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import gradio as gr
import requests
from PIL import Image
from io import BytesIO
import os
# Load API Token from environment variable
API_TOKEN = os.getenv("HF_API_TOKEN") # Ensure you've set this environment variable
# Hugging Face Inference API URL
API_URL = "https://api-inference.huggingface.co/models/enhanceaiteam/Flux-uncensored"
# Function to call Hugging Face API and get the generated image
def generate_image(prompt, progress=gr.Progress()):
headers = {"Authorization": f"Bearer {API_TOKEN}"}
data = {"inputs": prompt}
# Initialize progress
progress(0, desc="Starting image generation...")
response = requests.post(API_URL, headers=headers, json=data)
if response.status_code == 200:
progress(50, desc="Processing image...")
image_bytes = BytesIO(response.content)
image = Image.open(image_bytes)
progress(100, desc="Completed")
return image
else:
progress(100, desc="Error occurred")
return f"Error: {response.status_code}, {response.text}"
# Create Gradio interface
def create_ui():
with gr.Blocks(theme="hev832/Applio", title="Flux Uncensored") as ui:
gr.Markdown("## Flux Uncensored\nUnofficial Gradio Demo")
prompt_input = gr.Textbox(label="Enter a Prompt", placeholder="Describe the image you want to generate", lines=3)
generate_button = gr.Button("Generate Image")
with gr.Row():
output_image = gr.Image(label="Generated Image")
# Link the button to the function with progress
generate_button.click(fn=generate_image, inputs=prompt_input, outputs=output_image, show_progress=True)
return ui
# Run the interface
if __name__ == "__main__":
create_ui().launch(debug=True)
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