app.py

#import requests
import gradio as gr
import io
import random
import os
import time
import cv2
from PIL import Image
from deep_translator import GoogleTranslator
import json
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_100',
    body_text_color='secondary_600',
    body_text_color_subdued='*primary_500',
    body_text_weight='500',
    background_fill_primary='*primary_100',
    background_fill_secondary='*secondary_200',
    color_accent='*primary_300',
    border_color_accent_subdued='*primary_400',
    border_color_primary='*primary_400',
    block_background_fill='*primary_300',
    block_border_width='*panel_border_width',
    block_info_text_color='*primary_700',
    block_info_text_size='*text_md',
    panel_background_fill='*primary_200',
    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 query(lora_id, prompt, is_negative=False, steps=28, cfg_scale=3.5, sampler="DPM++ 2M Karras", seed=-1, strength=0.7, width=1024, height=1024):
    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: 896px;
    margin-left: auto;
    margin-right: auto;
    #body{background-image:"DigiP-AI/FLUX.Dev-LORA/abstract(1).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(label="Lora trigger words", open=False):
                        		gr.Markdown("""
                                    - **sdxl-realistic**: szn style
                                    - **stylesdxl-cyberpunk**: szn style
                                    - **maxfield-parrish-stylee**: Maxfield Parrish 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
                                    - **director-sofia-coppola-style**: Director Sofia Coppola Style
                            		""",
                                    label="Trigger words")                       
                            
                    with gr.Row():
                        custom_lora = gr.Dropdown([" ", "jwu114/lora-sdxl-realistic", "issaccyj/lora-sdxl-cyberpunk", "KappaNeuro/maxfield-parrish-style", "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", "KappaNeuro/director-sofia-coppola-style"], label="Custom LoRA", info="Please select from the list")
                    with gr.Row():
                        with gr.Accordion("⚙️ Advanced Settings", open=False, elem_id="settings-container"):
                            negative_prompt = gr.Textbox(label="Negative Prompt", placeholder="What should not be in the image", value="((((out of frame))), deformed, distorted, disfigured), poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, (mutated hands and fingers), disconnected limbs, mutation, mutated, ugly, disgusting, blurry, amputation, misspellings, typos", lines=3, elem_id="negative-prompt-text-input")
                            with gr.Row():
                                width = gr.Slider(label="Width", value=1024, minimum=64, maximum=1216, step=32)
                                height = gr.Slider(label="Height", value=1024, minimum=64, maximum=1216, step=32)
                            steps = gr.Slider(label="Sampling steps", value=28, 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", "DPM Fast" "Euler", "Euler a", "Euler+beta", "Heun", "DDIM", "PLMS", "UniPC"])
                            strength = gr.Slider(label="Strength", value=0.7, minimum=0, maximum=1, step=0.001)
                            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")
            with gr.Row():                
                clr_button =gr.Button("Clear",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")
    
            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("Image Upscaler"):    
        with gr.Row():
            with gr.Column():
                def upscale_image(input_image, radio_input):
                    upscale_factor = radio_input
                    output_image = cv2.resize(input_image, None, fx = upscale_factor, fy = upscale_factor, interpolation = cv2.INTER_CUBIC)
                    return output_image
                radio_input = gr.Radio(label="Upscale Levels", choices=[2, 4, 6, 8, 10], value=2)
                iface = gr.Interface(fn=upscale_image, inputs = [gr.Image(label="Input Image", interactive=True), radio_input], outputs = gr.Image(label="Upscaled Image", format="png"), title="Image Upscaler")
        
    
    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)

    
app.launch(show_api=False, share=False)