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from __future__ import annotations
import gradio as gr
import logging
logger = logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO,
format='%(asctime)s - %(filename)s[line:%(lineno)d] - %(levelname)s: %(message)s')
import subprocess
def runcmd(command):
ret = subprocess.run(command,shell=True,stdout=subprocess.PIPE,stderr=subprocess.PIPE,encoding="utf-8",timeout=60)
if ret.returncode == 0:
print("success:",ret)
else:
print("error:",ret)
runcmd("pip3 install --upgrade clueai")
import clueai
cl = clueai.Client("", check_api_key=False)
'''
#luck_t2i_btn_1, #luck_s2i_btn_1, #luck_i2i_btn_1, #luck_ici_btn_1{
color: #fff;
--tw-gradient-from: #BED336;
--tw-gradient-stops: var(--tw-gradient-from), var(--tw-gradient-to);
--tw-gradient-to: #BED336;
border-color: #BED336;
}
#luck_easy_btn_1, #luck_iti_btn_1, #luck_tsi_btn_1, #luck_isi_btn_1{
color: #fff;
--tw-gradient-from: #BED336;
--tw-gradient-stops: var(--tw-gradient-from), var(--tw-gradient-to);
--tw-gradient-to: #BED336;
border-color: #BED336;
}
'''
css='''
.container { max-width: 800px; margin: auto; }
#gen_btn_1{
color: #fff;
--tw-gradient-from: #f44336;
--tw-gradient-stops: var(--tw-gradient-from), var(--tw-gradient-to);
--tw-gradient-to: #ff9800;
border-color: #ff9800;
}
#t2i_btn_1, #s2i_btn_1, #i2i_btn_1, #ici_btn_1, #easy_btn_1, #iti_btn_1, #tsi_btn_1, #isi_btn_1{
color: #fff;
--tw-gradient-from: #f44336;
--tw-gradient-stops: var(--tw-gradient-from), var(--tw-gradient-to);
--tw-gradient-to: #ff9800;
border-color: #ff9800;
}
#import_t2i_btn_1, #import_s2i_btn_1, #import_i2i_btn_1, #import_ici_btn_1{
color: #fff;
--tw-gradient-from: #BED336;
--tw-gradient-stops: var(--tw-gradient-from), var(--tw-gradient-to);
--tw-gradient-to: #BED336;
border-color: #BED336;
}
#import_easy_btn_1, #import_iti_btn_1, #import_tsi_btn_1, #import_isi_btn_1{
color: #fff;
--tw-gradient-from: #BED336;
--tw-gradient-stops: var(--tw-gradient-from), var(--tw-gradient-to);
--tw-gradient-to: #BED336;
border-color: #BED336;
}
#record_btn{
}
#record_btn > div > button > span {
width: 2.375rem;
height: 2.375rem;
}
#record_btn > div > button > span > span {
width: 2.375rem;
height: 2.375rem;
}
audio {
margin-bottom: 10px;
}
div#record_btn > .mt-6{
margin-top: 0!important;
}
div#record_btn > .mt-6 button {
font-size: 1em;
width: 100%;
padding: 20px;
height: 60px;
}
div#txt2img_tab {
color: #BED336;
}
'''
task_styles = []
examples_list = []
task_style_to_task_prefix = {}
import csv
def read_examples(input_file):
header = True
with open(input_file) as finput:
csv_input = csv.reader(finput)
for line in csv_input:
if header:
header = False
continue
task_style, task_prefix, example = line
task_styles.append(task_style)
task_style_to_task_prefix[task_style] = task_prefix
examples_list.append([task_style, example])
read_examples("./examples.csv")
#print(task_styles)
def preprocess(text, task):
if task == "问答":
text = text.replace("?", ":").replace("？", ":")
text = text + "："
return task_style_to_task_prefix[task] + "\n" + text + "\n答案："
def inference_gen(text, task):
text = preprocess(text, task)
#print(text)
try:
prediction = cl.generate(
model_name='clueai-base',
prompt=text)
except Exception as e:
logger.error(f"error, e")
return
return prediction.generations[0].text
t2i_default_img_path_list = []
import base64, requests
from io import BytesIO
from PIL import Image
def inference_image(text, n_text, guidance_scale, style, shape, clarity, steps, shape_scale):
