app.py

import os

import gradio as gr
import numpy as np
from dotenv import load_dotenv
from huggingface_hub import InferenceClient, login

load_dotenv()

MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 2048
TOKEN = None


def get_token(oauth_token: gr.OAuthToken | None):
    global TOKEN
    if oauth_token and oauth_token.token:
        print("Received OAuth token, logging in...")
        TOKEN = oauth_token.token
    else:
        print("No OAuth token provided, using environment variable HF_TOKEN.")
        TOKEN = os.environ.get("HF_TOKEN")

def generate(prompt: str, seed: int =42, width: int =1024, height: int =1024, num_inference_steps: int = 25):
    """
    Generate an image from a prompt.
    Args:
        prompt (str):
                The prompt to generate an image from.
        seed (int, default=42):
            Seed for the random number generator.
        height (int,  default=1024):
            The height in pixels of the output image
        width (int, default=1024):
            The width in pixels of the output image
        num_inference_steps (int, default=25):
            The number of denoising steps. More denoising steps usually lead to a higher quality image at the
            expense of slower inference.
        
    """
    client = InferenceClient(provider="fal-ai", token=TOKEN)
    image = client.text_to_image(
        prompt=prompt,
        width=width,
        height=height,
        num_inference_steps=num_inference_steps,
        seed=seed,
        model="black-forest-labs/FLUX.1-dev"
    )
    return image, seed
 
examples = [
    "a tiny astronaut hatching from an egg on the moon",
    "a cat holding a sign that says hello world",
    "an anime illustration of a wiener schnitzel",
]

css="""
#col-container {
    margin: 0 auto;
    max-width: 520px;
}
"""

with gr.Blocks(css=css) as demo:
    demo.load(get_token, inputs=None, outputs=None)
    with gr.Sidebar():
        gr.Markdown("# Inference Provider")
        gr.Markdown("This Space showcases the black-forest-labs/FLUX.1-dev model, served by the nebius API. Sign in with your Hugging Face account to use this API.")
        button = gr.LoginButton("Sign in")
        button.click(fn=get_token, inputs=[], outputs=[])
        
    with gr.Column(elem_id="col-container"):
        gr.Markdown(f"""# FLUX.1 [schnell] with fal-ai through HF Inference Providers ⚡
learn more about HF Inference Providers [here](https://huggingface.co/docs/inference-providers/index)""")
        
        with gr.Row():
            
            prompt = gr.Text(
                label="Prompt",
                show_label=False,
                max_lines=1,
                placeholder="Enter your prompt",
                container=False,
            )
            
            run_button = gr.Button("Run", scale=0)
        
        result = gr.Image(label="Result", show_label=False, format="png")
        
        with gr.Accordion("Advanced Settings", open=False):
            
            seed = gr.Slider(
                label="Seed",
                minimum=0,
                maximum=MAX_SEED,
                step=1,
                value=42,
            )
                        
            with gr.Row():
                
                width = gr.Slider(
                    label="Width",
                    minimum=256,
                    maximum=MAX_IMAGE_SIZE,
                    step=32,
                    value=1024,
                )
                
                height = gr.Slider(
                    label="Height",
                    minimum=256,
                    maximum=MAX_IMAGE_SIZE,
                    step=32,
                    value=1024,
                )
            
            with gr.Row():
                
  
                num_inference_steps = gr.Slider(
                    label="Number of inference steps",
                    minimum=1,
                    maximum=50,
                    step=1,
                    value=25,
                )
        
        gr.Examples(
            examples = examples,
            fn = generate,
            inputs = [prompt],
            outputs = [result, seed],
            cache_examples="lazy"
        )

    gr.on(
        triggers=[run_button.click, prompt.submit],
        fn = generate,
        inputs = [prompt, seed, width, height, num_inference_steps],
        outputs = [result, seed]
    )

demo.launch(mcp_server=True)