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import gradio as gr
import os
from PIL import Image
root = "/mnt/ve_share/generation/data/result/diffusions/vis/instructpix2pix/official"
log_path = "/mnt/ve_share/generation/recordx.txt"
def feedback(feedback_text, model, scene):
output = "%s@%s@%s@%s" % (model, scene, feedback_text, length)
with open(log_path, "a") as input_file:
input_file.writelines(output + "\n")
print(output)
def get_file_paths(folder_path):
file_paths = []
for root, dirs, files in os.walk(folder_path):
for file in files:
file_path = os.path.join(root, file)
file_paths.append(file_path)
return file_paths
def generation_eval():
# print("request.headers: ", request.headers)
models = [f for f in os.listdir(root) if os.path.isdir(os.path.join(root, f))]
model_root = "%s/%s" % (root, models[-2])
scenes = [f for f in os.listdir(model_root) if os.path.isdir(os.path.join(model_root, f))]
def clip_api(model, scene):
# global feedback_btn
# feedback_btn = gr.Radio.update(choices=['未知(反馈可持续优化LSU🤗)', '准确率低(<30%)', '准确率中(30% ~ 80%)', '准确率高(>80%)',], value='未知(反馈可持续优化LSU🤗)')
ret_imgs = get_file_paths("%s/%s/%s" % (root, model, scene))
# print(ret_imgs)
global length
length = len(ret_imgs)
ret_imgs = [Image.open(_) for _ in ret_imgs]
return ret_imgs
examples = []
# examples = [
# ["道路上工作的环卫工人", 20, VLP, "是"],
# ["雨天打伞的人", 20, VLP, "是"],
# ["救护车", 20, VLP, "是"],
# ["校车", 20, VLP, "是"],
# ["警车", 20, VLP, "是"],
# ["喝水的人", 20, VLP, "是"],
# ["后备箱打开", 20, VLP, "是"],
# ]
title = "<h1 align='center'>图像生成人工评测平台</h1>"
with gr.Blocks() as demo:
gr.Markdown(title)
# gr.Markdown(description)
with gr.Row():
with gr.Column(scale=1):
# with gr.Column(scale=2):
# text = gr.Textbox(value="道路上工作的环卫工人", label="请填写文本", elem_id=0, interactive=True)
# num = gr.components.Slider(minimum=0, maximum=200, step=1, value=30, label="返回图片数", elem_id=2)
model = gr.components.Radio(label="模型选择", choices=models, value=models[0], elem_id=3)
scene = gr.components.Radio(label="场景选择", choices=scenes, value=scenes[0], elem_id=4)
# thumbnail = gr.components.Radio(label="是否返回缩略图", choices=["yes", "no"], value="yes", elem_id=4)
btn = gr.Button("搜索")
# feedback_btn = gr.Radio(label="检索结果反馈",
# choices=['未知(反馈可持续优化LSU🤗)', '准确率低(<30%)', '准确率中(30% ~ 80%)', '准确率高(>80%)',], value='未知(反馈可持续优化LSU🤗)')
with gr.Column(scale=100):
out = gr.Gallery(label="检索结果为：").style(grid=6, height=200)
feedback_btn = gr.Textbox(value="", label="请填写合格图像数", elem_id=0, interactive=True)
btn2 = gr.Button("提交")
# inputs = [text, num, model, thumbnail]
inputs = [model, scene]
btn.click(fn=clip_api, inputs=inputs, outputs=[out])
btn2.click(feedback, inputs=[feedback_btn, model, scene])
# feedback_btn.change(feedback, inputs=feedback_btn)
gr.Examples(examples, inputs=inputs)
return demo
if __name__ == "__main__":
with gr.TabbedInterface(
[generation_eval()],
["图像生成人工评测"],
) as demo:
demo.launch(
enable_queue=True,
share=True,
)
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