Spaces:
Sleeping
Sleeping
File size: 2,570 Bytes
222d7ce 23d59a5 165dc5d 70ab081 e16e2d6 23d59a5 566b9eb 83a1864 e7e8aa5 222d7ce c50d086 81351b9 c50d086 81351b9 c50d086 e75ddc5 c50d086 d1adaa8 23d59a5 e7e8aa5 1d193c7 566b9eb 41f1c59 2ba6b16 1d193c7 566b9eb e7e8aa5 deba054 d7d4715 f552bec d7d4715 41f1c59 c50d086 81351b9 e7e8aa5 222d7ce |
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 |
import gradio as gr
import os
import requests
import json
import base64
from io import BytesIO
from PIL import Image
from huggingface_hub import login
from css_html_js import custom_css
from about import (
CITATION_BUTTON_LABEL,
CITATION_BUTTON_TEXT,
EVALUATION_QUEUE_TEXT,
INTRODUCTION_TEXT,
LLM_BENCHMARKS_TEXT,
TITLE,
)
myip = "34.219.98.113"
myport=8080
is_spaces = True if "SPACE_ID" in os.environ else False
is_shared_ui = False
def process_image_from_binary(img_stream):
if img_stream is None:
print("no image binary")
return
image_data = base64.b64decode(img_stream)
image_bytes = BytesIO(image_data)
img = Image.open(image_bytes)
return img
def generate_img(concept, prompt, seed, steps):
print(f"my IP is {myip}, my port is {myport}")
response = requests.post('http://{}:{}/generate'.format(myip, myport),
json={"concept": concept, "prompt": prompt, "seed": seed, "steps": steps},
timeout=(10, 1200))
print(f"result: {response}")
image = None
if response.status_code == 200:
response_json = response.json()
print(response_json)
image = process_image_from_binary(response_json['image'])
else:
print(f"Request failed with status code {response.status_code}")
return image
with gr.Blocks() as demo:
gr.HTML(TITLE)
gr.Markdown(INTRODUCTION_TEXT, elem_classes="markdown-text")
with gr.Row() as advlearn:
with gr.Column():
# gr.Markdown("Please upload your model id.")
drop_text = gr.Dropdown(["Object-Church", "Object-Parachute", "Object-Garbage_Truck",
"Style-VanGogh","Concept-Nudity", "None"],
label="AdvUnlearn Text Encoder")
with gr.Column():
text_input = gr.Textbox(label="Prompt")
with gr.Row():
with gr.Column():
with gr.Row():
seed = gr.Textbox(label="seed", value=666)
with gr.Row():
steps = gr.Textbox(label="num_steps", value=100)
with gr.Row():
start_button = gr.Button("AdvUnlearn",size='lg')
with gr.Column(min_width=512):
result_img = gr.Image(label="Image Gnerated by AdvUnlearn",width=512,show_share_button=False,show_download_button=False)
start_button.click(fn=generate_img, inputs=[drop_text, text_input, seed, steps], outputs=result_img, api_name="generate")
demo.launch() |