app.py

import gradio as gr
import requests
from PIL import Image
import base64
from io import BytesIO

def query_hf_image_generation(api_key, prompt):
    API_URL = f"https://api-inference.huggingface.co/models/stabilityai/stable-diffusion-xl-base-1.0"
    headers = {
        "Authorization": f"Bearer {api_key}",
        "Content-Type": "application/json"
    }
    data = {
        "inputs": prompt
    }

    response = requests.post(API_URL, headers=headers, json=data)
    result = response.json()
    
    # Check if the API response contains an error.
    if 'error' in result:
        return "Error: " + result['error'], None

    # Assuming the API returns an image in base64 format.
    image_data = result['data'][0]  # Adjust this according to your specific API response structure
    image_bytes = base64.b64decode(image_data.split(",")[1])
    image = Image.open(BytesIO(image_bytes))
    return image

iface = gr.Interface(
    fn=query_hf_image_generation,
    inputs=[
        gr.Textbox(label="Hugging Face API Key", placeholder="Enter your Hugging Face API Key here..."),
        gr.Textbox(lines=2, placeholder="Enter your prompt here...", label="Prompt")
    ],
    outputs=gr.Image(label="Generated Image"),
    title="Stable Diffusion XL Image Generator",
    description="Enter your API Key and a prompt to generate an image using the Stable Diffusion XL model from Hugging Face."
)

iface.launch()