app.py

import gradio as gr
import requests
import io
from PIL import Image
import os

# Access the token from the environment secrets
HF_API_TOKEN = os.getenv("HF_API_TOKEN")
API_URL = "https://api-inference.huggingface.co/models/black-forest-labs/FLUX.1-dev"
headers = {"Authorization": f"Bearer {HF_API_TOKEN}"}  # No need to hard-code the token

def query(payload):
    # Make the API request using the token from environment secrets
    response = requests.post(API_URL, headers=headers, json=payload)
    
    # Check if the response is successful
    if response.status_code != 200:
        raise Exception(f"Failed to generate image: {response.status_code}, {response.text}")
    
    return response.content

def generate_image(prompt):
    # Query the model with the user-provided prompt
    image_bytes = query({"inputs": prompt})
    
    # Open the image from the bytes received
    image = Image.open(io.BytesIO(image_bytes))
    
    return image

# Create a Gradio interface
interface = gr.Interface(
    fn=generate_image,                 # Function to call
    inputs="text",                     # Input will be a text box
    outputs="image",                   # Output will be an image
    title="Hugging Face Image Generator",  # Title for the Gradio space
    description="Enter a prompt to generate an image using the Hugging Face model."
)

# Launch the app
interface.launch()