app.py

# This is a Gradio app that integrates a chat interface with a text-to-image and image editing model.
import gradio as gr
import numpy as np
import random
import os
from huggingface_hub import InferenceClient

# --- Constants ---
MAX_SEED = np.iinfo(np.int32).max

# --- Initialize Inference Client ---
client = InferenceClient(
    provider="fal-ai",
    api_key=os.environ["HF_TOKEN"],
    bill_to="huggingface",
)

# --- Core Inference Function for ChatInterface ---
def chat_fn(message, chat_history, seed, randomize_seed, guidance_scale, steps):
    """
    Performs image generation or editing based on user input from the chat interface.
    """
    prompt = message["text"]
    files = message["files"]

    if not prompt and not files:
        raise gr.Error("Please provide a prompt and/or upload an image.")

    if randomize_seed:
        seed = random.randint(0, MAX_SEED)

    input_image = None
    if files:
        print(f"Received image: {files[0]}")
        try:
            # Try to open and convert the image
            with open(files[0], "rb") as image_file:
                input_image = image_file.read()
        except Exception as e:
            raise gr.Error(f"Could not process the uploaded image: {str(e)}. Please try uploading a different image format (JPEG, PNG, WebP).")

    if input_image:
        print(f"Received prompt for image editing: {prompt}")
        image = client.image_to_image(
            input_image,
            prompt=prompt,
            model="black-forest-labs/FLUX.1-Kontext-dev",
            guidance_scale=guidance_scale,
            num_inference_steps=steps,
            seed=seed
        )
    else:
        print(f"Received prompt for text-to-image: {prompt}")
        image = client.text_to_image(
            prompt=prompt,
            model="black-forest-labs/FLUX.1-Kontext-dev",
            guidance_scale=guidance_scale,
            num_inference_steps=steps,
            seed=seed
        )

    # Return the PIL Image as a Gradio Image component
    return gr.Image(value=image)

# --- UI Definition using gr.ChatInterface ---

seed_slider = gr.Slider(label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=42)
randomize_checkbox = gr.Checkbox(label="Randomize seed", value=False)
guidance_slider = gr.Slider(label="Guidance Scale", minimum=1.0, maximum=10.0, step=0.1, value=2.5)
steps_slider = gr.Slider(label="Steps", minimum=1, maximum=30, value=28, step=1)

# --- Examples without external URLs ---
# Remove examples temporarily to avoid format issues
examples = None

demo = gr.ChatInterface(
    fn=chat_fn,
    title="FLUX.1 Kontext [dev]",
    description="""<p style='text-align: center;'>
    A simple chat UI for the <b>FLUX.1 Kontext</b> model running on ZeroGPU.
    <br>
    To edit an image, upload it and type your instructions (e.g., "Add a hat").
    <br>
    To generate an image, just type a prompt (e.g., "A photo of an astronaut on a horse").
    <br>
    Find the model on <a href='https://huggingface.co/black-forest-labs/FLUX.1-Kontext-dev' target='_blank'>Hugging Face</a>.
    </p>""",
    multimodal=True,  # This is important for MultimodalTextbox to work
    textbox=gr.MultimodalTextbox(
        file_types=["image"],
        placeholder="Type a prompt and/or upload an image...",
        render=False
    ),
    additional_inputs=[
        seed_slider,
        randomize_checkbox,
        guidance_slider,
        steps_slider
    ],
    examples=examples,
    theme="soft"
)

if __name__ == "__main__":
    demo.launch(show_error=True)