"""
SpeechGraph Module
"""

from typing import Optional, Type

from pydantic import BaseModel

from ..models import OpenAITextToSpeech
from ..nodes import FetchNode, GenerateAnswerNode, ParseNode, TextToSpeechNode
from ..utils.logging import get_logger
from ..utils.save_audio_from_bytes import save_audio_from_bytes
from .abstract_graph import AbstractGraph
from .base_graph import BaseGraph

logger = get_logger(__name__)


class SpeechGraph(AbstractGraph):
    """
    SpeechyGraph is a scraping pipeline that scrapes the web, provide an answer
    to a given prompt, and generate an audio file.

    Attributes:
        prompt (str): The prompt for the graph.
        source (str): The source of the graph.
        config (dict): Configuration parameters for the graph.
        schema (BaseModel): The schema for the graph output.
        llm_model: An instance of a language model client, configured for generating answers.
        embedder_model: An instance of an embedding model clienta
                        configured for generating embeddings.
        verbose (bool): A flag indicating whether to show print statements during execution.
        headless (bool): A flag indicating whether to run the graph in headless mode.
        model_token (int): The token limit for the language model.

    Args:
        prompt (str): The prompt for the graph.
        source (str): The source of the graph.
        config (dict): Configuration parameters for the graph.
        schema (BaseModel): The schema for the graph output.

    Example:
        >>> speech_graph = SpeechGraph(
        ...     "List me all the attractions in Chioggia and generate an audio summary.",
        ...     "https://en.wikipedia.org/wiki/Chioggia",
        ...     {"llm": {"model": "openai/gpt-3.5-turbo"}}
    """

    def __init__(
        self,
        prompt: str,
        source: str,
        config: dict,
        schema: Optional[Type[BaseModel]] = None,
    ):
        super().__init__(prompt, config, source, schema)

        self.input_key = "url" if source.startswith("http") else "local_dir"

    def _create_graph(self) -> BaseGraph:
        """
        Creates the graph of nodes representing the workflow for web scraping and audio generation.

        Returns:
            BaseGraph: A graph instance representing the web scraping and audio generation workflow.
        """

        fetch_node = FetchNode(input="url | local_dir", output=["doc"])

        parse_node = ParseNode(
            input="doc",
            output=["parsed_doc"],
            node_config={"chunk_size": self.model_token, "llm_model": self.llm_model},
        )

        generate_answer_node = GenerateAnswerNode(
            input="user_prompt & (relevant_chunks | parsed_doc | doc)",
            output=["answer"],
            node_config={
                "llm_model": self.llm_model,
                "additional_info": self.config.get("additional_info"),
                "schema": self.schema,
            },
        )

        text_to_speech_node = TextToSpeechNode(
            input="answer",
            output=["audio"],
            node_config={"tts_model": OpenAITextToSpeech(self.config["tts_model"])},
        )

        return BaseGraph(
            nodes=[fetch_node, parse_node, generate_answer_node, text_to_speech_node],
            edges=[
                (fetch_node, parse_node),
                (parse_node, generate_answer_node),
                (generate_answer_node, text_to_speech_node),
            ],
            entry_point=fetch_node,
            graph_name=self.__class__.__name__,
        )

    def run(self) -> str:
        """
        Executes the scraping process and returns the answer to the prompt.

        Returns:
            str: The answer to the prompt.
        """

        inputs = {"user_prompt": self.prompt, self.input_key: self.source}
        self.final_state, self.execution_info = self.graph.execute(inputs)

        audio = self.final_state.get("audio", None)
        if not audio:
            raise ValueError("No audio generated from the text.")
        save_audio_from_bytes(audio, self.config.get("output_path", "output.mp3"))
        logger.info("Audio saved to %s", self.config.get("output_path", "output.mp3"))

        return self.final_state.get("answer", "No answer found.")