from fastapi import APIRouter, Depends, Request
from app.api.deps import get_api_key, get_current_module
from app.models.module import Module
from sqlalchemy.orm import Session
from app.core.database import get_db
from app.core.limiter import limiter
from app.core.config import settings
from pydantic import BaseModel
from google import genai
import asyncio

router = APIRouter()

class LLMRequest(BaseModel):
    prompt: str
    context: str = ""

# Shared client instance (global)
_client = None

def get_gemini_client():
    global _client
    if _client is None and settings.GOOGLE_API_KEY and settings.GOOGLE_API_KEY != "your-google-api-key":
        _client = genai.Client(api_key=settings.GOOGLE_API_KEY, http_options={'api_version': 'v1alpha'})
    return _client

@router.post("/chat")
@limiter.limit(settings.RATE_LIMIT)
async def gemini_chat(
    request: Request,
    chat_data: LLMRequest,
    api_key: str = Depends(get_api_key),
    module: Module = Depends(get_current_module),
    db: Session = Depends(get_db)
):
    client = get_gemini_client()
    
    try:
        if not client:
             # Mock response
             response_text = f"MOCK: Gemini response to '{chat_data.prompt}'"
             if module:
                 # Estimate tokens for mock
                 prompt_tokens = len(chat_data.prompt) // 4
                 completion_tokens = len(response_text) // 4
                 module.ingress_tokens += prompt_tokens
                 module.egress_tokens += completion_tokens
                 module.total_tokens += (prompt_tokens + completion_tokens)
                 db.commit()

             return {
                "status": "mock",
                "model": "gemini",
                "response": response_text
            }
            
        # Using the async generation method provided by the new google-genai library
        # We use await to ensure we don't block the event loop
        response = await client.aio.models.generate_content(
            model="gemini-2.0-flash", 
            contents=chat_data.prompt
        )
        
        # Track usage if valid module
        if module:
            # Estimate tokens since metadata might vary
            # 1 char ~= 0.25 tokens (rough estimate if exact count not returned)
            # Gemini response usually has usage_metadata
            usage = response.usage_metadata
            prompt_tokens = usage.prompt_token_count if usage else len(chat_data.prompt) // 4
            completion_tokens = usage.candidates_token_count if usage else len(response.text) // 4
            
            module.ingress_tokens += prompt_tokens
            module.egress_tokens += completion_tokens
            module.total_tokens += (prompt_tokens + completion_tokens)
            db.commit()
        
        return {
            "status": "success",
            "model": "gemini",
            "response": response.text
        }
    except Exception as e:
        return {"status": "error", "detail": str(e)}