app.py

import requests
import io
from PIL import Image
import gradio as gr
import os  # To access environment variables

# Access the Hugging Face API token securely
API_TOKEN = os.getenv("HF_API_TOKEN")

if not API_TOKEN:
    raise ValueError("Hugging Face API token not found. Please check your Space's secrets configuration.")

API_URL = "https://api-inference.huggingface.co/models/stabilityai/stable-diffusion-xl-base-1.0"
headers = {"Authorization": f"Bearer {API_TOKEN}"}

def query(payload):
    response = requests.post(API_URL, headers=headers, json=payload)
    return response.content

def generate_image(prompt, negative_prompt, guidance_scale, width, height, num_inference_steps):
    payload = {
        "inputs": prompt,
        "parameters": {
            "negative_prompt": negative_prompt,
            "guidance_scale": guidance_scale,
            "width": width,
            "height": height,
            "num_inference_steps": num_inference_steps,
        },
    }
    image_bytes = query(payload)
    image = Image.open(io.BytesIO(image_bytes))
    return image

# Create Gradio interface
iface = gr.Interface(
    fn=generate_image,
    inputs=[
        gr.Textbox(label="Prompt"),
        gr.Textbox(label="Negative Prompt"),
        gr.Slider(label="Guidance Scale", minimum=1, maximum=20, step=0.1, default=7.5),
        gr.Slider(label="Width", minimum=768, maximum=1024, step=1, default=1024),
        gr.Slider(label="Height", minimum=768, maximum=1024, step=1, default=768),
        gr.Slider(label="Number of Inference Steps", minimum=20, maximum=50, step=1, default=30)
    ],
    outputs=gr.Image(type="pil"),
    title="Stable Diffusion XL Image Generator",
    description="Generate images with Stable Diffusion XL. Provide a prompt, specify any negative prompts, and adjust the image generation parameters.",
)

# Launch the Gradio app, setting share=True for Hugging Face Spaces
iface.launch(share=True)