#!/usr/bin/env python
# -*- coding: UTF-8 -*-
"""
@Project ：trinity_client
@File    ：service_att_recognition.py
@Author  ：周成融
@Date    ：2023/7/26 12:01:05
@detail  ：
"""
import json
import logging
import time

import cv2
import numpy as np
import redis
from PIL import Image
from minio import Minio
from tritonclient.utils import np_to_triton_dtype

from app.core.config import *
import tritonclient.grpc as grpcclient
from app.schemas.generate_image import GenerateSingleLogoImageModel
from app.service.generate_image.utils.upload_sd_image import upload_png_sd, upload_SDXL_image

logger = logging.getLogger()


class GenerateSingleLogoImage:
    def __init__(self, request_data):
        if DEBUG is False:
            self.connection = pika.BlockingConnection(pika.ConnectionParameters(**RABBITMQ_PARAMS))
            self.channel = self.connection.channel()
        # self.connection = pika.BlockingConnection(pika.ConnectionParameters(**RABBITMQ_PARAMS))
        # self.channel = self.connection.channel()
        self.minio_client = Minio(MINIO_URL, access_key=MINIO_ACCESS, secret_key=MINIO_SECRET, secure=MINIO_SECURE)
        self.grpc_client = grpcclient.InferenceServerClient(url=GSL_MODEL_URL)
        self.redis_client = redis.StrictRedis(host=REDIS_HOST, port=REDIS_PORT, db=REDIS_DB, decode_responses=True)
        self.batch_size = 1
        self.category = "single_logo"
        self.negative_prompts = "bad, ugly"
        self.seed = request_data.seed
        self.tasks_id = request_data.tasks_id
        self.prompt = request_data.prompt
        self.user_id = self.tasks_id[self.tasks_id.rfind('-') + 1:]
        self.gen_single_logo_data = {'tasks_id': self.tasks_id, 'status': 'PENDING', 'message': "pending", 'image_url': ''}
        self.redis_client.set(self.tasks_id, json.dumps(self.gen_single_logo_data))
        self.redis_client.expire(self.tasks_id, 600)

    def read_tasks_status(self):
        status_data = self.redis_client.get(self.tasks_id)
        return json.loads(status_data), status_data

    def infer(self, inputs):
        return self.grpc_client.async_infer(
            model_name=GSL_MODEL_NAME,
            inputs=inputs,
            callback=self.callback
        )

    def callback(self, result, error):
        if error:
            self.gen_single_logo_data['status'] = "FAILURE"
            self.gen_single_logo_data['message'] = str(error)
            # self.generate_data['data'] = str(error)
            self.redis_client.set(self.tasks_id, json.dumps(self.gen_single_logo_data))
        else:
            image = result.as_numpy("generated_image")
            image_result = Image.fromarray(np.squeeze(image.astype(np.uint8)))
            image_url = upload_SDXL_image(image_result, user_id=self.user_id, category=f"{self.category}", object_name=f"{self.tasks_id}.png")
            self.gen_single_logo_data['status'] = "SUCCESS"
            self.gen_single_logo_data['message'] = "success"
            self.gen_single_logo_data['image_url'] = str(image_url)
            self.redis_client.set(self.tasks_id, json.dumps(self.gen_single_logo_data))

    def get_result(self):
        try:
            # prompt
            prompts = [self.prompt] * self.batch_size
            text_obj = np.array(prompts, dtype="object").reshape((-1, 1))
            input_text = grpcclient.InferInput("prompt", text_obj.shape, np_to_triton_dtype(text_obj.dtype))
            input_text.set_data_from_numpy(text_obj)

            # negative_prompts
            text_obj_neg = np.array(self.negative_prompts, dtype="object").reshape((-1, 1))
            # print('text obj neg: ', text_obj_neg)
            input_text_neg = grpcclient.InferInput("negative_prompt", text_obj_neg.shape, np_to_triton_dtype(text_obj_neg.dtype))
            input_text_neg.set_data_from_numpy(text_obj_neg)

            # seed
            seed = np.array(self.seed, dtype="object").reshape((-1, 1))
            input_seed = grpcclient.InferInput("seed", seed.shape, np_to_triton_dtype(seed.dtype))
            input_seed.set_data_from_numpy(seed)

            inputs = [input_text, input_text_neg, input_seed]

            ctx = self.infer(inputs)
            time_out = 600
            generate_data = None
            while time_out > 0:
                generate_data, _ = self.read_tasks_status()
                # logger.info(generate_data)
                if generate_data['status'] in ["REVOKED", "FAILURE"]:
                    ctx.cancel()
                    break
                elif generate_data['status'] == "SUCCESS":
                    break
                time_out -= 1
                time.sleep(0.1)
                # logger.info(time_out, generate_data)
            return generate_data
        except Exception as e:
            raise Exception(str(e))
        finally:
            dict_generate_data, str_generate_data = self.read_tasks_status()
            if DEBUG is False:
                self.channel.basic_publish(exchange='', routing_key=GI_RABBITMQ_QUEUES, body=str_generate_data)
            # self.channel.basic_publish(exchange='', routing_key=GI_RABBITMQ_QUEUES, body=str_generate_data)
            logger.info(f" [x] Sent {json.dumps(dict_generate_data, indent=4)}")


def infer_cancel(tasks_id):
    redis_client = redis.StrictRedis(host=REDIS_HOST, port=REDIS_PORT, db=REDIS_DB, decode_responses=True)
    data = {'tasks_id': tasks_id, 'status': 'REVOKED', 'message': "revoked", 'data': 'revoked'}
    generate_data = json.dumps(data)
    redis_client.set(tasks_id, generate_data)
    return data


if __name__ == '__main__':
    rd = GenerateSingleLogoImageModel(
        tasks_id="123-89",
        prompt='an apple',
        seed="2",
    )
    server = GenerateSingleLogoImage(rd)
    print(server.get_result())