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7975c90 98c74d2 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 | import gradio as gr
from gradio_client import Client, handle_file
import re
# Instantiate a Client object from gradio_client pointing to the 'selfit-camera/Omni-Image-Editor' Space.
client = Client("selfit-camera/Omni-Image-Editor")
# Define a Python function for text-to-image generation
def generate_image(prompt):
"""
Generate an image from a text prompt using the Omni Image Editor API.
Args:
prompt (str): Text description of the image to generate
Returns:
str: URL of the generated image or error message
"""
try:
# Call the client.predict() method with the user's prompt, aspect_ratio='16:9', and api_name='/text_to_image_interface'.
result = client.predict(
prompt=prompt,
aspect_ratio="16:9",
api_name="/text_to_image_interface"
)
# The predict method returns a tuple. The first element of this tuple is an HTML string containing the image.
# Extract the image URL from this HTML string.
html_string = result[0]
match = re.search(r"src='([^']+)'", html_string)
if match:
image_url = match.group(1)
return image_url
else:
# Handle cases where the URL might not be found
return "https://via.placeholder.com/400x200?text=Error:Image+Not+Found"
except Exception as e:
return f"Error generating image: {str(e)}"
# Define a Python function for image editing
def edit_image(input_image, edit_prompt):
"""
Edit an image based on a text prompt using the Omni Image Editor API.
Args:
input_image (str): Path to the image file or image object
edit_prompt (str): Text description of the edits to apply
Returns:
str: URL of the edited image or error message
"""
try:
if input_image is None:
return "Please upload an image first"
# Use handle_file to properly handle the image upload
result = client.predict(
input_image=handle_file(input_image),
prompt=edit_prompt,
api_name="/edit_image_interface"
)
# Extract the image URL from the HTML response
if isinstance(result, tuple) and len(result) > 0:
html_string = result[0]
match = re.search(r"src='([^']+)'", html_string)
if match:
image_url = match.group(1)
return image_url
else:
return "https://via.placeholder.com/400x200?text=Error:Image+Not+Found"
else:
return str(result)
except Exception as e:
return f"Error editing image: {str(e)}"
# Define a Python function for image upscaling
def upscale_image(input_image):
"""
Upscale an image to higher resolution using the Omni Image Editor API.
Args:
input_image (str): Path to the image file or image object to upscale
Returns:
str: URL of the upscaled image or error message
"""
try:
if input_image is None:
return "Please upload an image first"
# Use handle_file to properly handle the image upload
result = client.predict(
input_image=handle_file(input_image),
api_name="/image_upscale_interface"
)
# Extract the image URL from the HTML response
if isinstance(result, tuple) and len(result) > 0:
html_string = result[0]
match = re.search(r"src='([^']+)'", html_string)
if match:
image_url = match.group(1)
return image_url
else:
return "https://via.placeholder.com/400x200?text=Error:Image+Not+Found"
else:
return str(result)
except Exception as e:
return f"Error upscaling image: {str(e)}"
# Create a Gradio application using gr.Blocks for more granular control.
with gr.Blocks(
title='Omni Image Editor with Gradio',
theme=gr.themes.Soft()
) as demo:
gr.Markdown("# Omni Image Editor Studio")
gr.Markdown("Generate images from text descriptions or edit existing images with AI-powered tools.")
with gr.Tabs():
# Text-to-Image Tab
with gr.TabItem("Text to Image Generator"):
gr.Markdown("### Generate Images from Text")
gr.Markdown("Describe the image you want to generate in detail for best results.")
with gr.Row():
with gr.Column():
prompt_input = gr.Textbox(
label='Image Description',
placeholder='e.g., A futuristic city at sunset with flying cars, neon lights, cyberpunk style, high quality',
lines=3
)
generate_btn = gr.Button("🎨 Generate Image", variant="primary")
generated_image = gr.Image(label='Generated Image', type='filepath')
# Bind the generate_image function to the button click event
generate_btn.click(
fn=generate_image,
inputs=[prompt_input],
outputs=[generated_image]
)
# Image Editing Tab
with gr.TabItem("Image Editor"):
gr.Markdown("### Edit Images with AI")
gr.Markdown("Upload an image and describe the changes you want to make.")
with gr.Row():
with gr.Column():
input_image = gr.Image(
label='Upload Image',
type='filepath'
)
with gr.Row():
with gr.Column():
edit_prompt = gr.Textbox(
label='Edit Instructions',
placeholder='e.g., Change the sky to sunset colors, add stars, increase contrast',
lines=3
)
edit_btn = gr.Button("✨ Edit Image", variant="primary")
edited_image = gr.Image(label='Edited Image', type='filepath')
# Bind the edit_image function to the button click event
edit_btn.click(
fn=edit_image,
inputs=[input_image, edit_prompt],
outputs=[edited_image]
)
# Image Upscaling Tab
with gr.TabItem("Image Upscaler"):
gr.Markdown("### Upscale Images to Higher Resolution")
gr.Markdown("Upload an image and enhance it to higher resolution using AI-powered upscaling.")
with gr.Row():
with gr.Column():
upscale_input = gr.Image(
label='Upload Image to Upscale',
type='filepath'
)
upscale_btn = gr.Button("⬆️ Upscale Image", variant="primary")
upscaled_image = gr.Image(label='Upscaled Image', type='filepath')
# Bind the upscale_image function to the button click event
upscale_btn.click(
fn=upscale_image,
inputs=[upscale_input],
outputs=[upscaled_image]
)
# Launch the Gradio application.
if __name__ == "__main__":
demo.launch(Share=True)
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