app.py

import gradio as gr
import requests
import io
import random
import os
import time
import numpy as np
import subprocess
import torch
from transformers import AutoProcessor, AutoModelForCausalLM
from PIL import Image
from deep_translator import GoogleTranslator
import json
from datetime import datetime
from fastapi import FastAPI

app = FastAPI()

#----------Start of theme----------
theme = gr.themes.Soft(
    primary_hue="zinc",
    secondary_hue="stone",
    font=[gr.themes.GoogleFont('Kavivanar'), gr.themes.GoogleFont('Kavivanar'), 'system-ui', 'sans-serif'],
    font_mono=[gr.themes.GoogleFont('Source Code Pro'), gr.themes.GoogleFont('Inconsolata'), gr.themes.GoogleFont('Inconsolata'), 'monospace'],
).set(
    body_background_fill='*primary_50',
    body_text_color='secondary_600',
    body_text_color_subdued='*primary_500',
    body_text_weight='500',
    background_fill_primary='*primary_100',
    background_fill_secondary='*secondary_100',
    color_accent='*primary_300',
    border_color_accent_subdued='*primary_400',
    border_color_primary='*primary_400',
    block_background_fill='*primary_200',
    block_border_width='*panel_border_width',
    block_info_text_color='*primary_700',
    block_info_text_size='*text_md',
    panel_background_fill='*primary_100',
    accordion_text_color='*primary_600',
    table_text_color='*primary_600',
    input_background_fill='*primary_50',
    input_background_fill_focus='*primary_100',
    button_primary_background_fill='*primary_500',
    button_primary_background_fill_hover='*primary_400',
    button_primary_text_color='*primary_50',
    button_primary_text_color_hover='*primary_100',
    button_cancel_background_fill='*primary_500',
    button_cancel_background_fill_hover='*primary_400'
)
#----------End of theme----------

API_TOKEN = os.getenv("HF_READ_TOKEN")
headers = {"Authorization": f"Bearer {API_TOKEN}"}
timeout = 100

def flip_image(x):
    return np.fliplr(x)

def clear():
  return None    

def query(lora_id, prompt, is_negative=False, steps=28, cfg_scale=3.5, sampler="DPM++ 2M Karras", seed=-1, strength=100, width=896, height=1152):
    if prompt == "" or prompt == None:
        return None

    if lora_id.strip() == "" or lora_id == None:
        lora_id = "black-forest-labs/FLUX.1-dev"

    key = random.randint(0, 999)

    API_URL = "https://api-inference.huggingface.co/models/"+ lora_id.strip()

    API_TOKEN = random.choice([os.getenv("HF_READ_TOKEN")])
    headers = {"Authorization": f"Bearer {API_TOKEN}"}
    
    # prompt = GoogleTranslator(source='ru', target='en').translate(prompt)
    # print(f'\033[1mGeneration {key} translation:\033[0m {prompt}')
    prompt = GoogleTranslator(source='ru', target='en').translate(prompt)
    print(f'\033[1mGeneration {key} translation:\033[0m {prompt}')

    prompt = f"{prompt} | ultra detail, ultra elaboration, ultra quality, perfect."
    print(f'\033[1mGeneration {key}:\033[0m {prompt}')

    # If seed is -1, generate a random seed and use it
    if seed == -1:
        seed = random.randint(1, 1000000000)

    # Prepare the payload for the API call, including width and height
    payload = {
        "inputs": prompt,
        "is_negative": is_negative,
        "steps": steps,
        "cfg_scale": cfg_scale,
        "seed": seed if seed != -1 else random.randint(1, 1000000000),
        "strength": strength,
        "parameters": {
            "width": width,  # Pass the width to the API
            "height": height  # Pass the height to the API
        }
    }

    response = requests.post(API_URL, headers=headers, json=payload, timeout=timeout)
    if response.status_code != 200:
        print(f"Error: Failed to get image. Response status: {response.status_code}")
        print(f"Response content: {response.text}")
        if response.status_code == 503:
            raise gr.Error(f"{response.status_code} : The model is being loaded")
        raise gr.Error(f"{response.status_code}")

    try:
        image_bytes = response.content
        image = Image.open(io.BytesIO(image_bytes))
        print(f'\033[1mGeneration {key} completed!\033[0m ({prompt})')
        return image, seed
    except Exception as e:
        print(f"Error when trying to open the image: {e}")
        return None

examples = [
    "a beautiful woman with blonde hair and blue eyes",
    "a beautiful woman with brown hair and grey eyes",
    "a beautiful woman with black hair and brown eyes",
]

css = """
#app-container {
    max-width: 930px;
    margin-left: auto;
    margin-right: auto;
}
".gradio-container {background: url('file=abstract.jpg')}
   
