import os
import logging
from fastapi import FastAPI, HTTPException, Query
from fastapi.responses import StreamingResponse
from pydantic import BaseModel
from openai import AsyncOpenAI
from typing import Optional

# Configure logging
logging.basicConfig(level=logging.DEBUG)
logger = logging.getLogger(__name__)

app = FastAPI(
    title="Orion AI API",
    description="API for streaming AI responses with model selection and publisher via URL",
    version="1.0.0"
)

# Define valid models (replace with actual models supported by https://models.github.ai/inference)
VALID_MODELS = [
    "DeepSeek-V3-0324",  # Try without deepseek/ prefix
    "gpt-3.5-turbo",     # Placeholder
    "llama-3",           # Placeholder
    "mistral-7b"         # Placeholder
]

class GenerateRequest(BaseModel):
    prompt: str
    publisher: Optional[str] = None  # Allow publisher in the body if needed

async def generate_ai_response(prompt: str, model: str, publisher: Optional[str]):
    logger.debug(f"Received prompt: {prompt}, model: {model}, publisher: {publisher}")
    
    # Configuration for AI endpoint
    token = os.getenv("GITHUB_TOKEN")
    endpoint = os.getenv("AI_SERVER_URL", "https://models.github.ai/inference")
    default_publisher = os.getenv("DEFAULT_PUBLISHER", "abdullahalioo")  # Fallback publisher
    
    if not token:
        logger.error("GitHub token not configured")
        raise HTTPException(status_code=500, detail="GitHub token not configured")

    # Use provided publisher or fallback to environment variable
    final_publisher = publisher or default_publisher
    if not final_publisher:
        logger.error("Publisher is required")
        raise HTTPException(status_code=400, detail="Publisher is required")

    # Validate model
    if model not in VALID_MODELS:
        logger.error(f"Invalid model: {model}. Valid models: {VALID_MODELS}")
        raise HTTPException(status_code=400, detail=f"Invalid model. Valid models: {VALID_MODELS}")

    logger.debug(f"Using endpoint: {endpoint}, publisher: {final_publisher}")
    client = AsyncOpenAI(
        base_url=endpoint,
        api_key=token,
        default_headers={"X-Publisher": final_publisher}  # Pass publisher as header
    )

    try:
        stream = await client.chat.completions.create(
            messages=[
                {"role": "system", "content": "You are a helpful assistant named Orion, created by Abdullah Ali"},
                {"role": "user", "content": prompt}
            ],
            model=model,
            temperature=1.0,
            top_p=1.0,
            stream=True
        )

        async for chunk in stream:
            if chunk.choices and chunk.choices[0].delta.content:
                yield chunk.choices[0].delta.content

    except Exception as err:
        logger.error(f"AI generation failed: {str(err)}")
        # Check for specific AI server errors
        error_msg = str(err)
        if "unknown_model" in error_msg.lower():
            raise HTTPException(status_code=400, detail=f"AI server error: {error_msg}")
        yield f"Error: {error_msg}"
        raise HTTPException(status_code=500, detail=f"AI generation failed: {error_msg}")

@app.post("/generate", summary="Generate AI response", response_description="Streaming AI response")
async def generate_response(
    model: str = Query("DeepSeek-V3-0324", description="The AI model to use"),
    prompt: Optional[str] = Query(None, description="The input text prompt for the AI"),
    publisher: Optional[str] = Query(None, description="Publisher identifier (optional, defaults to DEFAULT_PUBLISHER env var)"),
    request: Optional[GenerateRequest] = None
):
    """
    Generate a streaming AI response based on the provided prompt, model, and publisher.
    
    - **model**: The AI model to use (e.g., DeepSeek-V3-0324)
    - **prompt**: The input text prompt for the AI (query param or body)
    - **publisher**: The publisher identifier (optional, defaults to DEFAULT_PUBLISHER env var)
    """
    logger.debug(f"Request received - model: {model}, prompt: {prompt}, publisher: {publisher}, body: {request}")
    
    # Determine prompt source: query parameter or request body
    final_prompt = prompt if prompt is not None else (request.prompt if request is not None else None)
    # Determine publisher source: query parameter or request body
    final_publisher = publisher if publisher is not None else (request.publisher if request is not None else None)
    
    if not final_prompt or not final_prompt.strip():
        logger.error("Prompt cannot be empty")
        raise HTTPException(status_code=400, detail="Prompt cannot be empty")
    
    if not model or not model.strip():
        logger.error("Model cannot be empty")
        raise HTTPException(status_code=400, detail="Model cannot be empty")
    
    return StreamingResponse(
        generate_ai_response(final_prompt, model, final_publisher),
        media_type="text/event-stream"
    )

@app.get("/models", summary="List available models")
async def list_models():
    """
    List all available models supported by the AI server.
    """
    return {"models": VALID_MODELS}

def get_app():
    return app