app.py

from langdetect import detect as get_language
from collections import namedtuple
from inspect import signature
import os
import subprocess
import logging
import re
import random
from string import ascii_letters, digits, punctuation
import requests
import sys
import warnings
import time
import asyncio
import math
from pathlib import Path
from functools import partial
from dataclasses import dataclass
from typing import Any
import pillow_heif
import spaces
import numpy as np
import numpy.typing as npt
import torch
from torch import nn
import gradio as gr
from lxml.html import fromstring
from huggingface_hub import hf_hub_download
from safetensors.torch import load_file, save_file
from diffusers import FluxPipeline
from PIL import Image, ImageDraw, ImageFont
from transformers import pipeline, T5ForConditionalGeneration, T5Tokenizer
from refiners.fluxion.utils import manual_seed
from refiners.foundationals.latent_diffusion import Solver, solvers
from refiners.foundationals.latent_diffusion.stable_diffusion_1.multi_upscaler import (
    MultiUpscaler,
    UpscalerCheckpoints,
)
from datetime import datetime

model = T5ForConditionalGeneration.from_pretrained("t5-large")
tokenizer = T5Tokenizer.from_pretrained("t5-large")

def log(msg):
    print(f'{datetime.now().time()} {msg}')

Tile = tuple[int, int, Image.Image]
Tiles = list[tuple[int, int, list[Tile]]]

def conv_block(in_nc: int, out_nc: int) -> nn.Sequential:
    return nn.Sequential(
        nn.Conv2d(in_nc, out_nc, kernel_size=3, padding=1),
        nn.LeakyReLU(negative_slope=0.2, inplace=True),
    )

class ResidualDenseBlock_5C(nn.Module):
    """
    Residual Dense Block
    The core module of paper: (Residual Dense Network for Image Super-Resolution, CVPR 18)
    Modified options that can be used:
        - "Partial Convolution based Padding" arXiv:1811.11718
        - "Spectral normalization" arXiv:1802.05957
        - "ICASSP 2020 - ESRGAN+ : Further Improving ESRGAN" N. C.
            {Rakotonirina} and A. {Rasoanaivo}
    """

    def __init__(self, nf: int = 64, gc: int = 32) -> None:
        super().__init__()  # type: ignore[reportUnknownMemberType]

        self.conv1 = conv_block(nf, gc)
        self.conv2 = conv_block(nf + gc, gc)
        self.conv3 = conv_block(nf + 2 * gc, gc)
        self.conv4 = conv_block(nf + 3 * gc, gc)
        # Wrapped in Sequential because of key in state dict.
        self.conv5 = nn.Sequential(nn.Conv2d(nf + 4 * gc, nf, kernel_size=3, padding=1))

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        x1 = self.conv1(x)
        x2 = self.conv2(torch.cat((x, x1), 1))
        x3 = self.conv3(torch.cat((x, x1, x2), 1))
        x4 = self.conv4(torch.cat((x, x1, x2, x3), 1))
        x5 = self.conv5(torch.cat((x, x1, x2, x3, x4), 1))
        return x5 * 0.2 + x


class RRDB(nn.Module):
    """
    Residual in Residual Dense Block
    (ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks)
    """

    def __init__(self, nf: int) -> None:
        super().__init__()  # type: ignore[reportUnknownMemberType]
        self.RDB1 = ResidualDenseBlock_5C(nf)
        self.RDB2 = ResidualDenseBlock_5C(nf)
        self.RDB3 = ResidualDenseBlock_5C(nf)

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        out = self.RDB1(x)
        out = self.RDB2(out)
        out = self.RDB3(out)
        return out * 0.2 + x


class Upsample2x(nn.Module):
    """Upsample 2x."""

    def __init__(self) -> None:
        super().__init__()  # type: ignore[reportUnknownMemberType]

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        return nn.functional.interpolate(x, scale_factor=2.0)  # type: ignore


class ShortcutBlock(nn.Module):
    """Elementwise sum the output of a submodule to its input"""

    def __init__(self, submodule: nn.Module) -> None:
        super().__init__()  # type: ignore[reportUnknownMemberType]
        self.sub = submodule

