viscpm-paint / app.py
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#!/usr/bin/env python
# encoding: utf-8
import gradio as gr
from datasets import load_dataset
from PIL import Image
import re
import os
import requests
import threading
import base64
import io
sem = threading.Semaphore()
def infer(prompt, negative, scale):
try:
url = os.environ.get('SERVICE_URL', 'https://modelbest.cn/')
sem.acquire()
resp = requests.post(url, headers={
"X-Model-Best-Model": "viscpm-paint-balance",
"X-Model-Best-Trace-ID": "test-trace",
}, json={
"question": prompt,
"negative_prompt": negative,
"num_images_per_prompt": 4,
})
sem.release()
resp = resp.json()
images = resp['data']['response']
images = [Image.open(io.BytesIO(base64.b64decode(encoded_image))).convert("RGB") for encoded_image in images]
return images
except Exception as e:
print(e)
return []
with gr.Blocks() as demo:
gr.Markdown('<div align="center"><big><b>百亿参数量中英双语多模态大模型VisCPM</b></big></div>')
with gr.Column(variant="panel"):
with gr.Row(variant="compact"):
with gr.Column(variant="compact"):
text = gr.Textbox(
label="Enter your prompt",
show_label=False,
max_lines=1,
placeholder="输入提示词",
).style(
container=False,
)
neg_text = gr.Textbox(
label="Enter your negative prompt",
show_label=False,
max_lines=1,
placeholder="输入负向提示词(你不想生成的内容)",
).style(
container=False,
)
btn = gr.Button("生成图像").style(full_width=False)
gallery = gr.Gallery(
label="Generated images", show_label=False, elem_id="gallery", height="1024"
).style(columns=[2], rows=[2], object_fit="contain")
btn.click(infer, [text, neg_text], gallery)
if __name__ == "__main__":
demo.launch()