# api.py
# This is your main web server file.
# It creates ONE instance of your counselor and serves it.

from fastapi import FastAPI
from fastapi.responses import StreamingResponse
from pydantic import BaseModel
from counselor import UltraAdvancedHybridCounselor  # Your main counselor class
import logging
from fastapi.middleware.cors import CORSMiddleware
import uvicorn
import os
from typing import Optional, Dict, Any # Added for new response models

# ---------- Logging ----------
# (Your logging setup is great, no changes)
logging.basicConfig(
    level=logging.INFO,
    format="%(asctime)s - %(name)s - %(levelname)s - %(funcName)s:%(lineno)d - %(message)s",
    handlers=[logging.FileHandler("logs/api.log", encoding="utf-8"), logging.StreamHandler()],
)
logger = logging.getLogger(__name__)

# ---------- App ----------
app = FastAPI(title="DreamFlow-AI API")

# ---------- CORS ----------
# (Your CORS setup is great, no changes)
ALLOWED_ORIGINS = [
    "http://localhost:3000",
    "http://127.0.0.1:3000",
    "http://localhost:5173",
    "http://127.0.0.1:5173",
]
ALLOWED_ORIGIN_REGEX = r"https://.*\.hf\.space$"  # matches https://<space>.hf.space

app.add_middleware(
    CORSMiddleware,
    allow_origins=ALLOWED_ORIGINS,
    allow_origin_regex=ALLOWED_ORIGIN_REGEX,
    allow_credentials=False,
    allow_methods=["GET", "POST", "OPTIONS"],
    allow_headers=["*"],
    expose_headers=["Content-Type"],
)

# ---------- Model ----------
# This is the most important part.
# This creates ONE instance when the app starts,
# which also starts the background fine-tuning thread.
logger.info("Starting UltraAdvancedHybridCounselor...")
counselor = UltraAdvancedHybridCounselor()
logger.info("Counselor instance created. Starting API.")

# ---------- Pydantic Models ----------
class CounselRequest(BaseModel):
    query: str
    session_id: str

class HealthResponse(BaseModel):
    status: str
    current_model: Optional[str]
    web_search_enabled: bool

# ---------- Routes ----------

@app.get("/")
def root_health():
    """Basic health check for the root."""
    return {"status": "ok", "service": "DreamFlow-AI", "port": int(os.getenv("PORT", 7860))}

@app.get("/health", response_model=HealthResponse)
def get_health_status():
    """
    Returns the detailed health status of the counselor.
    """
    try:
        status = counselor.get_health_status()
        return {
            "status": status.get("status", "error"),
            "current_model": counselor.current_model,
            "web_search_enabled": counselor.tavily is not None
        }
    except Exception as e:
        logger.error(f"Error in /health: {e}")
        return {"status": "error", "current_model": None, "web_search_enabled": False}

@app.get("/model-info")
def get_model_info() -> Dict[str, Any]:
    """
    Returns detailed info on the model and its fine-tuning worker.
    """
    try:
        return counselor.get_current_model_info()
    except Exception as e:
        logger.error(f"Error in /model-info: {e}")
        return {"error": f"An unexpected error occurred: {e}"}

@app.post("/counsel")
async def counsel(request: CounselRequest):
    """
    Main endpoint for asking the counselor a question.
    (Your streaming implementation is great, no changes)
    """
    async def stream_gen():
        try:
            async for chunk in counselor.get_comprehensive_answer(request.query, request.session_id):
                yield chunk.encode("utf-8")
        except Exception as e:
            logger.error(f"Streaming error: {e}")
            yield b"An error occurred during streaming."
    return StreamingResponse(stream_gen(), media_type="text/plain")

# ---------- Entrypoint ----------
# (Your entrypoint is perfect, no changes)
if __name__ == "__main__":
    # Hugging Face Spaces (Docker) expects the app to listen on port 7860
    port = int(os.getenv("PORT", 7860))
    uvicorn.run(app, host="0.0.0.0", port=port)