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        return x + self.sub(x)


class RRDBNet(nn.Module):
    def __init__(self, in_nc: int, out_nc: int, nf: int, nb: int) -> None:
        super().__init__()  # type: ignore[reportUnknownMemberType]
        assert in_nc % 4 != 0  # in_nc is 3

        self.model = nn.Sequential(
            nn.Conv2d(in_nc, nf, kernel_size=3, padding=1),
            ShortcutBlock(
                nn.Sequential(
                    *(RRDB(nf) for _ in range(nb)),
                    nn.Conv2d(nf, nf, kernel_size=3, padding=1),
                )
            ),
            Upsample2x(),
            nn.Conv2d(nf, nf, kernel_size=3, padding=1),
            nn.LeakyReLU(negative_slope=0.2, inplace=True),
            Upsample2x(),
            nn.Conv2d(nf, nf, kernel_size=3, padding=1),
            nn.LeakyReLU(negative_slope=0.2, inplace=True),
            nn.Conv2d(nf, nf, kernel_size=3, padding=1),
            nn.LeakyReLU(negative_slope=0.2, inplace=True),
            nn.Conv2d(nf, out_nc, kernel_size=3, padding=1),
        )

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        return self.model(x)


def infer_params(state_dict: dict[str, torch.Tensor]) -> tuple[int, int, int, int, int]:
    # this code is adapted from https://github.com/victorca25/iNNfer
    scale2x = 0
    scalemin = 6
    n_uplayer = 0
    out_nc = 0
    nb = 0

    for block in list(state_dict):
        parts = block.split(".")
        n_parts = len(parts)
        if n_parts == 5 and parts[2] == "sub":
            nb = int(parts[3])
        elif n_parts == 3:
            part_num = int(parts[1])
            if part_num > scalemin and parts[0] == "model" and parts[2] == "weight":
                scale2x += 1
            if part_num > n_uplayer:
                n_uplayer = part_num
                out_nc = state_dict[block].shape[0]
        assert "conv1x1" not in block  # no ESRGANPlus

    nf = state_dict["model.0.weight"].shape[0]
    in_nc = state_dict["model.0.weight"].shape[1]
    scale = 2**scale2x

    assert out_nc > 0
    assert nb > 0

    return in_nc, out_nc, nf, nb, scale  # 3, 3, 64, 23, 4

# https://github.com/philz1337x/clarity-upscaler/blob/e0cd797198d1e0e745400c04d8d1b98ae508c73b/modules/images.py#L64
Grid = namedtuple("Grid", ["tiles", "tile_w", "tile_h", "image_w", "image_h", "overlap"])

# adapted from https://github.com/philz1337x/clarity-upscaler/blob/e0cd797198d1e0e745400c04d8d1b98ae508c73b/modules/images.py#L67
def split_grid(image: Image.Image, tile_w: int = 512, tile_h: int = 512, overlap: int = 64) -> Grid:
    w = image.width
    h = image.height

    non_overlap_width = tile_w - overlap
    non_overlap_height = tile_h - overlap

    cols = max(1, math.ceil((w - overlap) / non_overlap_width))
    rows = max(1, math.ceil((h - overlap) / non_overlap_height))

    dx = (w - tile_w) / (cols - 1) if cols > 1 else 0
    dy = (h - tile_h) / (rows - 1) if rows > 1 else 0

    grid = Grid([], tile_w, tile_h, w, h, overlap)
    for row in range(rows):
        row_images: list[Tile] = []
        y1 = max(min(int(row * dy), h - tile_h), 0)
        y2 = min(y1 + tile_h, h)
        for col in range(cols):
            x1 = max(min(int(col * dx), w - tile_w), 0)
            x2 = min(x1 + tile_w, w)
            tile = image.crop((x1, y1, x2, y2))
            row_images.append((x1, tile_w, tile))
        grid.tiles.append((y1, tile_h, row_images))

    return grid


# https://github.com/philz1337x/clarity-upscaler/blob/e0cd797198d1e0e745400c04d8d1b98ae508c73b/modules/images.py#L104
def combine_grid(grid: Grid):
    def make_mask_image(r: npt.NDArray[np.float32]) -> Image.Image:
        r = r * 255 / grid.overlap
        return Image.fromarray(r.astype(np.uint8), "L")

