app.py

import gradio as gr
from huggingface_hub import InferenceClient

# Define the inference function
def generate_image(prompt, seed, denoise):
    client = InferenceClient(model="tryonlabs/FLUX.1-dev-LoRA-Lehenga-Generator")
    # Assuming the model accepts these parameters; adjust based on actual API
    image = client.text_to_image(
        prompt=prompt,
        seed=seed if seed >= 0 else None,  # Use -1 for random seed
        denoise=denoise  # Denoising strength, typically between 0 and 1
    )
    return image

# Create the Gradio interface with advanced options
interface = gr.Interface(
    fn=generate_image,
    inputs=[
        gr.Textbox(label="Prompt", placeholder="Enter your prompt here..."),
        gr.Slider(label="Seed", minimum=-1, maximum=1000000, step=1, value=-1, info="Set to -1 for random seed"),
        gr.Slider(label="Denoise Strength", minimum=0.0, maximum=1.0, step=0.01, value=0.8, info="Controls noise reduction (0 to 1)")
    ],
    outputs=gr.Image(label="Generated Lehenga Image"),
    title="FLUX.1-dev Lehenga Generator",
    description="Generate custom Lehenga designs using FLUX.1-dev with LoRA. Adjust seed and denoise for control.",
)

# Launch the interface
interface.launch()