import app.bootstrap 
from fastapi import FastAPI, UploadFile, File, Form, Request
from fastapi.middleware.cors import CORSMiddleware
import shutil
import os
import json
from app.config import LLM_MODE, VIDEO_TEMP_PATH, LOG_FILE
from app.pipeline import run_pipeline
from app.decision import classify
from app.llm import ensure_deepface_model, ensure_model_ollama, generate_alert
from app.logger import get_logger, log

app = FastAPI()

# LIBERAR CORS (para testes)
app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],  # ou depois restringe
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)

#allow_origins=[
#    "https://seu-space.hf.space"
#]

def get_logger_from_request(request: Request):
    return request.app.state.logger

@app.on_event("startup")
def startup_event():
    app.state.logger = get_logger("API")
    app.state.logger.info("Aplicação iniciada.")
    ensure_deepface_model(app.state.logger)
    
@app.get("/")
def health(request: Request):
    logger = get_logger_from_request(request)
    log("Iniciando health check.", logger=logger)
    return {"status": "ok"}

@app.get("/logs")
def read_logs(request: Request):
    logger = get_logger_from_request(request)
    log("Lendo logs.", logger=logger)
    try:
        if not os.path.exists(LOG_FILE):
            return {"status": "error", "message": "Arquivo de log não encontrado"}

        with open(LOG_FILE, "r") as f:
            content = f.read()

        return {
            "status": "success",
            "log": content.splitlines()
        }

    except Exception as e:
        return {
            "status": "error",
            "message": str(e)
        }

@app.post("/analyze")
async def analyze(request: Request, video: UploadFile = File(...), medical_text: str = Form(None)):
    logger = get_logger_from_request(request)
    log("Iniciando análise.", logger=logger)

    log("Salvando vídeo.", logger=logger)
    with open(VIDEO_TEMP_PATH, "wb") as buffer:
        shutil.copyfileobj(video.file, buffer)
    log("Vídeo salvo com sucesso!", logger=logger)

    log("Iniciando Pipeline", logger=logger)
    data = run_pipeline(VIDEO_TEMP_PATH, logger)
    log("Pipeline concluído.", logger=logger)

    log("Iniciando classificação", logger=logger)
    estado = classify(data)
    log("Classificação concluída com sucesso!", logger=logger)

    if LLM_MODE == "ollama":
        log("Iniciando LLM.", logger=logger)
        ensure_model_ollama(logger=logger)
        log("LLM iniciada com sucesso!", logger=logger)

    log("Iniciando geração de alertas.", logger=logger)
    alerta = generate_alert(data, medical_text, logger=logger)
    log("Geração de alertas concluída com sucesso!", logger=logger)

    log(f"Excluindo arquivo de video: {VIDEO_TEMP_PATH}")
    if os.path.exists(VIDEO_TEMP_PATH):
        os.remove(VIDEO_TEMP_PATH)
    
    log(f"Arquivo de video '{VIDEO_TEMP_PATH}' excluído com sucesso!", logger=logger)

    log("retornando resultados...", logger=logger)

    return {
        "estado": estado,
        "alerta": alerta,
        "data": data
    }