    mask_w = make_mask_image(
        np.arange(grid.overlap, dtype=np.float32).reshape((1, grid.overlap)).repeat(grid.tile_h, axis=0)
    )
    mask_h = make_mask_image(
        np.arange(grid.overlap, dtype=np.float32).reshape((grid.overlap, 1)).repeat(grid.image_w, axis=1)
    )

    combined_image = Image.new("RGB", (grid.image_w, grid.image_h))
    for y, h, row in grid.tiles:
        combined_row = Image.new("RGB", (grid.image_w, h))
        for x, w, tile in row:
            if x == 0:
                combined_row.paste(tile, (0, 0))
                continue

            combined_row.paste(tile.crop((0, 0, grid.overlap, h)), (x, 0), mask=mask_w)
            combined_row.paste(tile.crop((grid.overlap, 0, w, h)), (x + grid.overlap, 0))

        if y == 0:
            combined_image.paste(combined_row, (0, 0))
            continue

        combined_image.paste(
            combined_row.crop((0, 0, combined_row.width, grid.overlap)),
            (0, y),
            mask=mask_h,
        )
        combined_image.paste(
            combined_row.crop((0, grid.overlap, combined_row.width, h)),
            (0, y + grid.overlap),
        )

    return combined_image


class UpscalerESRGAN:
    def __init__(self, model_path: Path, device: torch.device, dtype: torch.dtype):
        self.model_path = model_path
        self.device = device
        self.model = self.load_model(model_path)
        self.to(device, dtype)

    def __call__(self, img: Image.Image) -> Image.Image:
        return self.upscale_without_tiling(img)

    def to(self, device: torch.device, dtype: torch.dtype):
        self.device = device
        self.dtype = dtype
        self.model.to(device=device, dtype=dtype)

    def load_model(self, path: Path) -> RRDBNet:
        filename = path
        state_dict: dict[str, torch.Tensor] = torch.load(filename, weights_only=True, map_location=self.device)  # type: ignore
        in_nc, out_nc, nf, nb, upscale = infer_params(state_dict)
        assert upscale == 4, "Only 4x upscaling is supported"
        model = RRDBNet(in_nc=in_nc, out_nc=out_nc, nf=nf, nb=nb)
        model.load_state_dict(state_dict)
        model.eval()

        return model

    def upscale_without_tiling(self, img: Image.Image) -> Image.Image:
        img_np = np.array(img)
        img_np = img_np[:, :, ::-1]
        img_np = np.ascontiguousarray(np.transpose(img_np, (2, 0, 1))) / 255
        img_t = torch.from_numpy(img_np).float()  # type: ignore
        img_t = img_t.unsqueeze(0).to(device=self.device, dtype=self.dtype)
        with torch.no_grad():
            output = self.model(img_t)
        output = output.squeeze().float().cpu().clamp_(0, 1).numpy()
        output = 255.0 * np.moveaxis(output, 0, 2)
        output = output.astype(np.uint8)
        output = output[:, :, ::-1]
        return Image.fromarray(output, "RGB")

    # https://github.com/philz1337x/clarity-upscaler/blob/e0cd797198d1e0e745400c04d8d1b98ae508c73b/modules/esrgan_model.py#L208
    def upscale_with_tiling(self, img: Image.Image) -> Image.Image:
        img = img.convert("RGB")
        grid = split_grid(img)
        newtiles: Tiles = []
        scale_factor: int = 1

        for y, h, row in grid.tiles:
            newrow: list[Tile] = []
            for tiledata in row:
                x, w, tile = tiledata
                output = self.upscale_without_tiling(tile)
                scale_factor = output.width // tile.width
                newrow.append((x * scale_factor, w * scale_factor, output))
            newtiles.append((y * scale_factor, h * scale_factor, newrow))

        newgrid = Grid(
            newtiles,
            grid.tile_w * scale_factor,
            grid.tile_h * scale_factor,
            grid.image_w * scale_factor,
            grid.image_h * scale_factor,
            grid.overlap * scale_factor,
        )
        output = combine_grid(newgrid)
        return output

