"""See https://huggingface.co/spaces/Gradio-Blocks/Story-to-video/blob/main/app.py."""
import base64
import io
import re
from random import choice

import gradio as gr
import translators as ts
from fastlid import fastlid
from logzero import logger
from PIL import Image  # opencv-python
from tenacity import retry
from tenacity.stop import stop_after_attempt, stop_after_delay

# from PIL import Image
# from transformers import AutoTokenizer, AutoModelForSeq2SeqLM,pipeline
# import requests
# import torch

image_gen = gr.Interface.load("spaces/multimodalart/latentdiffusion")

examples_ = [
    "蓝色的夜，森林中好多萤火虫",
    "黑云压城城欲摧 ，甲光向日金鳞开。",
    "黄金在河里流淌，宝石遍地，空中铺满巨大的彩虹。",
    "季姬寂，集鸡，鸡即棘鸡。棘鸡饥叽，季姬及箕稷济鸡。",
    "an apple",
    "a cat",
    "blue moon",
    "metaverse",
]


@retry(stop=(stop_after_delay(10) | stop_after_attempt(5)))
def tr_(text: str) -> str:
    """Wrap [ts.deepl, ts.sogou, ts.baidu, ts.google] with tenacity."""
    for tr in [ts.deepl, ts.sogou, ts.baidu, ts.google]:
        try:
            res = tr(text)
            logger.info(" api used: %s", tr.__name__)
            tr_.api_used = tr.__name__
            break
        except Exception:
            continue
    else:
        res = "Something is probably wong, ping dev to fix it if you like."
    return res


def generate_images(phrase: str, steps: int = 125):
    if not phrase.strip():
        phrase = choice(examples_)

    generated_text = phrase
    detected = "en"
    api_used = ""
    try:
        detected = fastlid(phrase)[0]
    except Exception as exc:
        logger.error(exc)

    # safe guard short Chinese phrases
    if len(phrase) < 10 and re.search(r"[一-龟]+", phrase):
        detected = "zh"

    if detected not in ["en"]:
        try:
            generated_text = tr_(phrase)
            api_used = f"({tr_.api_used})"
        except Exception as exc:
            logger.error(exc)
            return None, f"{phrase:}, errors: {str(exc)}"

    # steps = 125
    width = 256
    height = 256
    num_images = 4
    num_images = 1
    diversity = 6

    try:
        image_bytes = image_gen(
            generated_text, steps, width, height, num_images, diversity
        )
    except Exception as exc:
        logger.error(exc)
        return None, f"phrase: {phrase}, errors: {str(exc)}. Try again."

    # Algo from spaces/Gradio-Blocks/latent_gpt2_story/blob/main/app.py
    # generated_images = []

    img = None
    err_msg = f"{phrase} {api_used}"
    for image in image_bytes[1]:
        image_str = image[0]
        try:
            image_str = image_str.replace("data:image/png;base64,", "")
        except Exception as exc:
            logger.error(exc)
            err_msg = str(exc)
            return None, f"errors: {err_msg}. Try again."
        decoded_bytes = base64.decodebytes(bytes(image_str, "utf-8"))
        img = Image.open(io.BytesIO(decoded_bytes))

        # generated_images.append(img)

    # return generated_images

    return img, err_msg


# examples = [["an apple", 125], ["Donald Trump", 125]]
examples = [list(_) for _ in zip(examples_, [125] * len(examples_))]

inputs = [
    # "text",
    gr.Text(value="a dog with a funny hat"),
    gr.Slider(minimum=2, maximum=250, value=115, step=5),
]

iface = gr.Interface(
    generate_images,
    inputs,
    # ["image", gr.Text(value="", label="phrase")],
    [gr.Image(label=""), gr.Text(value="", label="phrase")],
    examples=examples,
)

iface.launch(enable_queue=True)