app.py

import gradio as gr
import requests
import io
import random
import os
import time
from PIL import Image
from deep_translator import GoogleTranslator
import json
from theme import theme
from fastapi import FastAPI

app = FastAPI()
# Project by Nymbo

API_URL = "https://api-inference.huggingface.co/models/black-forest-labs/FLUX.1-dev"
API_TOKEN = os.getenv("HF_READ_TOKEN")
headers = {"Authorization": f"Bearer {API_TOKEN}"}
timeout = 100

# Function to query the API and return the generated image
def query(prompt, is_negative=False, steps=35, cfg_scale=7, sampler="DPM++ 2M Karras", seed=-1, strength=0.7, width=1024, height=1024):
    if prompt == "" or prompt is None:
        return None

    key = random.randint(0, 999)
    
    API_TOKEN = random.choice([os.getenv("HF_READ_TOKEN")])
    headers = {"Authorization": f"Bearer {API_TOKEN}"}
    
    # Translate the prompt from Russian to English if necessary
    prompt = GoogleTranslator(source='ru', target='en').translate(prompt)
    print(f'\033[1mGeneration {key} translation:\033[0m {prompt}')

    # Add some extra flair to the prompt
    prompt = f"{prompt} | ultra detail, ultra elaboration, ultra quality, perfect."
    print(f'\033[1mGeneration {key}:\033[0m {prompt}')
    
    # 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
        }
    }

    # Send the request to the API and handle the response
    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:
        # Convert the response content into an image
        image_bytes = response.content
        image = Image.open(io.BytesIO(image_bytes))
        print(f'\033[1mGeneration {key} completed!\033[0m ({prompt})')
        return image
    except Exception as e:
        print(f"Error when trying to open the image: {e}")
        return None

# CSS to style the app
css = """
.gradio-container {background-color: MediumAquaMarine}
footer{display:none !important}
#app-container {
    max-width: 930px;
    margin-left: auto;
    margin-right: auto;
}
"""

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",
]

# Build the Gradio UI with Blocks
with gr.Blocks(theme=theme, css=css) as app:
    # Add a title to the app
    gr.HTML("<center><h1>FLUX.1-Dev</h1></center>")
    with gr.Tabs() as tabs:
        with gr.TabItem("✍️ Text to Image 🖼", visible=True):
            # Container for all the UI elements
            with gr.Column(elem_id="app-container"):
                # Add a text input for the main prompt
                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")
                        
                        # Accordion for advanced settings
                        with gr.Row():
                            with gr.Accordion("Advanced Settings", open=False):
                                negative_prompt = gr.Textbox(label="Negative Prompt", placeholder="What should not be in the image", value="(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=35, minimum=1, maximum=100, step=1)
                                cfg = gr.Slider(label="CFG Scale", value=7, minimum=1, maximum=20, step=1)
                                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) # Setting the seed to -1 will make it random
                                method = gr.Radio(label="Sampling method", value="DPM++ 2M Karras", choices=["DPM++ 2M Karras", "DPM++ 2S a Karras", "DPM2 a Karras", "DPM2 Karras", "DPM++ SDE Karras", "DEIS", "LMS", "DPM Adaptive", "DPM++ 2M", "DPM2 Ancestral", "DPM++ S", "DPM++ SDE", "DDPM", "DPM Fast", "dpmpp_2s_ancestral", "Euler", "Euler CFG PP", "Euler a", "Euler Ancestral", "Euler+beta", "Heun", "Heun PP2", "DDIM", "LMS Karras", "PLMS", "UniPC", "UniPC BH2"])
                            
                # Add a button to trigger the image generation
                with gr.Row():
                    text_button = gr.Button("Run", variant='primary', elem_id="gen-button")
                
                # Image output area to display the generated image
                with gr.Row():
                    image_output = gr.Image(type="pil", label="Image Output", show_share_button=False, elem_id="gallery")
                with gr.Row():    
                    clear_prompt =gr.Button("Clear 🗑️",variant="primary", elem_id="clear_button")
                    clear_prompt.click(lambda: (None, None), None, [text_prompt, image_output], queue=False, show_api=False)
                    
                with gr.Row():        
                    gr.Examples(
                        examples = examples,    
                        inputs = [text_prompt],    
                    )            
                
                # Bind the button to the query function with the added width and height inputs
                text_button.click(query, inputs=[text_prompt, negative_prompt, steps, cfg, method, seed, strength, width, height], outputs=image_output)

app.queue(default_concurrency_limit=200, max_size=200)  # <-- Sets up a queue with default parameters
if __name__ == "__main__":
    # Launch the Gradio app
    app.launch(show_api=False, share=True)