@dataclass(kw_only=True)
class ESRGANUpscalerCheckpoints(UpscalerCheckpoints):
    esrgan: Path

class ESRGANUpscaler(MultiUpscaler):
    def __init__(
        self,
        checkpoints: ESRGANUpscalerCheckpoints,
        device: torch.device,
        dtype: torch.dtype,
    ) -> None:
        super().__init__(checkpoints=checkpoints, device=device, dtype=dtype)
        self.esrgan = UpscalerESRGAN(checkpoints.esrgan, device=self.device, dtype=self.dtype)

    def to(self, device: torch.device, dtype: torch.dtype):
        self.esrgan.to(device=device, dtype=dtype)
        self.sd = self.sd.to(device=device, dtype=dtype)
        self.device = device
        self.dtype = dtype

    def pre_upscale(self, image: Image.Image, upscale_factor: float, **_: Any) -> Image.Image:
        image = self.esrgan.upscale_with_tiling(image)
        return super().pre_upscale(image=image, upscale_factor=upscale_factor / 4)

pillow_heif.register_heif_opener()
pillow_heif.register_avif_opener()

CHECKPOINTS = ESRGANUpscalerCheckpoints(
    unet=Path(
        hf_hub_download(
            repo_id="refiners/juggernaut.reborn.sd1_5.unet",
            filename="model.safetensors",
            revision="347d14c3c782c4959cc4d1bb1e336d19f7dda4d2",
        )
    ),
    clip_text_encoder=Path(
        hf_hub_download(
            repo_id="refiners/juggernaut.reborn.sd1_5.text_encoder",
            filename="model.safetensors",
            revision="744ad6a5c0437ec02ad826df9f6ede102bb27481",
        )
    ),
    lda=Path(
        hf_hub_download(
            repo_id="refiners/juggernaut.reborn.sd1_5.autoencoder",
            filename="model.safetensors",
            revision="3c1aae3fc3e03e4a2b7e0fa42b62ebb64f1a4c19",
        )
    ),
    controlnet_tile=Path(
        hf_hub_download(
            repo_id="refiners/controlnet.sd1_5.tile",
            filename="model.safetensors",
            revision="48ced6ff8bfa873a8976fa467c3629a240643387",
        )
    ),
    esrgan=Path(
        hf_hub_download(
            repo_id="philz1337x/upscaler",
            filename="4x-UltraSharp.pth",
            revision="011deacac8270114eb7d2eeff4fe6fa9a837be70",
        )
    ),
    negative_embedding=Path(
        hf_hub_download(
            repo_id="philz1337x/embeddings",
            filename="JuggernautNegative-neg.pt",
            revision="203caa7e9cc2bc225031a4021f6ab1ded283454a",
        )
    ),
    negative_embedding_key="string_to_param.*",
    loras={
        "more_details": Path(
            hf_hub_download(
                repo_id="philz1337x/loras",
                filename="more_details.safetensors",
                revision="a3802c0280c0d00c2ab18d37454a8744c44e474e",
            )
        ),
        "sdxl_render": Path(
            hf_hub_download(
                repo_id="philz1337x/loras",
                filename="SDXLrender_v2.0.safetensors",
                revision="a3802c0280c0d00c2ab18d37454a8744c44e474e",
            )
        )
    }
)

device = DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
DTYPE = torch.bfloat16 if torch.cuda.is_bf16_supported() else torch.float32
enhancer = ESRGANUpscaler(checkpoints=CHECKPOINTS, device=DEVICE, dtype=DTYPE)

# logging

warnings.filterwarnings("ignore")
root = logging.getLogger()
root.setLevel(logging.WARN)
handler = logging.StreamHandler(sys.stderr)
handler.setLevel(logging.WARN)
formatter = logging.Formatter('\n >>> [%(levelname)s] %(asctime)s %(name)s: %(message)s\n')
handler.setFormatter(formatter)
root.addHandler(handler)

# constant data

base = "black-forest-labs/FLUX.1-schnell"

