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import gradio as gr
import torch
from diffusers import StableDiffusionInpaintPipeline
# pipe = StableDiffusionInpaintPipeline.from_pretrained(
# "stabilityai/stable-diffusion-2-inpainting",
# torch_dtype=torch.float16,
# )
from PIL import Image
def process_image(image: Image.Image, prompt: str, slider_value: int) -> Image.Image:
# Placeholder function for processing
# Replace this with your actual processing logic
# For example, modifying the image based on the slider value and prompt
processed_image = image.copy() # Just returning a copy for now
return processed_image
with gr.Blocks() as demo:
# Title at the top center
gr.Markdown("<h1 style='text-align: center;'>Image Inprinting</h1>")
with gr.Row():
with gr.Column(scale=1):
# Image upload on the left
image_input = gr.Image(type='pil', label='Upload Image')
# Slider below the image upload
slider = gr.Slider(minimum=1, maximum=4, step=1, value=1, label='Select Zoom')
# Textbox for prompt
prompt_input = gr.Textbox(label='Enter Prompt')
# Submit button
submit_btn = gr.Button("Submit")
with gr.Column(scale=1):
# Output image on the right
image_output = gr.Image(label='Output Image')
# Event handler to process the image when the button is clicked
submit_btn.click(fn=process_image, inputs=[image_input, prompt_input, slider], outputs=image_output)
# Launch the Gradio app
demo.launch(debug=True)
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