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from constants import RESOURCES
from data_preprocessing import RandomizeImageTransform
from utils import beam_search_decode
import streamlit as st
import PIL
import torch
import torchvision.transforms as T
MODEL_PATH = RESOURCES + "/model_2tcuvfsj.pt"
transformer = torch.load(MODEL_PATH)
image_transform = T.Compose((
T.ToTensor(),
RandomizeImageTransform(width=transformer.hparams['image_width'],
height=transformer.hparams['image_height'],
random_magnitude=0)
))
st.title("Image to TeX")
st.image("resources/frontend/fraction_derivative.png", width=500)
st.image("resources/frontend/positional_encoding.png")
st.image("resources/frontend/taylor_sequence_expanded.png")
# st.image("resources/frontend/taylor_sequence.png")
# st.image("resources/frontend/maclaurin_series.png")
# st.image("resources/frontend/gauss_distribution.png")
image_file = st.file_uploader("Upload an image with equation", type=([".png", ".jpg", ".jpeg"]))
if image_file is not None:
image = PIL.Image.open(image_file)
image = image.convert("RGB")
texs = beam_search_decode(transformer, image, image_transform=image_transform)
# streamlit latex doesn't support boldmath
tex = texs[0].replace("\\boldmath", "")
st.latex(tex)
st.markdown(tex)
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