# precision data

seq=256
width=2048
height=2048
image_steps=8
img_accu=0

# ui data

css="".join(["""
input, input::placeholder {
    text-align: center !important;
}
*, *::placeholder {
    font-family: Suez One !important;
}
h1,h2,h3,h4,h5,h6 {
    width: 100%;
    text-align: center;
}
footer {
    display: none !important;
}
.image-container {
    aspect-ratio: """,str(width),"/",str(height),""" !important;
}
.dropdown-arrow {
    display: none !important;
}
*:has(>.btn) {
    display: flex;
    justify-content: space-evenly;
    align-items: center;
}
.btn {
    display: flex;
}
"""])

js="""
function custom(){
    document.querySelector("div#prompt input").addEventListener("keydown",function(e){
        e.target.setAttribute("last_value",e.target.value);
    });
    document.querySelector("div#prompt input").addEventListener("input",function(e){
        if( e.target.value.toString().match(/[^ a-zA-Z,]|( |,){2,}/gsm) ){
            e.target.value = e.target.getAttribute("last_value");
            e.target.removeAttribute("last_value");
        }
    });

    document.querySelector("div#prompt2 input").addEventListener("keydown",function(e){
        e.target.setAttribute("last_value",e.target.value);
    });
    document.querySelector("div#prompt2 input").addEventListener("input",function(e){
        if( e.target.value.toString().match(/[^ a-zA-Z,]|( |,){2,}/gsm) ){
            e.target.value = e.target.getAttribute("last_value");
            e.target.removeAttribute("last_value");
        }
    });
}
"""

# torch pipes

image_pipe = FluxPipeline.from_pretrained(base, torch_dtype=torch.bfloat16).to(device)
image_pipe.enable_model_cpu_offload()
image_pipe.enable_vae_slicing()
image_pipe.enable_vae_tiling()

# functionality

def upscaler(
    input_image: Image.Image,
    prompt: str = "Photorealistic, Hyperrealistic, Realistic Photography, High-Quality Photography, Natural.",
    negative_prompt: str = "Distorted, Discontinuous, Blurry, Doll-Like, Overly-Plastic, Low-Quality, Painted, Smoothed, Artificial, Phony, Gaudy, Digital Effects.",
    seed: int = int(str(random.random()).split(".")[1]),
    upscale_factor: int = 2,
    controlnet_scale: float = 0.6,
    controlnet_decay: float = 1.0,
    condition_scale: int = 6,
    tile_width: int = 112,
    tile_height: int = 144,
    denoise_strength: float = 0.35,
    num_inference_steps: int = 30,
    solver: str = "DDIM",
) -> Image.Image:

    log(f'CALL upscaler')

    manual_seed(seed)

    solver_type: type[Solver] = getattr(solvers, solver)

    log(f'DBG upscaler 1')

    enhanced_image = enhancer.upscale(
        image=input_image,
        prompt=prompt,
        negative_prompt=negative_prompt,
        upscale_factor=upscale_factor,
        controlnet_scale=controlnet_scale,
        controlnet_scale_decay=controlnet_decay,
        condition_scale=condition_scale,
        tile_size=(tile_height, tile_width),
        denoise_strength=denoise_strength,
        num_inference_steps=num_inference_steps,
        loras_scale={"more_details": 0.5, "sdxl_render": 1.0},
        solver_type=solver_type,
    )

    log(f'RET upscaler')

    return enhanced_image

def get_tensor_length(tensor):
    nums = list(tensor.size())
    ret = 1
    for num in nums:
        ret = ret * num
    return ret

def summarize(
    text, max_len=20, min_len=10
):
    log(f'CALL summarize')
    
    words = text.split()
    
    if get_tensor_length(words) < 5:
        print("Summarization Error: Text is too short, 5 words minimum!")
        return text
        
    prefix = "summarize: "
    ret = ""

    for index in math.ceil( len(words) / 512 ):
        
        chunk = " ".join(words[ index*512:(index+1)*512 ])
        inputs = tokenizer.encode( prefix + chunk, return_tensors="pt", max_length=float('inf'), truncation=False)
        
        while get_tensor_length(inputs) > max_len:
            
            inputs = model.generate(
                inputs,
                length_penalty=2.0,
                num_beams=4,
                early_stopping=True,
                max_length=max( get_tensor_length(inputs) // 4 , max_len ),
                min_length=min_len
            )

