app.py

import gradio as gr
from gradio_client import Client
import base64
from PIL import Image
import io

# Initialize the client
client = Client("radames/Enhance-This-HiDiffusion-SDXL")

def pil_to_base64(img):
    buffered = io.BytesIO()
    img.save(buffered, format="PNG")
    return base64.b64encode(buffered.getvalue()).decode('utf-8')

def enhance_image(image, prompt):
    # Convert the image to base64
    image_base64 = pil_to_base64(image)
    
    # Use the client to predict the result
    result_base64 = client.predict(image_base64, prompt, api_name="/predict")
    
    # Convert result from base64 to PIL Image
    result_image = Image.open(io.BytesIO(base64.b64decode(result_base64)))
    return result_image

# Create the Gradio interface
iface = gr.Interface(
    fn=enhance_image,
    inputs=[
        gr.inputs.Image(type="pil", label="Input Image"),
        gr.inputs.Textbox(lines=2, placeholder="Enter prompt here", label="Prompt")
    ],
    outputs=gr.outputs.Image(type="pil", label="Enhanced Image"),
    title="Image Enhancement with Text Prompt",
    description="Upload an image and provide a text prompt to enhance the image using HiDiffusion SDXL."
)

# Launch the interface
iface.launch()