"""
with gr.Blocks(theme=theme, css=css, elem_id="app-container") as app:
    gr.HTML("<center><h6>🎨 FLUX.1-Dev with LoRA 🇬🇧</h6></center>")
    with gr.Tab("Text to Image"):
        with gr.Column(elem_id="app-container"):
            with gr.Row():
                with gr.Column(elem_id="prompt-container"):
                    with gr.Row():
                        text_prompt = gr.Textbox(label="Prompt", placeholder="Enter a prompt here", lines=2, elem_id="prompt-text-input")
                    with gr.Row():
                        with gr.Accordion("🎨 Lora trigger words", open=False):
                        		gr.Markdown("""
                                    - **sdxl-realistic**: szn style
                                    - **stylesdxl-cyberpunk**: szn style
                                    - **surreal-harmony**: Surreal Harmony
                                    - **extremely-detailed**: extremely detailed
                                    - **dark-fantasy**: Dark Fantasy
                                    - **analogredmond**: AnalogRedmAF
                                    - **jules-bastien-lepage-style**: Jules Bastien Lepage Style
                                    - **john-singer-sargent-style**: John Singer Sargent Style
                                    - **alphonse-mucha-style**: Alphonse Mucha Style
                                    - **ultra-realistic-illustration**: ultra realistic illustration
                                    - **eye-catching**: eye-catching
                                    - **john-constable-style**: John Constable Style
                                    - **film-noir**: in the style of FLMNR
                                    - **flux-lora-pro-headshot**: PROHEADSHOT
                        		""")                       
                            
                    with gr.Row():
                        custom_lora = gr.Dropdown([" ", "jwu114/lora-sdxl-realistic", "issaccyj/lora-sdxl-cyberpunk", "hugovntr/flux-schnell-realism", "fofr/sdxl-deep-down", "KappaNeuro/surreal-harmony", "ntc-ai/SDXL-LoRA-slider.extremely-detailed", "prithivMLmods/Canopus-LoRA-Flux-FaceRealism", "KappaNeuro/dark-fantasy", "artificialguybr/analogredmond", "KappaNeuro/jules-bastien-lepage-style", "KappaNeuro/john-singer-sargent-style", "KappaNeuro/alphonse-mucha-style", "ntc-ai/SDXL-LoRA-slider.ultra-realistic-illustration", "ntc-ai/SDXL-LoRA-slider.eye-catching", "KappaNeuro/john-constable-style", "dvyio/flux-lora-film-noir", "dvyio/flux-lora-pro-headshot"], label="Custom LoRA",)
                    with gr.Row():
                        with gr.Accordion("⚙️ Advanced Settings", open=False, elem_id="settings-container"):
                            negative_prompt = gr.Textbox(label="Negative Prompt", lines=5, placeholder="What should not be in the image", value=" Bad anatomy, Bad proportions, Deformed, Disconnected limbs, Disfigured, Extra arms, Extra limbs, Extra hands, Fused fingers, Gross proportions, Long neck, Malformed limbs, Mutated, Mutated hands, Mutated limbs, Missing arms, Missing fingers, Poorly drawn hands, Poorly drawn face ugly, tiling, poorly drawn hands, poorly drawn feet, poorly drawn face, out of frame, extra limbs, disfigured, deformed, body out of frame, bad anatomy, watermark, signature, cut off, low contrast, underexposed, overexposed, bad art, beginner, amateur, distorted face, blurry, draft, grainy, text, logo, watermark, banner, extra digits, signature, unprompted-nsfw")
                            with gr.Row():
                                width = gr.Slider(label="Width", value=896, minimum=64, maximum=1216, step=32)
                                height = gr.Slider(label="Height", value=1152, minimum=64, maximum=1216, step=32)
                            steps = gr.Slider(label="Sampling steps", value=50, minimum=1, maximum=100, step=1)
                            cfg = gr.Slider(label="CFG Scale", value=3.5, minimum=1, maximum=20, step=0.5)
                            method = gr.Radio(label="Sampling method", value="DPM++ 2M Karras", choices=["DPM++ 2M Karras", "DPM++ SDE Karras", "DEIS", "LMS", "DPM Adaptive", "DPM++ 2M", "DPM2 Ancestral", "DPM++ S", "DPM++ SDE", "DDPM", "DPM Fast", "Euler", "Euler CFG PP", "Euler a", "Euler Ancestral", "Euler+beta", "Heun", "Heun PP2", "DDIM", "PLMS", "UniPC", "UniPC BH2"])
                            strength = gr.Slider(label="Strength", value=100, minimum=0, maximum=100, step=1)
                            seed = gr.Slider(label="Seed", value=-1, minimum=-1, maximum=1000000000, step=1)
                    with gr.Row():
                        with gr.Accordion("🫘Seed", open=False):
                            seed_output = gr.Textbox(label="Seed Used", show_copy_button = True, elem_id="seed-output")
                            
            with gr.Row():
                text_button = gr.Button("Run", variant='primary', elem_id="gen-button")
                clr_button =gr.Button("Clear Prompt",variant="primary", elem_id="clear_button")
                clr_button.click(lambda: gr.Textbox(value=""), None, text_prompt)
                
            with gr.Row():
                image_output = gr.Image(type="pil", label="Image Output", format="png", elem_id="gallery")
            with gr.Row():
                clear_btn = gr.Button(value="Clear Image", variant="primary", elem_id="clear_button")
                clear_btn.click(clear, inputs=[], outputs=[image_output])
    
            gr.Examples(
                examples = examples,
                inputs = [text_prompt],
            )
            
    text_button.click(query, inputs=[custom_lora, text_prompt, negative_prompt, steps, cfg, method, seed, strength, width, height], outputs=[image_output, seed_output])

    with gr.Tab("Flip Image"):
        with gr.Row():
            image_input = gr.Image()
            image_output = gr.Image(format="png")
        with gr.Row():    
            image_button = gr.Button("Run", variant='primary')
            image_button.click(flip_image, inputs=image_input, outputs=image_output)

    if __name__ == "__main__": 
        app.launch(show_api=False, share=False)