        toks = tokenizer.decode(inputs[0], skip_special_tokens=True)
        ret = ret + ("" if ret == "" else " ") + toks

    inputs = tokenizer.encode( prefix + ret, return_tensors="pt", max_length=float('inf'), truncation=False)
    gen = model.generate(
        inputs,
        length_penalty=1.0,
        num_beams=4,
        early_stopping=True,
        max_length=max_len,
        min_length=min_len
    )
    summary = tokenizer.decode(gen[0], skip_special_tokens=True)
    log(f'RET summarize with summary as {summary}')
    return summary

def generate_random_string(length):
    characters = str(ascii_letters + digits)
    return ''.join(random.choice(characters) for _ in range(length))

def pipe_generate_image(p1,p2):
    log(f'CALL pipe_generate')
    imgs = image_pipe(
            prompt=p1,
            negative_prompt=p2,
            height=height,
            width=width,
            guidance_scale=img_accu,
            num_images_per_prompt=1,
            num_inference_steps=image_steps,
            max_sequence_length=seq,
            generator=torch.Generator(device).manual_seed(int(str(random.random()).split(".")[1]))
    ).images
    log(f'RET pipe_generate')
    return imgs

def add_song_cover_text(img,artist,song,height,width):

    draw = ImageDraw.Draw(img,mode="RGBA")

    rows = 1
    labels_distance = 1/3

    textheight=min(math.ceil( width / 10 ), math.ceil( height / 5 ))
    font = ImageFont.truetype(r"Alef-Bold.ttf", textheight)
    textwidth = draw.textlength(song,font)
    x = math.ceil((width - textwidth) / 2)
    y = height - (textheight * rows / 2) - (height / 2)
    y = math.ceil(y - (height / 2 * labels_distance))
    draw.text((x, y), song, (255,255,255,85), font=font, spacing=2, stroke_width=math.ceil(textheight/20), stroke_fill=(0,0,0,170))

    textheight=min(math.ceil( width / 10 ), math.ceil( height / 5 ))
    font = ImageFont.truetype(r"Alef-Bold.ttf", textheight)
    textwidth = draw.textlength(artist,font)
    x = math.ceil((width - textwidth) / 2)
    y = height - (textheight * rows / 2) - (height / 2)
    y = math.ceil(y + (height / 2 * labels_distance))
    draw.text((x, y), artist, (0,0,0,85), font=font, spacing=2, stroke_width=math.ceil(textheight/20), stroke_fill=(255,255,255,170))

    return img

def all_pipes(pos,neg,artist,song):

    imgs = pipe_generate_image(pos,neg)

    for i in range(len(imgs)):
        imgs[i] = upscaler(imgs[i])

    return imgs

language_codes = {
    "af": "Afrikaans",
    "ar": "Arabic",
    "bg": "Bulgarian",
    "bn": "Bengali",
    "ca": "Catalan",
    "cs": "Czech",
    "cy": "Welsh",
    "da": "Danish",
    "de": "German",
    "el": "Greek",
    "en": "English",
    "es": "Spanish",
    "et": "Estonian",
    "fa": "Persian (Farsi)",
    "fi": "Finnish",
    "fr": "French",
    "gu": "Gujarati",
    "he": "Hebrew",
    "hi": "Hindi",
    "hr": "Croatian",
    "hu": "Hungarian",
    "id": "Indonesian",
    "it": "Italian",
    "ja": "Japanese",
    "kn": "Kannada",
    "ko": "Korean",
    "lt": "Lithuanian",
    "lv": "Latvian",
    "mk": "Macedonian",
    "ml": "Malayalam",
    "mr": "Marathi",
    "ne": "Nepali",
    "nl": "Dutch",
    "no": "Norwegian",
    "pa": "Punjabi",
    "pl": "Polish",
    "pt": "Portuguese",
    "ro": "Romanian",
    "ru": "Russian",
    "sk": "Slovak",
    "sl": "Slovenian",
    "so": "Somali",
    "sq": "Albanian",
    "sv": "Swedish",
    "sw": "Swahili",
    "ta": "Tamil",
    "te": "Telugu",
    "th": "Thai",
    "tl": "Tagalog (Filipino)",
    "tr": "Turkish",
    "uk": "Ukrainian",
    "ur": "Urdu",
    "vi": "Vietnamese",
    "zh-cn": "Chinese (Simplified)",
    "zh-tw": "Chinese (Traditional)",
}

