# https://huggingface.co/yuntian-deng/latex2im_ss_finetunegptneo
import gradio as gr
import numpy as np
import requests
import base64
import os
API_ENDPOINT = os.getenv('API_ENDPOINT')
API_KEY = os.getenv('API_KEY')
title = "
Markup-to-Image Diffusion Models with Scheduled Sampling
"
authors = "Yuntian Deng, Noriyuki Kojima, Alexander M. Rush"
info = 'Paper Code'
with gr.Blocks() as demo:
gr.Markdown(title)
gr.Markdown(authors)
gr.Markdown(info)
with gr.Row():
with gr.Column(scale=2):
textbox = gr.Textbox(label=r'Type LaTeX formula below and click "Generate"', lines=1, max_lines=1, placeholder='Type LaTeX formula here and click "Generate"', value=r'\sum_{t=1}^T\E_{y_t \sim {\tilde P(y_t| y_0)}} \left\| \frac{y_t - \sqrt{\bar{\alpha}_t}y_0}{\sqrt{1-\bar{\alpha}_t}} - \epsilon_\theta(y_t, t)\right\|^2.')
submit_btn = gr.Button("Generate", elem_id="btn")
with gr.Column(scale=3):
slider = gr.Slider(0, 1000, value=0, label='step (out of 1000)')
image = gr.Image(label="Rendered Image", show_label=False, elem_id="image")
inputs = [textbox]
outputs = [slider, image, submit_btn]
def infer(formula):
data = {'formula': formula, 'api_key': API_KEY}
try:
with requests.post(url=API_ENDPOINT, data=data, timeout=600, stream=True) as r:
i = 0
for line in r.iter_lines():
response = line.decode('ascii').strip()
r = base64.decodebytes(response.encode('ascii'))
q = np.frombuffer(r, dtype=np.float32).reshape((64, 320, 3))
i += 1
yield i, q, submit_btn.update(visible=False)
yield i, q, submit_btn.update(visible=True)
except Exception as e:
yield 1000, 255*np.ones((64, 320, 3)), submit_btn.update(visible=True)
submit_btn.click(fn=infer, inputs=inputs, outputs=outputs)
demo.queue(concurrency_count=20, max_size=200).launch(enable_queue=True)