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)
    if response.status_code != 200:
        return "Error: Unexpected status code {} with message {}".format(response.status_code, response.text)

    result = response.json()
    
    # Debug output to help figure out what result looks like
    print("DEBUG:", result)

    # 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.
    if 'data' in result:
        base64_image = result['data'][0]  # This might need to be adjusted based on API return structure
        base64_data = base64_image.split(",")[1] if ',' in base64_image else base64_image
        image_bytes = base64.b64decode(base64_data)
        image = Image.open(BytesIO(image_bytes))
        return image
    else:
        return "Error: 'data' not found in response."

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()