from typing import List, Optional, Dict, Any, Generator
from pydantic import Field
import requests
from loguru import logger
from openai import OpenAI
from openai.types.chat import ChatCompletionChunk

from airtrain.core.skills import Skill, ProcessingError
from airtrain.core.schemas import InputSchema, OutputSchema
from .credentials import FireworksCredentials
from .models import FireworksMessage, FireworksResponse


class FireworksInput(InputSchema):
    """Schema for Fireworks AI chat input"""

    user_input: str = Field(..., description="User's input text")
    system_prompt: str = Field(
        default="You are a helpful assistant.",
        description="System prompt to guide the model's behavior",
    )
    conversation_history: List[Dict[str, str]] = Field(
        default_factory=list,
        description="List of previous conversation messages in [{'role': 'user|assistant', 'content': 'message'}] format",
    )
    model: str = Field(
        default="accounts/fireworks/models/deepseek-r1",
        description="Fireworks AI model to use",
    )
    temperature: float = Field(
        default=0.7, description="Temperature for response generation", ge=0, le=1
    )
    max_tokens: Optional[int] = Field(
        default=None, description="Maximum tokens in response"
    )
    context_length_exceeded_behavior: str = Field(
        default="truncate", description="Behavior when context length is exceeded"
    )
    stream: bool = Field(
        default=False,
        description="Whether to stream the response token by token",
    )


class FireworksOutput(OutputSchema):
    """Schema for Fireworks AI chat output"""

    response: str = Field(..., description="Model's response text")
    used_model: str = Field(..., description="Model used for generation")
    usage: Dict[str, int] = Field(default_factory=dict, description="Usage statistics")


class FireworksChatSkill(Skill[FireworksInput, FireworksOutput]):
    """Skill for interacting with Fireworks AI models"""

    input_schema = FireworksInput
    output_schema = FireworksOutput

    def __init__(self, credentials: Optional[FireworksCredentials] = None):
        """Initialize the skill with optional credentials"""
        super().__init__()
        self.credentials = credentials or FireworksCredentials.from_env()
        self.client = OpenAI(
            base_url="https://api.fireworks.ai/inference/v1",
            api_key=self.credentials.fireworks_api_key.get_secret_value(),
        )

    def _build_messages(self, input_data: FireworksInput) -> List[Dict[str, str]]:
        """Build messages list from input data including conversation history."""
        messages = [{"role": "system", "content": input_data.system_prompt}]

        if input_data.conversation_history:
            messages.extend(input_data.conversation_history)

        messages.append({"role": "user", "content": input_data.user_input})
        return messages

    def process_stream(self, input_data: FireworksInput) -> Generator[str, None, None]:
        """Process the input and stream the response token by token."""
        try:
            messages = self._build_messages(input_data)

            stream = self.client.chat.completions.create(
                model=input_data.model,
                messages=messages,
                temperature=input_data.temperature,
                max_tokens=input_data.max_tokens,
                stream=True,
            )

            for chunk in stream:
                if chunk.choices[0].delta.content is not None:
                    yield chunk.choices[0].delta.content

        except Exception as e:
            raise ProcessingError(f"Fireworks streaming failed: {str(e)}")

    def process(self, input_data: FireworksInput) -> FireworksOutput:
        """Process the input and return the complete response."""
        try:
            if input_data.stream:
                # For streaming, collect the entire response
                response_chunks = []
                for chunk in self.process_stream(input_data):
                    response_chunks.append(chunk)
                response = "".join(response_chunks)
            else:
                # For non-streaming, use regular completion
                messages = self._build_messages(input_data)
                completion = self.client.chat.completions.create(
                    model=input_data.model,
                    messages=messages,
                    temperature=input_data.temperature,
                    max_tokens=input_data.max_tokens,
                    stream=False,
                )
                response = completion.choices[0].message.content

            return FireworksOutput(
                response=response,
                used_model=input_data.model,
                usage={
                    "total_tokens": completion.usage.total_tokens,
                    "prompt_tokens": completion.usage.prompt_tokens,
                    "completion_tokens": completion.usage.completion_tokens,
                },
            )

        except Exception as e:
            raise ProcessingError(f"Fireworks chat failed: {str(e)}")
