
        return (
            "\n\n".join([doc.page_content for doc in results[:3]])
            if results
            else "No relevant information found."
        )


class SpeechToText(Tool):
    def __init__(self):
        super().__init__(
            name="speech_to_text",
            description="Given a certain audio file path, outputs the path to a .txt file with the transcription",
            parameters={
                "audio_file_path": {
                    "type": "string",
                    "description": "the name with extension of the file",
                    "required": True,
                },
            },
        )

    async def execute(self, **kwargs) -> str:
        params = self._process_parameters(**kwargs)
        audio_file_path = params.get("audio_file_path")
        client = OpenAI(api_key=Config.get_openai_api_key())
        if Path(audio_file_path).exists():
            with open(audio_file_path, "rb") as audio_file:
                transcription = client.audio.transcriptions.create(
                    model="gpt-4o-transcribe", file=audio_file
                )

                # Write transcription to a .txt file
                txt_file_path = Path(audio_file_path).with_suffix(".txt")
                with open(txt_file_path, "w") as txt_file:
                    txt_file.write(transcription.text)

                return f"Transcription saved to {txt_file_path}"
        else:
            return "Audio file does not exist"


class CodeInterpreter(Tool):
    def __init__(self):
        super().__init__(
            name="code_interpreter",
            description="Given a certain code file path, outputs the content of the file",
            parameters={
                "code_file_path": {
                    "type": "string",
                    "description": "the name with extension of the file",
                    "required": True,
                },
            },
        )

    async def execute(self, **kwargs) -> str:
        params = self._process_parameters(**kwargs)
        code_file_path = params.get("code_file_path")
        client = OpenAI(api_key=Config.get_openai_api_key())
        if Path(code_file_path).exists():
            content = read_file_content(code_file_path)
            return content
        else:
            return "Code file does not exist"