try:
res = requests.get(f"https://www.clueai.cn/clueai/hf_text2image?text={text}")
except Exception as e:
logger.error(f"error, {e}")
return
json_dict = res.json()
file_path_list = []
for i, image in enumerate(json_dict["images"]):
image = image.encode('utf-8')
binary_data = base64.b64decode(image)
img_data = BytesIO(binary_data)
img = Image.open(img_data)
file_path_list.append(img)
return file_path_list
image_styles = ['无', '细节大师', '对称美', '虚拟引擎', '空间感', '机械风格', '形状艺术', '治愈', '电影构图', '电影构图（治愈）', '荒芜感', '漫画', '逃离艺术', '斯皮尔伯格', '幻想', '杰作', '壁画', '朦胧', '黑白（3d)', '梵高', '毕加索', '莫奈', '丰子恺', '现代', '欧美']
with gr.Blocks(css=css, title="ClueAI") as demo:
gr.Markdown('<h1><center><font color=red style="font-size:50px;">ClueAI全能师</font></center></h1>')
with gr.TabItem("文本生成", id='_tab'):
with gr.Row(variant="compact").style( equal_height=True):
text = gr.Textbox("俄罗斯天然气管道泄漏爆炸",
label="编辑内容", show_label=False, max_lines=20,
placeholder="在这里输入...",
)
task = gr.Dropdown(label="任务", show_label=True, choices=task_styles, value="标题生成文章")
btn = gr.Button("生成",elem_id="gen_btn_1").style(full_width=False)
with gr.Accordion("高级操作", open=False):
n_text = gr.Textbox("",
label="不想要生成的元素", show_label=True, max_lines=2,
placeholder="在这里输入你不需要包含的内容...",
)
guidance_scale = gr.Slider(1, 20, value=7.5, step=0.5, label="和你的描述匹配程度，越大越匹配")
shape = gr.Radio(["1x1", "16x9", "手机壁纸"], label="尺寸", value="1x1")
shape_scale = gr.Radio([1, 2, 3], label="对图放大倍数", value=1)
steps = gr.Slider(10, 150, value=50, step=1, label="越大质量越好，生成时间越长")
clarity = gr.Radio(["标清", "高清"], label="清晰度", value="标清")
with gr.Row(variant="compact").style( equal_height=True):
output_text = gr.Textbox(
label="输出", show_label=True, max_lines=50,
placeholder="在这里展示结果",
)
gr.Examples(examples_list, [task, text], label="示例")
input_params = [text, task]
#text.submit(inference_gen, inputs=input_params, outputs=output_text)
btn.click(inference_gen, inputs=input_params, outputs=output_text)
with gr.TabItem("图像生成", id='txt2img_tab'):
with gr.Row(variant="compact").style( equal_height=True):
text = gr.Textbox("美丽的风景",
label="编辑内容", show_label=False, max_lines=2,
placeholder="在这里输入你的描述...",
)
btn = gr.Button("生成图像",elem_id="t2i_btn_1").style(full_width=False)
with gr.Row().style( equal_height=True):
generate_prompt_btn = gr.Button("手气不错", elem_id="luck_t2i_btn_1")
style = gr.Dropdown(label="风格", show_label=True, choices=image_styles, value="无")
with gr.Accordion("高级操作", open=False):
n_text = gr.Textbox("",
label="不想要生成的元素", show_label=True, max_lines=2,
placeholder="在这里输入你不需要包含的内容...",
)
guidance_scale = gr.Slider(1, 20, value=7.5, step=0.5, label="和你的描述匹配程度，越大越匹配")
shape = gr.Radio(["1x1", "16x9", "手机壁纸"], label="尺寸", value="1x1")
shape_scale = gr.Radio([1, 2, 3], label="对图放大倍数", value=1)
steps = gr.Slider(10, 150, value=50, step=1, label="越大质量越好，生成时间越长")
clarity = gr.Radio(["标清", "高清"], label="清晰度", value="标清")
gr.Examples(["秋日的晚霞", "星空", "室内装修", "婚礼鲜花"], text, label="示例")
t2i_gallery = gr.Gallery(
t2i_default_img_path_list,
label="生成图像",
show_label=False).style(
grid=[2], height="auto"
)
input_params = [text, n_text, guidance_scale, style, shape, clarity, steps, shape_scale]
generate_prompt_btn.click(inference_image, inputs=input_params, outputs=[t2i_gallery])
text.submit(inference_image, inputs=input_params, outputs=t2i_gallery)
btn.click(inference_image, inputs=input_params, outputs=t2i_gallery)
demo.launch()
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