from io import StringIO
import csv
import json
import logging

import torch
from transformers import pipeline
from pydantic import BaseModel, ValidationError, validator

logger = logging.getLogger(__name__)

class Card(BaseModel):
    question: str
    answer: str

class Message(BaseModel):
    role: str
    content: list[Card]

    @validator('content', pre=True)
    def parse_content(cls, v):
        if isinstance(v, str):
            try:
                content_list = json.loads(v)
                return content_list
            except json.JSONDecodeError as e:
                raise ValueError(f"Error decoding 'content' JSON: {e}") from e
        return v

    def content_to_json(self) -> str:
        return json.dumps([card.dict() for card in self.content], indent=2)

    def content_to_csv(self) -> str:
        output = StringIO()
        writer = csv.writer(output)
        writer.writerow(['Question', 'Answer'])  # CSV Header
        for card in self.content:
            writer.writerow([card.question, card.answer])
        return output.getvalue()

class PydanticEncoder(json.JSONEncoder):
    def default(self, obj):
        if isinstance(obj, BaseModel):
            return obj.dict()
        return super().default(obj)

class Pipeline:
    def __init__(self, model_name: str = "Qwen/Qwen2.5-7B-Instruct"):
        self.torch_pipe = pipeline(
            "text-generation",
            "Qwen/Qwen2.5-7B-Instruct",
            torch_dtype="auto",
            device_map="auto"
        )
        self.device = self._determine_device()
        self.messages = [
            {"role": "system", "content": """You are an expert flashcard creator. You always include a single knowledge item per flashcard.
            - You ALWAYS include a single knowledge item per flashcard.
            - You ALWAYS respond in valid JSON format.

            Format responses like the example below.

            EXAMPLE:
            [
                {"question": "What is AI?", "answer": "Artificial Intelligence."},
                {"question": "What is ML?", "answer": "Machine Learning."}
            ]
            """},
        ]

    def extract_flashcards(self, content: str = "", max_new_tokens: int = 1024) -> str:
        user_prompt = {"role": "user", "content": content}
        self.messages.append(user_prompt)
        response_message = self.torch_pipe(
            self.messages,
            max_new_tokens=max_new_tokens
        )[0]["generated_text"][-1]
        return response_message

    def format_flashcards(self, output_format: str, response: str) -> str:
        output = ""
        try :
            message = parse_message(response)
            logger.debug("after parse_obj_as")
        except ValidationError as e:
            raise e
        if output_format.lower() == "json":
            output = message.content_to_json()
        elif output_format.lower() == "csv":
            output = message.content_to_csv()
        return output

    def generate_flashcards(self, output_format: str, content: str) -> str:
        response = self.extract_flashcards(content)
        return self.format_flashcards(output_format, response)

    def parse_message(self, input_dict: dict[str, any]) -> Message:
        try:
            # Extract the role
            role = input_dict['role']

            # Parse the content
            content = input_dict['content']
            # If content is a string, try to parse it as JSON
            if isinstance(content, str):
                content = content.strip()
                content = json.loads(content)

            # Create Card objects from the content
            cards = [Card(**item) for item in content]

            # Create and return the Message object
            return Message(role=role, content=cards)
        except json.JSONDecodeError as e:
            raise ValueError(f"Invalid JSON in content: {str(e)}")
        except ValidationError as e:
            raise ValueError(f"Validation error: {str(e)}")
        except KeyError as e:
            raise ValueError(f"Missing required key: {str(e)}")

    def _determine_device(self):
        if torch.cuda.is_available():
            return torch.device("cuda")
        elif torch.backends.mps.is_available():
            return torch.device("mps")
        else:
            return torch.device("cpu")

def parse_message(input_dict: dict[str, any]) -> Message:
    try:
        # Extract the role
        role: str = input_dict['role']

        # Parse the content
        content: str = input_dict['content']

        # If content is a string, try to parse it as JSON
        if isinstance(content, str):
            content = json.loads(content)

        # Create Card objects from the content
        cards = [Card(**item) for item in content]

        # Create and return the Message object
        return Message(role=role, content=cards)
    except json.JSONDecodeError as e:
        raise ValueError(f"Invalid JSON in content: {str(e)}")
    except ValidationError as e:
        raise ValueError(f"Validation error: {str(e)}")
    except KeyError as e:
        raise ValueError(f"Missing required key: {str(e)}")