def translate(txt,to_lang="en",from_lang=False):
    log(f'CALL translate')
    if not from_lang:
        from_lang = get_language(txt)
    if(from_lang == to_lang):
        log(f'RET translate with txt as {txt}')
        return txt
    prefix = f"translate {language_codes[from_lang]} to {language_codes[to_lang]}: "
    words = txt.split()
    ret = ""
    for index in math.ceil( len(words) / 512 ):
        chunk = " ".join(words[ index*512:(index+1)*512 ])
        inputs = tokenizer.encode(prefix+chunk, return_tensors="pt", max_length=float('inf'), truncation=False)
        gen = model.generate(chunk,input)
        toks = tokenizer.decode(gen[0], skip_special_tokens=True)
        ret = ret + ("" if ret == "" else " ") + toks
    log(f'RET translate with ret as {ret}')
    return ret

@spaces.GPU(duration=300)
def handle_generation(artist,song,genre,lyrics):

    log(f'CALL handle_generate')

    pos_artist = re.sub("([ \t\n]){1,}", " ", artist).upper().strip()
    pos_song = re.sub("([ \t\n]){1,}", " ", song).lower().strip()
    pos_song = ' '.join(word[0].upper() + word[1:] for word in pos_song.split())

    pos_genre = re.sub(f'[{punctuation}]', '', re.sub("([ \t\n]){1,}", " ", genre)).lower().strip()
    pos_genre = ' '.join(word[0].upper() + word[1:] for word in pos_genre.split())

    pos_lyrics = re.sub(f'[{punctuation}]', '', re.sub("([ \t\n]){1,}", " ", lyrics)).lower().strip()
    pos_lyrics_sum = pos_lyrics if pos_lyrics == "" else summarize(translate(pos_lyrics))

    neg = f"Sexuality, Humanity, Textual, Labeled, Distorted, Discontinuous, Blurry, Doll-Like, Overly Plastic, Low-Quality, Painted, Smoothed, Artificial, Phony, Gaudy, Digital Effects."
    q = "\""
    pos = f'HQ Hyper-realistic { translate(pos_genre) } song "{ translate(pos_song) }"{ pos_lyrics_sum if pos_lyrics_sum == "" else ": " + pos_lyrics_sum }.'

    print(f"""
        Positive: {pos}

        Negative: {neg}
    """)

    imgs = all_pipes(pos,neg,pos_artist,pos_song)

    index = 1
    names = []
    for img in imgs:
        scaled_by = 2
        labeled_img = add_song_cover_text(img,artist,song,height*scaled_by,width*scaled_by)
        name = f'{artist} - {song} ({index}).png'
        labeled_img.save(name)
        names.append(name)
        index = index + 1

    # return names
    return names[0]
    
# entry

if __name__ == "__main__":
    with gr.Blocks(theme=gr.themes.Citrus(),css=css) as demo:
        gr.Markdown(f"""
            # Song Cover Image Generator
        """)
        with gr.Row():
            with gr.Column(scale=4):
                artist = gr.Textbox(
                        placeholder="Artist name",
                        value="",
                        container=False,
                        max_lines=1
                )
                song = gr.Textbox(
                        placeholder="Song name",
                        value="",
                        container=False,
                        max_lines=1
                )
                genre = gr.Textbox(
                        placeholder="Genre",
                        value="",
                        container=False,
                        max_lines=1
                )
                lyrics = gr.Textbox(
                    placeholder="Lyrics",
                    value="",
                    container=False,
                    max_lines=1
                )

            with gr.Column():
                cover = gr.Image(interactive=False,container=False,elem_classes="image-container", label="Result", show_label=True, type='filepath', show_share_button=False)

        run = gr.Button("Generate",elem_classes="btn")

        run.click(
            fn=handle_generation,
            inputs=[artist,song,genre,lyrics],
            outputs=[cover]
        )

    demo.queue().launch()